Cross-graph: robust and unsupervised embedding for attributed graphs with corrupted structure

Chun Wang, Bo Han, Shirui Pan, Jing Jiang, Gang Niu, Guodong Long

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

3 Citations (Scopus)

Abstract

Graph embedding has shown its effectiveness to represent graph information and capture deep relationships in graph data. Most recent graph embedding methods focus on attributed graphs, since they preserve both structure and content information in the network. However, corruption can exist in the graph structure as well as the node content of the graph, and both can lead to inferior embedding results. Unfortunately, few existing graph embedding algorithms have considered the corruption problem, and to the best of our knowledge, none has studied structural corruption in attributed graphs, including missing and redundant edges. This field is difficult for previous methods, mainly due to two challenges: (1) the existence of various corruption causes has made it difficult to recognize corruptions in graphs, and (2) the complexity of graph-structured data has increased the difficulty of handling corruption therein for graph embedding methods. These facts lead us here to propose a novel autoencoder-based graph embedding approach, which is robust against structural corruption. Our idea comes from the recent discovery of memorization effects in deep learning. Namely, deep neural networks prefer to fit clean data first, before they over-fit corrupted data. Specifically, we train two autoencoders simultaneously and let them learn the reliability of the edges in the graph from each other. The two autoencoders would evaluate the edges according to their reconstructed structure and manipulate this by devaluing those distrusted edges to update the structure information. The updated structure would be used further in the next iteration as the ground-truth of its peer-network. Experiments on different versions of real-world graphs show state-of-the-art results and demonstrate the robustness of our model against structural corruption.

Original languageEnglish
Title of host publicationProceedings - 20th IEEE International Conference on Data Mining, ICDM 2020
EditorsClaudia Plant, Haixun Wang, Alfredo Cuzzocrea, Carlo Zaniolo, Xindong Wu
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages571-580
Number of pages10
ISBN (Electronic)9781728183169
ISBN (Print)9781728183176
DOIs
Publication statusPublished - 2020
EventIEEE International Conference on Data Mining 2020 - Virtual, Sorrento, Italy
Duration: 17 Nov 202020 Nov 2020
Conference number: 20th
http://icdm.bigke.org/ (Website)
https://ieeexplore.ieee.org/xpl/conhome/9338245/proceeding (Proceedings)

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
PublisherIEEE, Institute of Electrical and Electronics Engineers
Volume2020-November
ISSN (Print)1550-4786
ISSN (Electronic)2374-8486

Conference

ConferenceIEEE International Conference on Data Mining 2020
Abbreviated titleICDM 2020
Country/TerritoryItaly
CitySorrento
Period17/11/2020/11/20
Internet address

Keywords

  • Graph autoencoder
  • Graph convolutional network
  • Network representation
  • Structural corruption

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