Homophily, structure, and content augmented network representation learning

Daokun Zhang, Jie Yin, Xingquan Zhu, Chengqi Zhang

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

96 Citations (Scopus)

Abstract

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, Josep Domingo-Ferrer, Ricardo Baeza-Yates, Zhi-Hua Zhou, Xindong Wu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages609-618
Number of pages10
ISBN (Electronic)9781509054725
DOIs
Publication statusPublished - 2 Jul 2016
Externally publishedYes
Event16th IEEE International Conference on Data Mining, ICDM 2016 - Barcelona, Catalonia, Spain
Duration: 12 Dec 201615 Dec 2016

Publication series

NameProceedings - IEEE International Conference on Data Mining, ICDM
Volume0
ISSN (Print)1550-4786

Conference

Conference16th IEEE International Conference on Data Mining, ICDM 2016
Country/TerritorySpain
CityBarcelona, Catalonia
Period12/12/1615/12/16

ASJC Scopus subject areas

  • General Engineering

Fingerprint

Dive into the research topics of 'Homophily, structure, and content augmented network representation learning'. Together they form a unique fingerprint.

Cite this