from google.colab import drive
drive.mount("gdrive")

Mounted at gdrive

import pandas as pd 
import matplotlib.pyplot as plt
import numpy as np

import networkx as nx

import requests
from bs4 import BeautifulSoup  
import json

import gc

Token Transfers Dataset

In this dataset, we have token transfers done between SALE contract and users for a certain time range.

df = pd.read_csv("/content/gdrive/MyDrive/project-kittybang/df.csv",index_col=['Unnamed: 0'])
df.head(-1) # lets look 5 from top and 5 from bottom

/usr/local/lib/python3.7/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (4) have mixed types.Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)
/usr/local/lib/python3.7/dist-packages/numpy/lib/arraysetops.py:580: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison
  mask |= (ar1 == a)

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	token_address	from_address	to_address	value	transaction_hash	log_index	block_timestamp	block_number	block_hash
0	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1001	0x14952600083dbec76c08fb7b4473704642aa3ff626b53f83cd261259eb2c4a82	49	2017-11-23 18:44:21+00:00	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a02c0ffdd4267a67ee050d
1	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1004	0x06ee06ee12893fbd6fc853f192a068322fe57152fbb269a1ee90b4a2582de031	71	2017-11-23 18:44:21+00:00	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a02c0ffdd4267a67ee050d
2	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1008	0xf9f572e702ede9adeb3dcb73c817a7837abe7cbbddc4150f1272e6c8e176cf60	79	2017-11-23 18:44:21+00:00	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a02c0ffdd4267a67ee050d
3	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1006	0xa71a76052a2ac8e231967d5714ddc977215729d13cdc9088c7e9133dbbebf893	75	2017-11-23 18:44:21+00:00	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a02c0ffdd4267a67ee050d
4	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1010	0x09c5fdb73577367cd5e2079f4b2c65d744933bb4f819b326ff93d1c0f30c903c	83	2017-11-23 18:44:21+00:00	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a02c0ffdd4267a67ee050d
...	...	...	...	...	...	...	...	...	...
2143115	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x92e75270032c1ef9ba6842e8978a9c99adc2a365	1275954	0x0001084c0088dff4e1368340b0e52b2ca88a5c3bb79091a0dc96d60942859798	134	2021-05-06 22:37:27+00:00	12383505	0x941ebd4400c159d273bead4c57a2f0b72b43cebe6eefd90f8318099e83d41471
2143116	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x6b8d51a3adbc5ff208e64d4bb6ceb324149a1003	1892664	0x8bbefdef5c62bc4e1a9e109c095f792507da50aef395cf9d03ead8acfb340973	275	2021-05-06 22:52:11+00:00	12383568	0x300262d2b879ed1129a7c990076d5c134181b243280399b9529efd24a2bb9f3e
2143117	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xe784a7e2189cede2556cc103481c121102e968f4	375163	0x2cc016025cd7bfa1b0d72d0c2cc3fbb325179845238c22fd692a607988f18ec0	336	2021-05-06 23:07:51+00:00	12383632	0xee2eefd188a5776574b8c472deef883757ea9868fd309bddb817d2a160108e71
2143118	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x3cdf4b1b07775b8bd249009bbf211a4c87adb566	1090976	0x6919f2eb1ad7fede17f0f7871dca4232c6545ea39d6681af97a7dd85184d1ee3	311	2021-05-06 23:32:45+00:00	12383739	0x6a0dd54f91b2a6394c078ae232b7d6d3a426cf177d85cde37074e1b7edfcd3ba
2143119	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xb99d3369d98ab9c00fb6a48735763a64a1a13599	1110582	0x4fdc5a80a9a779e2fa464e96ed068905c76039878c62e96ea96cb50b6e059000	200	2021-05-06 23:56:28+00:00	12383829	0x89171b6e1fce0114150474fdfad1aca85050fed38c590bb3977689698634dab4

2143120 rows × 9 columns

print(f"Total # of transfers done by SALE contract is {len(df)}")

Total # of transfers done by SALE contract is 2143121

## Lets get a simple transaction hash to examine
for row in df.iterrows():
  print(row[1]["transaction_hash"], row[1]["value"])
  break

0x14952600083dbec76c08fb7b4473704642aa3ff626b53f83cd261259eb2c4a82 1001

This was an auction starting transaction where user basically puts the kitty to an auction. In order to check for a sale success transaction, lets check transactions where from_address equals to SALE contract address.

pd.set_option('display.max_colwidth', None)
tx = df.iloc[[2143115]]["transaction_hash"]
tx

2143115    0x0001084c0088dff4e1368340b0e52b2ca88a5c3bb79091a0dc96d60942859798
Name: transaction_hash, dtype: object

After a brief examination, we have observed that "value" column is actually id of the CryptoKitty.

Example: https://www.cryptokitties.co/kitty/1275954

## Cattributes Extracted

len(df.value.unique())

This is a very big data hence it would be very hard to maintain every one of them from the server but we can still give it a try

Parsing can be done locally since it crashes the current runtime.

import json
errConsecutive = 0
with open("cats.json","a") as f:
  for id in df.value.unique()[0:10]:
    r = requests.get(f"https://api.cryptokitties.co/v3/kitties/{id}")
    if(r.status_code==200):
      print(r.text)

