Unsupervised Attributed Multiplex Network Embedding (AAAI 2020)

Overview

Unsupervised Attributed Multiplex Network Embedding (DMGI)

Overview

Nodes in a multiplex network are connected by multiple types of relations. However, most existing network embedding methods assume that only a single type of relation exists between nodes. Even for those that consider the multiplexity of a network, they overlook node attributes, resort to node labels for training, and fail to model the global properties of a graph. We present a simple yet effective unsupervised network embedding method for attributed multiplex network called DMGI, inspired by Deep Graph Infomax (DGI) that maximizes the mutual information between local patches of a graph, and the global representation of the entire graph. We devise a systematic way to jointly integrate the node embeddings from multiple graphs by introducing 1) the consensus regularization framework that minimizes the disagreements among the relation-type specific node embeddings, and 2) the universal discriminator that discriminates true samples regardless of the relation types. We also show that the attention mechanism infers the importance of each relation type, and thus can be useful for filtering unnecessary relation types as a preprocessing step. Extensive experiments on various downstream tasks demonstrate that DMGI outperforms the state-of-the-art methods, even though DMGI is fully unsupervised.

Paper

Requirements

  • Python version: 3.6.8
  • Pytorch version: 1.2.0
  • networkx version: 2.3

How to Run

git clone https://github.com/pcy1302/DMGI.git
cd DMGI
mkdir saved_model
  • Download IMDB data from here to data
python main.py --embedder DMGI --dataset imdb --metapaths MAM,MDM --isAttn
  • Refer to the directory data for preprocessing for DBLP and Amazon datasets.

Data format [(ex) IMDB]

  • A dictionary containing the following keys

    • train_idx: training index, val_idx: validation index, test_idx: test index, feature: feature matrix, label: labels
    • Relations: MDM, MAM
  • NEW (20/10/06): You can download all the preprocessed datasets used in the paper from here

Cite (Bibtex)

  • If you find DMGI useful in your research, please cite the following paper:
    • Park, Chanyoung, Donghyun Kim, Jiawei Han, and Hwanjo Yu. "Unsupervised Attributed Multiplex Network Embedding." AAAI 2020.
    • Bibtex
@article{park2019unsupervised,
  title={Unsupervised Attributed Multiplex Network Embedding},
  author={Park, Chanyoung and Kim, Donghyun and Han, Jiawei and Yu, Hwanjo},
  booktitle={AAAI},
  year={2020}
}
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Comments
  • Running with dataset

    Running with dataset "IMDB"

    Dear authors : I use the "IMDB" dataset and run the program,I didn't change the parameters, but I got the different results from your paper. So,could you help me to explain this ? The following is the result.

    DMGI: [Classification] Macro-F1: 0.6509 (0.0101) | Micro-F1: 0.6519 (0.0076) [Clustering] NMI: 0.2015 [Similarity] [5,10,20,50,100] : [0.6115,0.6022,0.5944,0.581,0.5674]

    DMGI(attention): [Classification] Macro-F1: 0.6163 (0.0114) | Micro-F1: 0.6184 (0.0083) [Clustering] NMI: 0.1992 [Similarity] [5,10,20,50,100] : [0.5868,0.5754,0.5646,0.555,0.5449]

    opened by wangwenchu 6
  • Other Dataset

    Other Dataset

    Hi, I am excited to see this work which implemented Deep Graph Infomax on Multiplex Network! And I want to test ACM, DBLP, Amazon Dataset based on your code, can you provide these data sets and code about preprocess about them? I will be appreciated for your reply as soon as possible, thank you very much!

    opened by wjlpku 6
  • Is there a mistake in the paper about loss function?

    Is there a mistake in the paper about loss function?

    Hi, thanks for you great work here! I read the paper Deep Graph Infomax, DGI introduce a discriminator $D$ to maximize the mutual information. (Eq(1) in the paper). And it say this term should be maximized. But in your final loss function, there is no minus sign in front of it.

    I haven't understood DGI much, so I wonder if I got it wrong or there is a written mistake?

    Regards,

    opened by changym3 2
  • a little problem about results

    a little problem about results

    hi,sorry to interrupt you again~ we can see you get a so big improvement on amazon dataset ! i run your code on amazon dataset , and search parameters as your paper said "α; β; from 0:0001; 0:001; 0:01; 0:1". but i am sad i can not get a comparable result as your paper. some details is below: nmi: my best result is 0.2858 | 0.2954 for DMGI-att and DMGI. your paper is 0.412 and 0.425 macro | micro : my best result is 0.7426 | 0.7462| 0.7380| 0.7414 for DMGI-att and DMGI. your paper is 0.758 | 0.758 | 0.746 | 0.748. We can see my result is very close to you . sim: my best result is 0.820 and 0.809 for DMGI-att and DMGI. , your paper is 0.825 and 0.816 . we can see my result is also very close to you .

    so i am very confused about the performance about the metric nmi. do you meet the same problem or can you give me some suggestions?

    opened by chenbofeng123 2
Owner
Chanyoung Park
Assistant Professor @ KAIST
Chanyoung Park
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