Universal network representation for heterogeneous information networks

Ruiqi Hu, Celina Ping Yu, Sai Fu Fung, Shirui Pan, Haishuai Wang, Guodong Long

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

5 Citations (Scopus)


Network representation aims to represent the nodes in a network as continuous and compact vectors, and has attracted much attention in recent years due to its ability to capture complex structure relationships inside networks. However, existing network representation methods are commonly designed for homogeneous information networks where all the nodes (entities) of a network are of the same type, e.g., papers in a citation network. In this paper, we propose a universal network representation approach (UNRA), that represents different types of nodes in heterogeneous information networks in a continuous and common vector space. The UNRA is built on our latest mutually updated neural language module, which simultaneously captures inter-relationship among homogeneous nodes and node-content correlation. Relationships between different types of nodes are also assembled and learned in a unified framework. Experiments validate that the UNRA achieves outstanding performance, compared to six other state-of-the-art algorithms, in node representation, node classification, and network visualization. In node classification, the UNRA achieves a 3% to 132% performance improvement in terms of accuracy.

Original languageEnglish
Title of host publicationInternational Joint Conference on Neural Networks (IJCNN) 2017
Subtitle of host publicationAnchorage, Alaska, USA May 14 – May 19, 2017
EditorsChrisina Jayne
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509061815, 9781509061822
ISBN (Print)9781509061839
Publication statusPublished - 2017
Externally publishedYes
EventIEEE International Joint Conference on Neural Networks 2017 - Anchorage, United States of America
Duration: 14 May 201719 May 2017
https://ieeexplore.ieee.org/xpl/conhome/7958416/proceeding (Proceedings)


ConferenceIEEE International Joint Conference on Neural Networks 2017
Abbreviated titleIJCNN 2017
Country/TerritoryUnited States of America
Internet address

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