{"id":1001,"name":"TheFirst","generation":0,"created_at":"2017-11-23T09:18:57.000Z","birthday":"2017-11-23T00:00:00.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/1001.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/1001.svg","color":"mintgreen","bio":"Hey cutie! I'm TheFirst. In high school, I was voted most likely to work at NASA. When my owner isn't watching, I steal their oversized sweaters and use them for litter paper. I'm not sorry. I think you'll love me beclaws I have cattitude.","kitty_type":null,"is_fancy":false,"is_exclusive":false,"is_special_edition":false,"fancy_type":null,"language":"en","is_prestige":false,"prestige_type":null,"prestige_ranking":null,"prestige_time_limit":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1519819626755,"dynamic_cooldown":1521108340267,"cooldown_index":9,"cooldown_end_block":4650045,"pending_tx_type":null,"pending_tx_since":null},"purrs":{"count":8,"is_purred":false},"watchlist":{"count":0,"is_watchlisted":false},"hatcher":{"address":"0xba52c75764d6f594735dc735be7f1830cdf58ddf","image":"2","nickname":"COO","hasDapper":false,"twitter_id":null,"twitter_image_url":null,"twitter_handle":null},"auction":{},"offer":{},"owner":{"address":"0x672ec49f7f7eac25c3a2e651f67f579bb5da8936","hasDapper":false,"twitter_id":null,"twitter_image_url":null,"twitter_handle":null,"image":"7","nickname":"Kickycat"},"matron":{},"sire":{},"children":[{"id":3043,"name":"Yasla Love Child | Gen1","generation":1,"enhanced_cattributes":[{"type":"body","kittyId":3043,"position":-1,"description":"ragamuffin"},{"type":"coloreyes","kittyId":3043,"position":-1,"description":"mintgreen"},{"type":"eyes","kittyId":3043,"position":-1,"description":"thicccbrowz"},{"type":"pattern","kittyId":3043,"position":-1,"description":"luckystripe"},{"type":"mouth","kittyId":3043,"position":-1,"description":"happygokitty"},{"type":"colorprimary","kittyId":3043,"position":-1,"description":"orangesoda"},{"type":"colorsecondary","kittyId":3043,"position":-1,"description":"chocolate"},{"type":"colortertiary","kittyId":3043,"position":-1,"description":"kittencream"}],"owner_wallet_address":"0xc7af99fe5513eb6710e6d5f44f9989da40f27f26","owner":{"address":"0xc7af99fe5513eb6710e6d5f44f9989da40f27f26"},"created_at":"2017-11-23T20:15:04.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3043.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3043.svg","color":"mintgreen","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1520423687635},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3043.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/3043.items.svg"},{"id":3153,"name":"Silver (Gen 1)","generation":1,"enhanced_cattributes":[{"type":"body","kittyId":3153,"position":-1,"description":"sphynx"},{"type":"coloreyes","kittyId":3153,"position":-1,"description":"mintgreen"},{"type":"eyes","kittyId":3153,"position":-1,"description":"crazy"},{"type":"pattern","kittyId":3153,"position":-1,"description":"luckystripe"},{"type":"mouth","kittyId":3153,"position":-1,"description":"happygokitty"},{"type":"colorprimary","kittyId":3153,"position":-1,"description":"shadowgrey"},{"type":"colorsecondary","kittyId":3153,"position":-1,"description":"chocolate"},{"type":"colortertiary","kittyId":3153,"position":-1,"description":"granitegrey"}],"owner_wallet_address":"0x6b1a6500f1c358468a72f9b726b0818aa212999d","owner":{"address":"0x6b1a6500f1c358468a72f9b726b0818aa212999d"},"created_at":"2017-11-23T22:35:11.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3153.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3153.svg","color":"mintgreen","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1520591397023},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3153.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/3153.items.svg"},{"id":3239,"name":"Original","generation":6,"enhanced_cattributes":[{"type":"body","kittyId":3239,"position":-1,"description":"munchkin"},{"type":"coloreyes","kittyId":3239,"position":-1,"description":"topaz"},{"type":"eyes","kittyId":3239,"position":-1,"description":"simple"},{"type":"pattern","kittyId":3239,"position":-1,"description":"totesbasic"},{"type":"mouth","kittyId":3239,"position":-1,"description":"happygokitty"},{"type":"colorprimary","kittyId":3239,"position":-1,"description":"orangesoda"},{"type":"colorsecondary","kittyId":3239,"position":-1,"description":"swampgreen"},{"type":"colortertiary","kittyId":3239,"position":-1,"description":"granitegrey"}],"owner_wallet_address":"0x9c4b1564355a702b17f6c9e2f829c19147fb76a2","owner":{"address":"0x9c4b1564355a702b17f6c9e2f829c19147fb76a2"},"created_at":"2017-11-23T23:11:30.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3239.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3239.svg","color":"topaz","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1520097880150},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3239.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/3239.items.svg"},{"id":3567,"name":"Son of The First","generation":6,"enhanced_cattributes":[{"type":"body","kittyId":3567,"position":-1,"description":"himalayan"},{"type":"coloreyes","kittyId":3567,"position":-1,"description":"sizzurp"},{"type":"eyes","kittyId":3567,"position":-1,"description":"simple"},{"type":"pattern","kittyId":3567,"position":-1,"description":"luckystripe"},{"type":"mouth","kittyId":3567,"position":-1,"description":"soserious"},{"type":"colorprimary","kittyId":3298,"position":7,"description":"cloudwhite"},{"type":"colorsecondary","kittyId":3567,"position":-1,"description":"royalpurple"},{"type":"colortertiary","kittyId":3567,"position":-1,"description":"granitegrey"}],"owner_wallet_address":"0xb1690c08e213a35ed9bab7b318de14420fb57d8c","owner":{"address":"0xb1690c08e213a35ed9bab7b318de14420fb57d8c"},"created_at":"2017-11-24T01:22:16.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3567.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3567.svg","color":"sizzurp","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1519797365355},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/3567.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/3567.items.svg"},{"id":5438,"name":"Purrpull","generation":9,"enhanced_cattributes":[{"type":"body","kittyId":5438,"position":-1,"description":"himalayan"},{"type":"coloreyes","kittyId":5438,"position":-1,"description":"chestnut"},{"type":"eyes","kittyId":5438,"position":-1,"description":"simple"},{"type":"pattern","kittyId":5438,"position":-1,"description":"totesbasic"},{"type":"mouth","kittyId":5438,"position":-1,"description":"happygokitty"},{"type":"colorprimary","kittyId":5438,"position":-1,"description":"greymatter"},{"type":"colorsecondary","kittyId":5438,"position":-1,"description":"swampgreen"},{"type":"colortertiary","kittyId":5438,"position":-1,"description":"granitegrey"}],"owner_wallet_address":"0x9b1ba150e7ef6b7195b1c1c7eb109fd905e6deed","owner":{"address":"0x9b1ba150e7ef6b7195b1c1c7eb109fd905e6deed"},"created_at":"2017-11-24T20:28:58.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/5438.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/5438.svg","color":"chestnut","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1459187940458},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/5438.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/5438.items.svg"},{"id":10219,"name":"Tintin G14V","generation":14,"enhanced_cattributes":[{"type":"body","kittyId":3006,"position":1,"description":"laperm"},{"type":"coloreyes","kittyId":10219,"position":-1,"description":"mintgreen"},{"type":"eyes","kittyId":10219,"position":-1,"description":"simple"},{"type":"pattern","kittyId":10219,"position":-1,"description":"luckystripe"},{"type":"mouth","kittyId":3020,"position":1,"description":"tongue"},{"type":"colorprimary","kittyId":10219,"position":-1,"description":"shadowgrey"},{"type":"colorsecondary","kittyId":10219,"position":-1,"description":"coffee"},{"type":"colortertiary","kittyId":10219,"position":-1,"description":"kittencream"}],"owner_wallet_address":"0xccb77cda5c2351b41ea74bb2a5d9ef107308f5b1","owner":{"address":"0xccb77cda5c2351b41ea74bb2a5d9ef107308f5b1"},"created_at":"2017-11-28T20:45:07.000Z","image_url":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/10219.svg","image_url_cdn":"https://img.cn.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/10219.svg","color":"mintgreen","is_fancy":false,"is_exclusive":false,"is_special_edition":false,"kitty_type":null,"fancy_type":null,"status":{"is_ready":true,"is_gestating":false,"cooldown":1458668582277},"hatched":true,"wrapped":false,"image_url_png":"https://img.cryptokitties.co/0x06012c8cf97bead5deae237070f9587f8e7a266d/10219.png","image_url_kitty_items":"https://img.cryptokitties.cohttps://storage.googleapis.com/ck-kitty-image/0x06012c8cf97bead5deae237070f9587f8e7a266d/10219.items.svg"},{"id":10423,"name":"Gen 2 Lemonade 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This part should be done faster.

In order to inspect whether the network shows tendency to be a BB model we must first model it in a temporal manner and inspect whether we can simulate transactions. First we will query dataframe by a date range and then sort the obtained data. Followingly, we will check if we can observe referential attachment. Referential attachment corresponds to a fact that nodes with more links tends to have more links. And there is also a term called fitness where there is a parameter which allows users to get more connections. In order analysis, we are defending that users with more money tends to make more transactions (buy and sell) which also makes them to be more fitted to obtain more links in the network.

