Efficient estimation of heat kernel pagerank for local clustering

Renchi Yang, Xiaokui Xiao, Zhewei Wei, Sourav S. Bhowmick, Jun Zhao, Rong Hua Li

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

14 Citations (Scopus)

Abstract

Given an undirected graph G and a seed node s, the local clustering problem aims to identify a high-quality cluster containing s in time roughly proportional to the size of the cluster, regardless of the size of G. This problem finds numerous applications on large-scale graphs. Recently, heat kernel PageRank (HKPR), which is a measure of the proximity of nodes in graphs, is applied to this problem and found to be more efficient compared with prior methods. However, existing solutions for computing HKPR either are prohibitively expensive or provide unsatisfactory error approximation on HKPR values, rendering them impractical especially on billion-edge graphs. In this paper, we present TEA and TEA+, two novel local graph clustering algorithms based on HKPR, to address the aforementioned limitations. Specifically, these algorithms provide non-trivial theoretical guarantees in relative error of HKPR values and the time complexity. The basic idea is to utilize deterministic graph traversal to produce a rough estimation of exact HKPR vector, and then exploit Monte-Carlo random walks to refine the results in an optimized and non-trivial way. In particular, TEA+ offers practical efficiency and effectiveness due to non-trivial optimizations. Extensive experiments on real-world datasets demonstrate that TEA+ outperforms the state-of-the-art algorithm by more than four times on most benchmark datasets in terms of computational time when achieving the same clustering quality, and in particular, is an order of magnitude faster on large graphs including the widely studied Twitter and Friendster datasets.

Original languageEnglish
Title of host publicationSIGMOD '19: Proceedings of the 2019 International Conference on Management of Data
PublisherAssociation for Computing Machinery (ACM)
Pages1339-1356
Number of pages18
ISBN (Print)9781450356435
DOIs
Publication statusPublished - 25 Jun 2019
EventACM SIGMOD International Conference on Management of Data, SIGMOD 2019 - Amsterdam, Netherlands
Duration: 30 Jun 20195 Jul 2019
https://dl.acm.org/doi/proceedings/10.1145/3299869

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078

Conference

ConferenceACM SIGMOD International Conference on Management of Data, SIGMOD 2019
Country/TerritoryNetherlands
CityAmsterdam
Period30/06/195/07/19
Internet address

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • Heat kernel PageRank
  • Local clustering

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