Empirical Comparison of Algorithms for Network Community Detection
Jure Leskovec, Kevin J. Lang, Michael W. Mahoney

TL;DR
This paper empirically compares various algorithms for network community detection, analyzing their performance and biases across different objective functions and community sizes in large real-world graphs.
Contribution
It provides a systematic comparison of community detection algorithms, evaluating their effectiveness and biases across multiple objective functions and community sizes.
Findings
Different algorithms exhibit size-dependent biases.
Objective functions influence the quality of detected communities.
Algorithms vary in their ability to optimize community quality across sizes.
Abstract
Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest. In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Mobile Crowdsensing and Crowdsourcing