Filter by time

df.dtypes

token_address       object
from_address        object
to_address          object
value               object
transaction_hash    object
log_index            int64
block_timestamp     object
block_number         int64
block_hash          object
dtype: object

df['block_timestamp'] = pd.to_datetime(df['block_timestamp'], format='%Y-%m-%d %H:%M:%S').dt.tz_localize(None)
df['date'] = pd.to_datetime(df['block_timestamp'], format='%Y-%m-%d %H:%M:%S').dt.date
df['time'] = pd.to_datetime(df['block_timestamp'], format='%Y-%m-%d %H:%M:%S').dt.time

##Sort by datetime

df = df.sort_values(by="block_timestamp",ascending=True)

df.head()

.dataframe tbody tr th {
    vertical-align: top;
}

.dataframe thead th {
    text-align: right;
}

</style>

	token_address	from_address	to_address	value	transaction_hash	log_index	block_timestamp	block_number	block_hash	date	time
0	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1001	0x14952600083dbec76c08fb7b4473704642aa3ff626b5...	49	2017-11-23 18:44:21	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a0...	2017-11-23	18:44:21
9	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1007	0xe0fa7d9556342571697f82d676f82fcec694939f4511...	77	2017-11-23 18:44:21	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a0...	2017-11-23	18:44:21
7	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1003	0xab3204ad1b1293908b62b4052a2380da80035e64ffab...	69	2017-11-23 18:44:21	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a0...	2017-11-23	18:44:21
6	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1009	0xc3cf0ccf1716a38fbf155d5b27d9f18431fa802e6fc9...	81	2017-11-23 18:44:21	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a0...	2017-11-23	18:44:21
5	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xba52c75764d6f594735dc735be7f1830cdf58ddf	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	1002	0xb66af526b991e930f7fd9c0adab43e75deda32f604b8...	65	2017-11-23 18:44:21	4608519	0x7aa12edf18340af4055a9ed77c230b00579b811387a0...	2017-11-23	18:44:21

###Bin by day '2017-11-23' to '2018-02-23'

import datetime
import pytz 
temp = datetime.datetime(2017,11,23)
temporal_networks = []
while(temp<=datetime.datetime(2018,2,23)):
  #get respective information regarding on date
  mask = pd.Timestamp(temp.year, temp.month, temp.day)
  temp_df = df[df["date"]==mask]
  #sort by datetime
  sorted_df = temp_df.sort_values(by="block_timestamp",ascending=True)
  temp_network = nx.MultiDiGraph()
  for index,instance in enumerate(sorted_df.iterrows()):
    temp_network.add_edge(instance[1]["from_address"],instance[1]["to_address"],token=instance[1]["value"])
  temporal_networks.append(temp_network)
  temp += datetime.timedelta(days=1)
  print(temp, f"Total number of transactions done via:{len(temp_network.edges)}")

2017-11-24 00:00:00 Total number of transactions done via:973
2017-11-25 00:00:00 Total number of transactions done via:851
2017-11-26 00:00:00 Total number of transactions done via:645
2017-11-27 00:00:00 Total number of transactions done via:263
2017-11-28 00:00:00 Total number of transactions done via:361
2017-11-29 00:00:00 Total number of transactions done via:1407
2017-11-30 00:00:00 Total number of transactions done via:3277
2017-12-01 00:00:00 Total number of transactions done via:5319
2017-12-02 00:00:00 Total number of transactions done via:5238
2017-12-03 00:00:00 Total number of transactions done via:8964
2017-12-04 00:00:00 Total number of transactions done via:21851
2017-12-05 00:00:00 Total number of transactions done via:29639
2017-12-06 00:00:00 Total number of transactions done via:37290
2017-12-07 00:00:00 Total number of transactions done via:30637
2017-12-08 00:00:00 Total number of transactions done via:37352
2017-12-09 00:00:00 Total number of transactions done via:44130
2017-12-10 00:00:00 Total number of transactions done via:52038
2017-12-11 00:00:00 Total number of transactions done via:56271
2017-12-12 00:00:00 Total number of transactions done via:52707
2017-12-13 00:00:00 Total number of transactions done via:40574
2017-12-14 00:00:00 Total number of transactions done via:30715
2017-12-15 00:00:00 Total number of transactions done via:29831
2017-12-16 00:00:00 Total number of transactions done via:23262
2017-12-17 00:00:00 Total number of transactions done via:23727
2017-12-18 00:00:00 Total number of transactions done via:21094
2017-12-19 00:00:00 Total number of transactions done via:19108
2017-12-20 00:00:00 Total number of transactions done via:13632
2017-12-21 00:00:00 Total number of transactions done via:15464
2017-12-22 00:00:00 Total number of transactions done via:11314
2017-12-23 00:00:00 Total number of transactions done via:10118
2017-12-24 00:00:00 Total number of transactions done via:12257
2017-12-25 00:00:00 Total number of transactions done via:10562
2017-12-26 00:00:00 Total number of transactions done via:9714
2017-12-27 00:00:00 Total number of transactions done via:8851
2017-12-28 00:00:00 Total number of transactions done via:9651
2017-12-29 00:00:00 Total number of transactions done via:10297
2017-12-30 00:00:00 Total number of transactions done via:9094
2017-12-31 00:00:00 Total number of transactions done via:7595
2018-01-01 00:00:00 Total number of transactions done via:8093
2018-01-02 00:00:00 Total number of transactions done via:7957
2018-01-03 00:00:00 Total number of transactions done via:7880
2018-01-04 00:00:00 Total number of transactions done via:3627
2018-01-05 00:00:00 Total number of transactions done via:2312
2018-01-06 00:00:00 Total number of transactions done via:2075
2018-01-07 00:00:00 Total number of transactions done via:2759
2018-01-08 00:00:00 Total number of transactions done via:3695
2018-01-09 00:00:00 Total number of transactions done via:2786
2018-01-10 00:00:00 Total number of transactions done via:1685
2018-01-11 00:00:00 Total number of transactions done via:1257
2018-01-12 00:00:00 Total number of transactions done via:1969
2018-01-13 00:00:00 Total number of transactions done via:2820
2018-01-14 00:00:00 Total number of transactions done via:2793
2018-01-15 00:00:00 Total number of transactions done via:3336
2018-01-16 00:00:00 Total number of transactions done via:1818
2018-01-17 00:00:00 Total number of transactions done via:2021
2018-01-18 00:00:00 Total number of transactions done via:2451
2018-01-19 00:00:00 Total number of transactions done via:3162
2018-01-20 00:00:00 Total number of transactions done via:3714
2018-01-21 00:00:00 Total number of transactions done via:4025
2018-01-22 00:00:00 Total number of transactions done via:3665
2018-01-23 00:00:00 Total number of transactions done via:3532
2018-01-24 00:00:00 Total number of transactions done via:3243
2018-01-25 00:00:00 Total number of transactions done via:3194
2018-01-26 00:00:00 Total number of transactions done via:2893
2018-01-27 00:00:00 Total number of transactions done via:3154
2018-01-28 00:00:00 Total number of transactions done via:3841
2018-01-29 00:00:00 Total number of transactions done via:3276
2018-01-30 00:00:00 Total number of transactions done via:3109
2018-01-31 00:00:00 Total number of transactions done via:3054
2018-02-01 00:00:00 Total number of transactions done via:2659
2018-02-02 00:00:00 Total number of transactions done via:1897
2018-02-03 00:00:00 Total number of transactions done via:2737
2018-02-04 00:00:00 Total number of transactions done via:2626
2018-02-05 00:00:00 Total number of transactions done via:2453
2018-02-06 00:00:00 Total number of transactions done via:2491
2018-02-07 00:00:00 Total number of transactions done via:2868
2018-02-08 00:00:00 Total number of transactions done via:3185
2018-02-09 00:00:00 Total number of transactions done via:3246
2018-02-10 00:00:00 Total number of transactions done via:2775
2018-02-11 00:00:00 Total number of transactions done via:2161
2018-02-12 00:00:00 Total number of transactions done via:3582
2018-02-13 00:00:00 Total number of transactions done via:2989
2018-02-14 00:00:00 Total number of transactions done via:3527
2018-02-15 00:00:00 Total number of transactions done via:6752
2018-02-16 00:00:00 Total number of transactions done via:8125
2018-02-17 00:00:00 Total number of transactions done via:5479
2018-02-18 00:00:00 Total number of transactions done via:4558
2018-02-19 00:00:00 Total number of transactions done via:4162
2018-02-20 00:00:00 Total number of transactions done via:4219
2018-02-21 00:00:00 Total number of transactions done via:4097
2018-02-22 00:00:00 Total number of transactions done via:3450
2018-02-23 00:00:00 Total number of transactions done via:3544
2018-02-24 00:00:00 Total number of transactions done via:3820

masked = sorted_df.from_address.unique()
print(len(masked))
print(masked)
totals = set(masked)
masked = sorted_df.to_address.unique()
print(len(masked))
print(masked)
for m in masked:
  if(m not in totals):
    totals.add(m)
print(len(sorted_df))

