research-article

Heat kernel based community detection

Authors:

Kyle Kloster and

David F. GleichAuthors Info & Claims

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

Pages 1386 - 1395

https://doi.org/10.1145/2623330.2623706

Published: 24 August 2014 Publication History

Abstract

The heat kernel is a type of graph diffusion that, like the much-used personalized PageRank diffusion, is useful in identifying a community nearby a starting seed node. We present the first deterministic, local algorithm to compute this diffusion and use that algorithm to study the communities that it produces. Our algorithm is formally a relaxation method for solving a linear system to estimate the matrix exponential in a degree-weighted norm. We prove that this algorithm stays localized in a large graph and has a worst-case constant runtime that depends only on the parameters of the diffusion, not the size of the graph. On large graphs, our experiments indicate that the communities produced by this method have better conductance than those produced by PageRank, although they take slightly longer to compute. On a real-world community identification task, the heat kernel communities perform better than those from the PageRank diffusion.

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Cited By

Feng BCheng FLiu YChang XWang XJin D(2024)Community Detection on Social Networks With Sentimental InteractionInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.34123220:1(1-23)Online publication date: 27-Mar-2024
https://doi.org/10.4018/IJSWIS.341232
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Index Terms

Heat kernel based community detection
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Unsupervised learning
        Cluster analysis
2. Mathematics of computing
  1. Discrete mathematics
    1. Graph theory
      1. Graph algorithms

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cover image ACM Conferences

KDD '14: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2014

2028 pages

ISBN:9781450329569

DOI:10.1145/2623330

General Chairs:
Sofus Macskassy
Facebook
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Claudia Perlich
Dstillery
,
Program Chairs:
Jure Leskovec
Stanford University
,
Wei Wang
UCLA
,
Rayid Ghani
University of Chicago

Copyright © 2014 ACM.

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Published: 24 August 2014

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KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 24 - 27, 2014

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KDD '14 Paper Acceptance Rate 151 of 1,036 submissions, 15%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

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https://doi.org/10.4018/IJSWIS.341232
Ni LYe RLuo WZhang Y(2024)Local Community Detection in Multiple Private NetworksACM Transactions on Knowledge Discovery from Data10.1145/364407818:5(1-21)Online publication date: 10-Feb-2024
https://dl.acm.org/doi/10.1145/3644078
Huang YGleich DVeldt NChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Densest Subhypergraph: Negative Supermodular Functions and Strongly Localized MethodsProceedings of the ACM on Web Conference 202410.1145/3589334.3645624(881-892)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645624
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