Co-Regularized Deep Multi-Network Embedding

This is a reference implementation for DMNE. The DMNE algorithm learns node representations on multi-network data. Please refer to the following paper for details.

Reference:

Co-Regularized Deep Multi-Network Embedding
Jingchao Ni, Shiyu Chang, Xiao Liu, Wei Cheng, Haifeng Chen, Dongkuan Xu and Xiang Zhang
Proceedings of the International Conference on World Wide Web (WWW), 2018.

For any questions about the code, please contact Jingchao Ni ([email protected]).

Input

The format of the input data is the edge list of each network.

Domain network

node_id node_id edge_weight

For undirected networks, the same edge will be written in two directions, e.g., 1, 2, 1.00 and 2, 1, 1.00.

Cross-network relationship

node_id_in_domain_1 node_id_in_domain_2 relationship_weight

Label in each domain (for evaluation)

node_id label

Output

For each network, there is an output file in emb/. If a network has n nodes, there are n+1 lines in its output file. The first line contains the number of nodes and the dimensionality of the embeddings.

num_of_nodes dim_of_embedding

The next n lines contain node embeddings.

node_id dim_1 dim_2 ... dim_d

where dim_1, ..., dim_d are the d-dimensional embedding of a node.

Running

• Install libsvm in libsvm/.
• Run rundemo.m to see the demo program on 6ng dataset.

Citing

If you find DMNE useful for your research, please consider citing the following paper:

@inproceedings{ni2018co,
  title={Co-Regularized Deep Multi-Network Embedding},
  author={Ni, Jingchao and Chang, Shiyu and Liu, Xiao and Cheng, Wei and Chen, Haifeng and Xu, Dongkuan and Zhang, Xiang},
  booktitle={Proceedings of the International Conference on World Wide Web (WWW)},
  pages={469--478},
  year={2018},
  organization={International World Wide Web Conferences Steering Committee}
}