23
['0xba52c75764d6f594735dc735be7f1830cdf58ddf'
 '0xb1690c08e213a35ed9bab7b318de14420fb57d8c'
 '0x5bbd03cee5d26734611cf357b76b538afdd2bf9a'
 '0x0b2bdfcfa98567b7fc1d86a64a4a19bd91a6b9d9'
 '0x177900f0126850525d42dbe3995bf586d0c0e456'
 '0xaa71f3669de789d88aeb8df7e52403792c4aa2bf'
 '0x82d0785a810cb6436a71621ff6dcf2bcbea3cf35'
 '0xcca5859e65518124a7d0452d4338a61953629448'
 '0x316c55d1895a085c4b39a98ecb563f509301aaf7'
 '0xe2ab57fd065376ab10a232694dfe17cc350f78ba'
 '0xab7e97c3f00d23ed3d0b15bad55539f1163a1be8'
 '0x0b8546f4d2f324aba30168dc8acf113c301d3cfe'
 '0x730fafe1108f507b454b6eb59f0eec1f1ba31935'
 '0x77efe0036a742b8c3f31fa87dc28e277f3c21d4b'
 '0xe262021a78e238ed9d70492a2c1204fe2b83152b'
 '0xef42cf85be6adf3081ada73af87e27996046fe63'
 '0x9fce702b9bd1af98b0668c12f5fe92cf3e9bff85'
 '0x6e129c4bef43eb234a10b969f15bc564511bec6c'
 '0x07c9ceeee50cfc50f4283e8f0b18a207c2bc4a0d'
 '0xba45fd17869d163ecfe971f40fa22a820d1c4bb3'
 '0x1fd9277f03bd18f740bffbf9f160f10bd8fa9f11'
 '0x51c3016d6699480ec6becdf2155079c27ebae040'
 '0x98139e00121ce58f35221f6e78aacf67dbefd822']
81
['0xb1690c08e213a35ed9bab7b318de14420fb57d8c'
 '0x672ec49f7f7eac25c3a2e651f67f579bb5da8936'
 '0x76df4c63c28d462d5064add17f51e2ad55c9d5a2'
 '0x730fafe1108f507b454b6eb59f0eec1f1ba31935'
 '0x316c55d1895a085c4b39a98ecb563f509301aaf7'
 '0x0b8546f4d2f324aba30168dc8acf113c301d3cfe'
 '0x3849082eff243a825a82baea2a7c6f20a1134fd0'
 '0x40dbf0a418f63e45afcd4e839e3582d1f6dc476e'
 '0x30ad9c33809f33b599f1b9e6b69958e1c22b3ae0'
 '0xcca5859e65518124a7d0452d4338a61953629448'
 '0xc8e4b2cee16afcb60912e0bda109e7989e5a6755'
 '0x82d0785a810cb6436a71621ff6dcf2bcbea3cf35'
 '0x0b2bdfcfa98567b7fc1d86a64a4a19bd91a6b9d9'
 '0x44cd75d0eccac283c62184ef87626d639e047441'
 '0xab7e97c3f00d23ed3d0b15bad55539f1163a1be8'
 '0x77efe0036a742b8c3f31fa87dc28e277f3c21d4b'
 '0x177900f0126850525d42dbe3995bf586d0c0e456'
 '0xe2ab57fd065376ab10a232694dfe17cc350f78ba'
 '0xba45fd17869d163ecfe971f40fa22a820d1c4bb3'
 '0x5bbd03cee5d26734611cf357b76b538afdd2bf9a'
 '0xccf948a112727b4c0defc6c657b4364573b41dd3'
 '0x68767833f3e4a9547c9bdf737b67ef48ab626f65'
 '0xe262021a78e238ed9d70492a2c1204fe2b83152b'
 '0xeaa9c0b9b295e9580721532d28cc2a68d7365be9'
 '0x4236acd13e9ce90c3103bb8ed7c7ce3e374323d7'
 '0xaa71f3669de789d88aeb8df7e52403792c4aa2bf'
 '0x98139e00121ce58f35221f6e78aacf67dbefd822'
 '0xd50db15c1d9e96c4aa675d7959026c02d9667755'
 '0x80f892c4783892c9caa72c616ceeb9a40333b963'
 '0x9fce702b9bd1af98b0668c12f5fe92cf3e9bff85'
 '0xba52c75764d6f594735dc735be7f1830cdf58ddf'
 '0x51c1c43ebcebfa10b378eae4dce1d976a7e70fa9'
 '0x74c1a2c30a1fcdad9dc4345302e4da696d4ca4a2'
 '0x191cce7ebf71d0a8feaba9c4e7a270d93ee4961b'
 '0x2bd7ee2e2055288f82f1b9f51905aed671b04a18'
 '0x8b104344f397afc33ee55c743a0fbd7d956201cd'
 '0x79bd592415ff6c91cfe69a7f9cd091354fc65a18'
 '0x214e0807b739259e432c6d6842fb6c4bc8eae4da'
 '0x4a72a6f841ea30f088c99ff9856859bc38962c3a'
 '0x479c388aa97638e6e2efeb62cb3924fcb00a5285'
 '0x08cbf077180d45868cc04d8c053ec37025545933'
 '0x6e129c4bef43eb234a10b969f15bc564511bec6c'
 '0x0441ba4e3ffc47b2ff5c5f8a47d11d5d64b3ffeb'
 '0xdf56b7800d8065260507644a4332256a5d66b09f'
 '0xda45198b4b55476c4ac0ce9de7a60c2f47e0eaae'
 '0x30306c44c10cdaff1056f91695ee05b43756ae4f'
 '0x9a4d5aa3d95e2ff3cf9baac5be82cd4126042aa4'
 '0x9fcfe63108f0957aad1c6f2ed30270e8d35c6491'
 '0xd046b3c521c0f5513c8a47eb3c2011684ea80b27'
 '0xbd1b456e0f29d061eadd3d168dd93d9e2dc7e5ff'
 '0x7bbd940ebe9114d909e4797270286336e593b2d5'
 '0xeb53365cf3fdab44aa568c2736697e41b15e300e'
 '0x4db2dadce262866c924ce32a008c8d9ad8de86a2'
 '0xc9351545c105a7048a41501fc2a255ff19f9bd65'
 '0x4d4fc2b1d694d3d7647443b79a7efd3bf4efdb41'
 '0x1fd9277f03bd18f740bffbf9f160f10bd8fa9f11'
 '0x9c2ce25922dc592ffdf7f2c6b0a8678b21682116'
 '0x51c3016d6699480ec6becdf2155079c27ebae040'
 '0x7c069b7c5e2700616588f7599dd497124e784689'
 '0x682a4c54f9c9ce3a66700ae6f3b67f5984d85c21'
 '0x40d7c5fbc89c0ca3880d79e38e5099d786f4ab65'
 '0x05222d011ddf8eb7c8d9f5a36ec784068a7a25e5'
 '0xb89465f04caf9334d3d116637ac2a9c4aa8dcff6'
 '0x831f0aa1b671cade0078eee53b4408059047439d'
 '0x63687e06503d72aa8921c04248035abba47378dc'
 '0x01b24d8631caf2ed9385e440534fcbddc9cf962e'
 '0xef42cf85be6adf3081ada73af87e27996046fe63'
 '0x61f4c2bf662755e92fb68a99508cdbb37a7bf839'
 '0x0904dac3347ea47d208f3fd67402d039a3b99859'
 '0x07c9ceeee50cfc50f4283e8f0b18a207c2bc4a0d'
 '0x4db6e79514d777702815db6d028e4dda5eb7cc01'
 '0xd5bfcb58f900b31c8155057616482a9d4ad70170'
 '0xef0265a157fe4d27d9eba922a8ed9086d0055d8f'
 '0x1a40ca0774518d8f7f41df7da070a4aae08184f1'
 '0x9f7e9b98519c486c6739db1bcd27a2fcc996e242'
 '0xf50376fc03b7fbcfcc5cd6c134f8c59818db7e01'
 '0x6f5096a3f9746d9deaca0afa4a3d96c418221e24'
 '0x303c16271bd4c2f689d329523af34e891324eb5c'
 '0xdc2cf5cbfa31958ed32c3ace696ee6bf1a6a19f4'
 '0xf745f479a7da0b538ee5bbc35d719c7c481e1b7e'
 '0xfb8b5922f50b1522446ddc9f5a4a89113c77978d']
973

