Homophily, structure, and content augmented network representation learning

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

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

89 Citations (Scopus)


Advances in social networking and communication technologies have witnessed an increasing number of applications where data is not only characterized by rich content information, but also connected with complex relationships representing social roles and dependencies between individuals. To enable knowledge discovery from such networked data, network representation learning (NRL) aims to learn vector representations for network nodes, such that off-The-shelf machine learning algorithms can be directly applied. To date, existing NRL methods either primarily focus on network structure or simply combine node content and topology for learning. We argue that in information networks, information is mainly originated from three sources: (1) homophily, (2) topology structure, and (3) node content. Homophily states social phenomenon where individuals sharing similar attributes (content) tend to be directly connected through local relational ties, while topology structure emphasizes more on global connections. To ensure effective network representation learning, we propose to augment three information sources into one learning objective function, so that the interplay roles between three parties are enforced by requiring the learned network representations (1) being consistent with node content and topology structure, and also (2) following the social homophily constraints in the learned space. Experiments on multi-class node classification demonstrate that the representations learned by the proposed method consistently outperform state-of-The-Art NRL methods, especially for very sparsely labeled networks.

Original languageEnglish
Title of host publicationProceedings - 16th IEEE International Conference on Data Mining, ICDM 2016
EditorsFrancesco Bonchi, Xindong Wu, Ricardo Baeza-Yates, Josep Domingo-Ferrer, Zhi-Hua Zhou
PublisherIEEE, Institute of Electrical and Electronics Engineers
Number of pages10
ISBN (Electronic)9781509054725
Publication statusPublished - 2016
Externally publishedYes
EventIEEE International Conference on Data Mining 2016 - Barcelona Catalonia, Spain
Duration: 12 Dec 201615 Dec 2016
Conference number: 16th
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7837023 (IEEE Conference Proceedings)

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
ISSN (Print)1550-4786


ConferenceIEEE International Conference on Data Mining 2016
Abbreviated titleICDM 2016
CityBarcelona Catalonia
Internet address

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