Community Detection in Networks with Node Attributes

@article{Yang2013CommunityDI,
  title={Community Detection in Networks with Node Attributes},
  author={Jaewon Yang and Julian J. McAuley and Jure Leskovec},
  journal={2013 IEEE 13th International Conference on Data Mining},
  year={2013},
  pages={1151-1156}
}
Community detection algorithms are fundamental tools that allow us to uncover organizational principles in networks. When detecting communities, there are two possible sources of information one can use: the network structure, and the features and attributes of nodes. Even though communities form around nodes that have common edges and common attributes, typically, algorithms have only focused on one of these two data modalities: community detection algorithms traditionally focus only on the… CONTINUE READING
Highly Influential
This paper has highly influenced 38 other papers. REVIEW HIGHLY INFLUENTIAL CITATIONS
Highly Cited
This paper has 413 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 18 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.

Citations

Publications citing this paper.
Showing 1-10 of 252 extracted citations

Discriminative Link Prediction using Local, Community, and Global Signals

IEEE Transactions on Knowledge and Data Engineering • 2016
View 9 Excerpts
Highly Influenced

Online social network analysis: detection of communities of interest

Journal of Intelligent Information Systems • 2018
View 5 Excerpts
Highly Influenced

Interpretable Probabilistic Divisive Clustering of Large Node-Attributed Networks

2017 European Intelligence and Security Informatics Conference (EISIC) • 2017
View 5 Excerpts
Highly Influenced

414 Citations

050100'14'16'18
Citations per Year
Semantic Scholar estimates that this publication has 414 citations based on the available data.

See our FAQ for additional information.

References

Publications referenced by this paper.
Showing 1-10 of 47 references

Block-LDA: Jointly Modeling Entity-Annotated Text and Entity-Entity Links

Handbook of Mixed Membership Models and Their Applications • 2014
View 12 Excerpts
Highly Influenced

Relational Topic Models for Document Networks

AISTATS • 2009
View 4 Excerpts
Highly Influenced

Similar Papers

Loading similar papers…