Edge and Node Count of Network

len(temporal_networks[0].edges)

len(temporal_networks[0].nodes)

len(totals)

Lets combine them properly.

Evaluation

import datetime
import pytz 
temp = datetime.datetime(2017,11,23)
#get respective information regarding on date
mask = pd.Timestamp(temp.year, temp.month, temp.day)
temp_df = df[df["date"]==mask]

len(temp_df[temp_df["from_address"]=="0xb1690c08e213a35ed9bab7b318de14420fb57d8c"])

len(temp_df[temp_df["to_address"]=="0xb1690c08e213a35ed9bab7b318de14420fb57d8c"])

temp_df[temp_df["from_address"]=="0xb1690c08e213a35ed9bab7b318de14420fb57d8c"]

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	token_address	from_address	to_address	value	transaction_hash	log_index	block_timestamp	block_number	block_hash	date	time
11	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x672ec49f7f7eac25c3a2e651f67f579bb5da8936	1001	0x5c8a04e3174fed51f8cbe8f8ec77b6d5714343c3ebc6...	62	2017-11-23 18:46:13	4608529	0x3105f33b84168256ce37bf8bd8629a60ff08b71fc6d1...	2017-11-23	18:46:13
12	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x76df4c63c28d462d5064add17f51e2ad55c9d5a2	1008	0x621c58ab6adc225f021cc66cc94003c4cf921f277375...	8	2017-11-23 18:46:58	4608531	0x1ce7af3b7ff7f93844e57100d275a4d48bb4a87c2ab7...	2017-11-23	18:46:58
22	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x730fafe1108f507b454b6eb59f0eec1f1ba31935	1003	0xc9812b823ddd7b32184525598aafeae78e3b4544a471...	50	2017-11-23 18:48:44	4608537	0x51a60adf64941cec197040d68d162d24b964111dbf5a...	2017-11-23	18:48:44
30	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x3849082eff243a825a82baea2a7c6f20a1134fd0	1004	0xa218fc480601f5810b51138ce92365deb00e9e0107d1...	57	2017-11-23 18:50:19	4608542	0x60a989e7e8cd4530f54c66c9dde5b08ebff70848df13...	2017-11-23	18:50:19
34	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x0b8546f4d2f324aba30168dc8acf113c301d3cfe	1009	0x55350380f679b0ee3f44bb4beff9a9f2394fb0dd7433...	59	2017-11-23 18:50:19	4608542	0x60a989e7e8cd4530f54c66c9dde5b08ebff70848df13...	2017-11-23	18:50:19
...	...	...	...	...	...	...	...	...	...	...	...
961	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xeaa9c0b9b295e9580721532d28cc2a68d7365be9	1385	0x47818bd6c6c2255a16ee7e266d3f7f359a1bec56ffd2...	80	2017-11-23 23:42:06	4609802	0x640aa1e0adefb9cafd71c29a22a72df29fff64d2905c...	2017-11-23	23:42:06
962	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x79bd592415ff6c91cfe69a7f9cd091354fc65a18	1403	0x2f4b3772b109f360e814257b988d92c97882ca9c0f98...	18	2017-11-23 23:42:09	4609803	0x6e5ef6a46adea1d34c1c34658201d64c604aa128965b...	2017-11-23	23:42:09
965	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xfb8b5922f50b1522446ddc9f5a4a89113c77978d	1423	0x83baca798126a4b1dc38473b08baf8e6bbff35b5a5b6...	4	2017-11-23 23:48:39	4609830	0x52ce5f2e09a94852a31137d8c65f4b2cb82e0cc5dd24...	2017-11-23	23:48:39
968	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xab7e97c3f00d23ed3d0b15bad55539f1163a1be8	1389	0xfdd417d260a0e227b6b523fc3e0c6df0efdd5e5197e4...	60	2017-11-23 23:53:56	4609851	0xf985a71a9bdc7b9a901b065846b0a26ad771f32abbe2...	2017-11-23	23:53:56
970	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0x1a40ca0774518d8f7f41df7da070a4aae08184f1	3319	0xae0d8b8be0f73045c5b193f3d4d580bb2d712bcabb6e...	17	2017-11-23 23:55:55	4609865	0x3b99c34e680627a8e8e83a2a373c265732511847f6e3...	2017-11-23	23:55:55

425 rows × 11 columns

temp = datetime.datetime(2017,11,23)
temporal_networks = []
while(temp<=datetime.datetime(2017,11,27)):#while(temp<=datetime.datetime(2018,2,23)):
  #get respective information regarding on date
  mask = pd.Timestamp(temp.year, temp.month, temp.day)
  temp_df = df[df["date"]<=mask]
  #sort by datetime - sorted data preserves order when filtered
  sorted_df = temp_df.sort_values(by="block_timestamp",ascending=True)
  temp_network = nx.MultiDiGraph()
  auctioned = sorted_df[sorted_df["to_address"]=="0xb1690c08e213a35ed9bab7b318de14420fb57d8c"]
  sold = sorted_df[sorted_df["from_address"]=="0xb1690c08e213a35ed9bab7b318de14420fb57d8c"]
  for index,instance in enumerate(auctioned.iterrows()):
      if(len(sold[sold["value"]==instance[1]["value"]])>0):
        sold_instance = sold[sold["value"]==instance[1]["value"]].iloc[[0]] #select top most and then remove top most
        sold.drop(sold_instance.index)
        temp_network.add_edge(instance[1]["from_address"],sold_instance["to_address"].values[0],token=instance[1]["value"])
      else:
        pass
        #temp_network.add_edge(instance[1]["from_address"],"0xb1690c08e213a35ed9bab7b318de14420fb57d8c",token=instance[1]["value"])
  temp += datetime.timedelta(days=1)
  temporal_networks.append(temp_network)

nx.write_gexf(temp_network,f"{path}.gexf")

x = temp_df[temp_df["from_address"]=="0xba52c75764d6f594735dc735be7f1830cdf58ddf"]
y = temp_df[temp_df["to_address"]=="0xba52c75764d6f594735dc735be7f1830cdf58ddf"]

len(x)

len(y)

