A versatile framework for attributed network clustering via K-nearest neighbor augmentation

Yiran Li, Gongyao Guo, Jieming Shi*, Renchi Yang, Shiqi Shen, Qing Li, Jun Luo

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

Abstract

Attributed networks containing entity-specific information in node attributes are ubiquitous in modeling social networks, e-commerce, bioinformatics, etc. Their inherent network topology ranges from simple graphs to hypergraphs with high-order interactions and multiplex graphs with separate layers. An important graph mining task is node clustering, aiming to partition the nodes of an attributed network into k disjoint clusters such that intra-cluster nodes are closely connected and share similar attributes, while inter-cluster nodes are far apart and dissimilar. It is highly challenging to capture multi-hop connections via nodes or attributes for effective clustering on multiple types of attributed networks. In this paper, we first present AHCKA as an efficient approach to attributed hypergraph clustering (AHC). AHCKA includes a carefully-crafted K-nearest neighbor augmentation strategy for the optimized exploitation of attribute information on hypergraphs, a joint hypergraph random walk model to devise an effective AHC objective, and an efficient solver with speedup techniques for the objective optimization. The proposed techniques are extensible to various types of attributed networks, and thus, we develop ANCKA as a versatile attributed network clustering framework, capable of attributed graph clustering, attributed multiplex graph clustering, and AHC. Moreover, we devise ANCKA-GPU with algorithmic designs tailored for GPU acceleration to boost efficiency. We have conducted extensive experiments to compare our methods with 19 competitors on 8 attributed hypergraphs, 16 competitors on 6 attributed graphs, and 16 competitors on 3 attributed multiplex graphs, all demonstrating the superb clustering quality and efficiency of our methods.

Original languageEnglish
Pages (from-to)1913-1943
Number of pages31
JournalVLDB Journal
Volume33
Issue number6
Early online date16 Sept 2024
DOIs
Publication statusPublished - Nov 2024

Scopus Subject Areas

  • Information Systems
  • Hardware and Architecture

User-Defined Keywords

  • Attributed Graph
  • Clustering
  • GPU Computing
  • KNN
  • Random Walks

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