Semi-Supervised Graph-to-Graph Translation

Tianxiang Zhao, Xianfeng Tang, Xiang Zhang, Suhang Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

Abstract

Graph translation is very promising research direction and has awide range of potential real-world applications. Graph is a natural structure for representing relationship and interactions, and its translation can encode the intrinsic semantic changes of relation-ships in different scenarios. However, despite its seemingly wide possibilities, usage of graph translation so far is still quite limited.One important reason is the lack of high-quality paired dataset. For example, we can easily build graphs representing peoples? shared music tastes and those representing co-purchase behavior, but a well paired dataset is much more expensive to obtain. Therefore,in this work, we seek to provide a graph translation model in the semi-supervised scenario. This task is non-trivial, because graph translation involves changing the semantics in the form of link topology and node attributes, which is difficult to capture due to the combinatory nature and inter-dependencies. Furthermore, due to the high order of freedom in graph's composition, it is difficult to assure the generalization ability of trained models. These difficulties impose a tighter requirement for the exploitation of unpaired samples. Addressing them, we propose to construct a dual representation space, where transformation is performed explicitly to model the semantic transitions. Special encoder/decoder structures are designed, and auxiliary mutual information loss is also adopted to enforce the alignment of unpaired/paired examples. We evaluate the proposed method in three different datasets.

Original languageEnglish (US)
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages1863-1872
Number of pages10
ISBN (Electronic)9781450368599
DOIs
StatePublished - Oct 19 2020
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: Oct 19 2020Oct 23 2020

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
Country/TerritoryIreland
CityVirtual, Online
Period10/19/2010/23/20

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

Fingerprint

Dive into the research topics of 'Semi-Supervised Graph-to-Graph Translation'. Together they form a unique fingerprint.

Cite this