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	token_address	from_address	to_address	value	transaction_hash	log_index	block_timestamp	block_number	block_hash	date	time
284	0x06012c8cf97bead5deae237070f9587f8e7a266d	0xb1690c08e213a35ed9bab7b318de14420fb57d8c	0xba52c75764d6f594735dc735be7f1830cdf58ddf	1150	0x03c2d6249416e041947ed896b63cd556b4a66391fb11...	47	2017-11-23 20:28:58	4608949	0xbbeceb36783cb4bec32159983f744cb8f2b73087643f...	2017-11-23	20:28:58

x[x["value"]==1150].iloc[[0]]["transaction_hash"]

241    0x09aeb996a6dcf755af5417395c585180c6e2bd22804279f16fc95323876b1cbd
Name: transaction_hash, dtype: object

y.iloc[[0]]["transaction_hash"]

284    0x03c2d6249416e041947ed896b63cd556b4a66391fb1114c2fb9123f4910066d2
Name: transaction_hash, dtype: object

This self-loop is Auction Cancellation.

Lets find most active users.

import operator
for temp_network in temporal_networks:
  degree_counts = dict(temp_network.degree())
  sorted_x = sorted(degree_counts.items(), key=operator.itemgetter(1),reverse=True)
  print(sorted_x[:10])

[('0xba52c75764d6f594735dc735be7f1830cdf58ddf', 394), ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 113), ('0x30ad9c33809f33b599f1b9e6b69958e1c22b3ae0', 17), ('0x4a72a6f841ea30f088c99ff9856859bc38962c3a', 17), ('0x0b8546f4d2f324aba30168dc8acf113c301d3cfe', 14), ('0x01b24d8631caf2ed9385e440534fcbddc9cf962e', 12), ('0x0b2bdfcfa98567b7fc1d86a64a4a19bd91a6b9d9', 11), ('0x177900f0126850525d42dbe3995bf586d0c0e456', 11), ('0xcca5859e65518124a7d0452d4338a61953629448', 10), ('0x479c388aa97638e6e2efeb62cb3924fcb00a5285', 10)]
[('0xba52c75764d6f594735dc735be7f1830cdf58ddf', 436), ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 114), ('0x51c3016d6699480ec6becdf2155079c27ebae040', 80), ('0x730fafe1108f507b454b6eb59f0eec1f1ba31935', 43), ('0x6e129c4bef43eb234a10b969f15bc564511bec6c', 31), ('0x0b2bdfcfa98567b7fc1d86a64a4a19bd91a6b9d9', 28), ('0xd50db15c1d9e96c4aa675d7959026c02d9667755', 24), ('0xaa71f3669de789d88aeb8df7e52403792c4aa2bf', 23), ('0xf50376fc03b7fbcfcc5cd6c134f8c59818db7e01', 22), ('0x30306c44c10cdaff1056f91695ee05b43756ae4f', 21)]
[('0xba52c75764d6f594735dc735be7f1830cdf58ddf', 436), ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 114), ('0x51c3016d6699480ec6becdf2155079c27ebae040', 96), ('0xf50376fc03b7fbcfcc5cd6c134f8c59818db7e01', 67), ('0x730fafe1108f507b454b6eb59f0eec1f1ba31935', 46), ('0xc0cb8ba0e2bfeb45a7cdc40dfc95b77ce8d2bab2', 46), ('0x6e129c4bef43eb234a10b969f15bc564511bec6c', 39), ('0xd50db15c1d9e96c4aa675d7959026c02d9667755', 37), ('0xaa71f3669de789d88aeb8df7e52403792c4aa2bf', 35), ('0x0b2bdfcfa98567b7fc1d86a64a4a19bd91a6b9d9', 34)]
[('0xba52c75764d6f594735dc735be7f1830cdf58ddf', 436), ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 114), ('0x51c3016d6699480ec6becdf2155079c27ebae040', 104), ('0xf50376fc03b7fbcfcc5cd6c134f8c59818db7e01', 69), ('0x764d6f045b6ab1a60c4595144c185c5ee60fa3df', 51), ('0x730fafe1108f507b454b6eb59f0eec1f1ba31935', 50), ('0xc0cb8ba0e2bfeb45a7cdc40dfc95b77ce8d2bab2', 50), ('0xd50db15c1d9e96c4aa675d7959026c02d9667755', 43), ('0x4a93c3c014da94070df96352d3e6795e0b682022', 43), ('0x6e129c4bef43eb234a10b969f15bc564511bec6c', 40)]
[('0xba52c75764d6f594735dc735be7f1830cdf58ddf', 436), ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 114), ('0x51c3016d6699480ec6becdf2155079c27ebae040', 111), ('0xf50376fc03b7fbcfcc5cd6c134f8c59818db7e01', 77), ('0x730fafe1108f507b454b6eb59f0eec1f1ba31935', 57), ('0xc0cb8ba0e2bfeb45a7cdc40dfc95b77ce8d2bab2', 57), ('0x764d6f045b6ab1a60c4595144c185c5ee60fa3df', 55), ('0x4a93c3c014da94070df96352d3e6795e0b682022', 48), ('0xd50db15c1d9e96c4aa675d7959026c02d9667755', 46), ('0xc3bbf1f914ac66bf61fe120657d445a5dad13265', 43)]

nx.draw(temp_network)

!pip install powerlaw

Collecting powerlaw
  Downloading https://files.pythonhosted.org/packages/d5/4e/3ceab890fafff8e78a5fd7f5340c232c38b21d181fcd32d7a31079db3646/powerlaw-1.4.6.tar.gz
Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from powerlaw) (1.4.1)
Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from powerlaw) (1.19.5)
Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from powerlaw) (3.2.2)
Requirement already satisfied: mpmath in /usr/local/lib/python3.7/dist-packages (from powerlaw) (1.2.1)
Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->powerlaw) (2.8.1)
Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->powerlaw) (0.10.0)
Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->powerlaw) (2.4.7)
Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->powerlaw) (1.3.1)
Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.1->matplotlib->powerlaw) (1.15.0)
Building wheels for collected packages: powerlaw
  Building wheel for powerlaw (setup.py) ... �[?25l�[?25hdone
  Created wheel for powerlaw: filename=powerlaw-1.4.6-cp37-none-any.whl size=24787 sha256=e7e68a13c25c9be87b208022eeaa84339eacc6887799c9055be660b10f3e55bc
  Stored in directory: /root/.cache/pip/wheels/e0/27/02/08d0e2865072bfd8d7c655e290521e3feca3fc22f1ac460601
Successfully built powerlaw
Installing collected packages: powerlaw
Successfully installed powerlaw-1.4.6

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import networkx as nx
import powerlaw
import math
import operator
import os

network = nx.read_gexf("/content/gdrive/MyDrive/project-kittybang/13_12_2017.gexf")
len(network.nodes)

Degree Distribution

network.to_undirected()

<networkx.classes.multigraph.MultiGraph at 0x7f23860cf410>

degree_sequence = sorted([d for n, d in network.degree()], reverse=True) # used for degree distribution and powerlaw test

fit = powerlaw.Fit(degree_sequence,xmin=1)

