Effective and Scalable Clustering on Massive Attributed Graphs
Renchi Yang, J. Y. Shi, Yin Yang, Keke Huang, Shiqi Zhang, Xiaokui Xiao
Abstract
Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nodes in G into k disjoint clusters, such that nodes within the same cluster share similar topological and attribute characteristics, while those in different clusters are dissimilar. This problem is challenging on massive graphs, e.g., with millions of nodes and billions of attribute values. For such graphs, existing solutions either incur prohibitively high costs, or produce clustering results with compromised quality.
Topics & Concepts
Cluster analysisComputer scienceInitializationScalabilityDisjoint setsGraphSolverTheoretical computer scienceData miningAlgorithmMathematicsCombinatoricsArtificial intelligenceProgramming languageDatabaseComplex Network Analysis TechniquesAdvanced Graph Neural NetworksAdvanced Clustering Algorithms Research