Community Detection in Networks with Node Attributes
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BibTeX
@MISC{Yang_communitydetection,
author = {Jaewon Yang and Julian Mcauley and Jure Leskovec},
title = {Community Detection in Networks with Node Attributes},
year = {}
}
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Abstract
Abstract—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 network structure, while clustering algorithms mostly consider only node attributes. In this paper, we develop Com-munities from Edge Structure and Node Attributes (CESNA), an accurate and scalable algorithm for detecting overlapping communities in networks with node attributes. CESNA statis-tically models the interaction between the network structure and the node attributes, which leads to more accurate community detection as well as improved robustness in the presence of noise in the network structure. CESNA has a linear runtime in the network size and is able to process networks an order of magnitude larger than comparable approaches. Last, CESNA also helps with the interpretation of detected communities by finding relevant node attributes for each community. I.
Keyphrases
node attribute community detection network structure data modality linear runtime common edge network size organizational principle fundamental tool abstract community detection algorithm comparable approach edge structure scalable algorithm relevant node attribute detected community accurate community detection common attribute possible source