/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))

fig2 = fit.plot_pdf(color='b', linewidth=2)
fit.power_law.plot_pdf(color='g', linestyle='--', ax=fig2)

/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))





<matplotlib.axes._subplots.AxesSubplot at 0x7f23860d3310>

R, p = fit.distribution_compare('power_law', 'exponential', normalized_ratio=True)
print (R, p)

3.4211532484856524 0.0006235618463377406


/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: divide by zero encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))
/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))

plt.figure(figsize=(10, 6))
fit.distribution_compare('power_law', 'lognormal')
fig4 = fit.plot_ccdf(linewidth=3, color='black')
fit.power_law.plot_ccdf(ax=fig4, color='r', linestyle='--') #powerlaw
fit.lognormal.plot_ccdf(ax=fig4, color='g', linestyle='--') #lognormal
fit.stretched_exponential.plot_ccdf(ax=fig4, color='b', linestyle='--')

/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))
/usr/local/lib/python3.7/dist-packages/powerlaw.py:700: RuntimeWarning: invalid value encountered in true_divide
  (Theoretical_CDF * (1 - Theoretical_CDF))





<matplotlib.axes._subplots.AxesSubplot at 0x7f238623d8d0>

graph = nx.barabasi_albert_graph(200000, 5)
k = []
Pk = []
for node in list(graph.nodes()):
    degree = graph.degree(nbunch=node)
    try:
        pos = k.index(degree)
    except ValueError as e:
        k.append(degree)
        Pk.append(1)
    else:
        Pk[pos] += 1
logk = []
logPk = []
# get a double log representation
for i in range(len(k)):
    logk.append(math.log10(k[i]))
    logPk.append(math.log10(Pk[i]))

order = np.argsort(logk)
logk_array = np.array(logk)[order]
logPk_array = np.array(logPk)[order]
plt.plot(logk_array, logPk_array, ".")
plt.xlabel("k")
plt.ylabel("P(k)")
plt.title("Degree Distribution in loglog form")
m, c = np.polyfit(logk_array, logPk_array, 1)
plt.plot(logk_array, m*logk_array + c, "-")

[<matplotlib.lines.Line2D at 0x7f23c9c1f8d0>]

k = []
Pk = []
graph=network
for node in list(graph.nodes()):
    degree = graph.degree(nbunch=node)
    try:
        pos = k.index(degree)
    except ValueError as e:
        k.append(degree)
        Pk.append(1)
    else:
        Pk[pos] += 1
logk = []
logPk = []
# get a double log representation
for i in range(len(k)):
    logk.append(math.log10(k[i]))
    logPk.append(math.log10(Pk[i]))

order = np.argsort(logk)
logk_array = np.array(logk)[order][:len(np.array(logk)[order])-1]
logPk_array = np.array(logPk)[order][:len(np.array(logPk)[order])-1]
plt.plot(logk_array, logPk_array, ".")
plt.xlabel("k")
plt.ylabel("P(k)")
plt.title("Degree Distribution in loglog form")
m, c = np.polyfit(logk_array, logPk_array, 1)
plt.plot(logk_array, m*logk_array + c, "-")

[<matplotlib.lines.Line2D at 0x7f23865a0990>]

#H-index Distribution

def _h_index_helper(iterable, sorted=False):
    if not isinstance(iterable, list):
        iterable = list(iterable)
    if not sorted:
        iterable.sort()

    value_count = len(iterable)
    for index, value in enumerate(iterable):
        result = value_count - index
        if result <= value:
            return result

    return 0

def h_index(G, node):
    """
    The Hirsch Index
    Parameters
    ----------
    G: graph
        A networkx Graph
    node: object
        A node in the networkx graph

    Returns
    -------
    int:  the H-index of the given node
    """
    neighbors = list(G.neighbors(node))
    degrees = list(degree for n, degree in G.degree(neighbors))
    return _h_index_helper(degrees)

graph = nx.barabasi_albert_graph(200000, 5)
k = []
Hk = []
for node in list(graph.nodes()):
    hindex_ = h_index(graph,node)
    try:
        pos = k.index(hindex_)
    except ValueError as e:
        k.append(hindex_)
        Hk.append(1)
    else:
        Hk[pos] += 1
logk = []
logPk = []
# get a double log representation
for i in range(len(k)):
  if(k[i]!=0):
    logk.append(math.log10(k[i]))
    logPk.append(math.log10(Hk[i]))
  else:
    logk.append(0.00000001)
    logPk.append(math.log10(Hk[i]))
order = np.argsort(logk)
logk_array = np.array(logk)[order]
logPk_array = np.array(logPk)[order]
plt.plot(logk_array, logPk_array, ".")
plt.xlabel("k")
plt.ylabel("H(k)")
plt.title("H-index Distribution in loglog form")
m, c = np.polyfit(logk_array, logPk_array, 1)
plt.plot(logk_array, m*logk_array + c, "-")

[<matplotlib.lines.Line2D at 0x7f2386582dd0>]

graph = network 
k = []
Hk = []
for node in list(graph.nodes()):
    hindex_ = h_index(graph,node)
    try:
        pos = k.index(hindex_)
    except ValueError as e:
        k.append(hindex_)
        Hk.append(1)
    else:
        Hk[pos] += 1
logk = []
logPk = []
# get a double log representation
for i in range(len(k)):
  if(k[i]!=0):
    logk.append(math.log10(k[i]))
    logPk.append(math.log10(Hk[i]))
  else:
    logk.append(0.00000001)
    logPk.append(math.log10(Hk[i]))
order = np.argsort(logk)
logk_array = np.array(logk)[order]
logPk_array = np.array(logPk)[order]
plt.plot(logk_array, logPk_array, ".")
plt.xlabel("k")
plt.ylabel("H(k)")
plt.title("H-index Distribution in loglog form")
m, c = np.polyfit(logk_array, logPk_array, 1)
plt.plot(logk_array, m*logk_array + c, "-")

[<matplotlib.lines.Line2D at 0x7f23861e1e90>]

max_ = 0
for node in network.nodes:
  if(max_<network.degree(node)):
    max_=network.degree(node)
print(f"Contract Address has {max_} connections")

Contract Address has 167740 connections

degrees = [(node,val) for (node, val) in network.degree()]
sorted(degrees, key=lambda x: x[1], reverse=True)[:20]

[('0xb1690c08e213a35ed9bab7b318de14420fb57d8c', 167740),
 ('0x3462177d5137e02d233a0ac00189e598a639c5f8', 1919),
 ('0x7e64b04ad39aef0751a16ac3fae6f499c1a87677', 1416),
 ('0x79bd592415ff6c91cfe69a7f9cd091354fc65a18', 1364),
 ('0xf1fa9a38914e853de933fbf7df2f278701e873df', 1346),
 ('0x06012c8cf97bead5deae237070f9587f8e7a266d', 1304),
 ('0x8081ecb6f3350fe5ed46f1bd74d5ada62c62e740', 1261),
 ('0x74f24ecda8a2d4fb3bfe5beeb1a1bdd87f9131d5', 1159),
 ('0x00ce0cea39ffeb12a89824266689df9a96b28870', 1044),
 ('0x3e5386fc13c123da53523f6b11417e83433c4fca', 1035),
 ('0x39e1262c564db2050a73b16f23f0112b2ad34a27', 978),
 ('0x88ba6d5a7abe727d73b0f4c7965775b959646f7e', 934),
 ('0x87fd2bb8e0a0069e645ec17b429b805696946afb', 894),
 ('0x491677edd34020bf644fd63b5d65113fb3b74b10', 869),
 ('0xacbba937a57ece1298b5d350f40c0eb16ec5fa4b', 848),
 ('0x5c035bb4cb7dacbfee076a5e61aa39a10da2e956', 828),
 ('0x00000062eb9388ce580151552d1eb0eb16457464', 804),
 ('0x6e129c4bef43eb234a10b969f15bc564511bec6c', 786),
 ('0xd387a6e4e84a6c86bd90c158c6028a58cc8ac459', 769),
 ('0x0065df99477370bf434a02734cc43477cce39d51', 764)]

Average Path Length

undinetwork = network.to_undirected()

length = []
avg = []
S = [undinetwork.subgraph(c).copy() for c in nx.connected_components(undinetwork)]
for s in S:
  temp = nx.average_shortest_path_length(s)
  if(temp!=0):
    avg.append(temp)
    length.append(len(s.edges()))

sum=0
lensum=0
for index,len_ in enumerate(length):
  sum+=avg[index]*len_
  lensum+=len_
print(lensum)
print(sum/lensum)
print(math.log(math.log(len(undinetwork.nodes))))

263400
2.6494942852528154
2.341666333067817

According to Albert-Barabasi average distance is lnln(N) for degree exponent 2<y<3

#Percolation

graph = nx.barabasi_albert_graph(200000, 5)
nx.set_node_attributes(graph, 0.1, 'percolation')
nx.percolation_centrality(graph,attribute='percolation')

This computation takes a very long time to calculate the respective results so this property is neglected for large graph.

#Clustering Coefficient

Multigraph Clustering Coefficients For Nodes

G = nx.DiGraph()
for u,v in network.edges():
    if G.has_edge(u,v):
        G[u][v]['weight'] += 1
    else:
        G.add_edge(u, v, weight=1)

clustering = nx.clustering(G,weight='weight')

degree_seq = {}
for node,cc in clustering.items():
  print(node , cc)
  degree = G.degree(node)
  if (degree in degree_seq):
    degree_seq[degree].append(cc)
  else:
    degree_seq[degree] = [cc]

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0xdae7839d4e504cd3b073c95fc93a5de505e592c4 0
0x3abfb38ba5b9b7feec17d50021f5e852fe126861 0
0x86e8f95cc03f7fc177b146d8f7733528f7b15471 0
0x6532673eb4b0851d7633fc0654d6a799e56994b1 0
0xcdebcf59ee33978320ca2ebcd433ace6144c63c4 0
0x201207f2736b9852dadaa75bef98a5008af417de 0
0x59a7e218043713c62b37ac335559f9b4d9d6d76f 0
0x669d9e86c31f1cd09d8d9ccaf9e427b047562b18 0
0xcfee29a030e7bd3ff71852a69ccc08ed7006416a 0
0x4ab198061bd056705755ca851c054f25e1e1e325 0
0xa52f7430e05ce4457436db0b8bbd11e287a61329 0
0x24857af62704a522f59f3ae7b2c5fb0c586f73a6 0
0x2eef6b27a17a4fcb94c30822c15481d8e8dda361 0
0xd08a542c912fdda32a85e7e8516ec355ca65e699 0
0x3a9ff7c47c6306fee96271f92e3a7b7b5dd4f2ad 0
0x653ff83bbfa090e993a0b7cb936e1cecb502a6a2 0
0x0020f6e1ac7a225bb5852fe6bdec2cd53261d30e 0
0x28f765c99cfb98ee969899fb1a8c6a389d0183cc 0
0x2a59764afcf7086c0931d01d860208baa632a2cd 0
0xfa176e5cc34e36dc24c02c03dad0ac745b459ea8 0
0x2f3d7894c0eebbd722c928304b82920963206207 0
0x13f0b7263693f35d17f84aab52f8cd029f7e1873 0
0x7461ebd33eea7a104917ffc46b3e7ae992fd0efb 0
0x2022f9e6030b5df414aa1ff17cead809931cc309 0
0x5fdc3d06a8a3f2247b5d900f9f16c95c5e648375 0
0x6cb38528ec03e48d90ea95eeed7856e504320933 0
0xe3332a571aeef4b97ef8f04f73f33e64aff28c33 0
0x764ee55fb08540bde0c8408f6e666b7c840c7a8b 0.004396574608178945
0x0f50fe357a3ed83734a428ab76e149316d01c9f3 0
0xc8aeda8dc230c6e7e96d230a4051dbce472a2e23 0
0x60a9efb7f5e12ae1ca1f399ad5eaaeff5ab2fade 0
0xbf5840c7d5f690c0e0d020a4318b84ae31f89b12 0
0x91ff478eeba3d9f59f2df9055e58e1a6b90db67c 0
0x01600b690f335a26988d3c83c4857dc547f084aa 0
0xe3f2d38fd192e07a5cde71a1c90a71f0dd841f7d 0.0018079450697123995
0xb64416bdfc41df1c86d4612b9955cd1bc821713e 0
0xa5a5f451c8cc4f789f9115b284e45c29fae929d1 0
0x6a5a58b204dfa580815a9f30b53228583b737836 0
0xc6e1484523fbe59db0d353c301464d6a189b0e88 0
0x0babf2eaec65388c71c455aeed48b6444ff843dc 0

distribution = {}
#average cc'S with respect to degrees
for key in degree_seq:
  print(degree_seq[key])
  sum_=0.0
  for value in degree_seq[key]:
    sum_+=value
  size = len(degree_seq[key])

  distribution[key] = sum_/size

[0.0004287277388349819, 0.00013158248876357057]
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[0.0006208420001926612, 0.000751856091782791, 0.000563124113250419, 0.0007188914709217427, 0.0010381570266565841, 0.00042314160747151426, 0.000536401420930798, 0.0006287033202895104, 0.0005632133643977916, 0.000566524578329856, 0.0005689600167262224, 0.00033952627290459176, 0.0008087912307300661, 0.0005551425528159359, 0.0006758072512709384, 0.0005494043105958838, 0.0005160532285018568, 0.0008397252292540843, 0.0006224802335431321, 0.0005800709322063346, 0.0005856488683722054, 0.0004986271694064112, 0.0006728144595754157, 0.00044901618820276414, 0.0004073046562075982, 0.0005623707809438286, 0.0004826134106473469, 0.0005865872461885452, 0.00046583964129142004, 0.0011374918778304086, 0.00047881988085315025, 0.00047537703029014596, 0.00030113992770747287, 0.0005877626440352107, 0.00044885639796081494, 0.0005995405926106081, 0.0005424891462310501, 0.00028807403466884644, 0.001063268453050386, 0.0007255353591439313, 0.0006067356394507975, 0.0003848541646664512, 0.0006820424331726522, 0.0009530048820132954, 0.0005564014700771092, 0.0006717587059441023, 0.0004286153028929206, 0.00026973796528057724, 0.0009762397058528662, 0.0003873245842712082, 0.00035639950107648236, 0.0009831731262503298, 0.00040180000255196207, 0.0009970838149668932, 0.000402317715470919, 0.00021529776292516454, 0.00035538312412651264, 0.0005674233359752559, 0.0005005171668237582, 0.0003109526215761397, 0.0013011298384917381, 0.000706360532315444, 0.0005555677373877281, 0.000783164426729397, 0.0007095697538408826, 0.0002993816332985792, 0.00048698238232942014]
[1.0970591776082832e-06]
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[0.00017868982786328533, 0.00015641575336620416, 0.00018416696269293057, 0.0002165275548152124]
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