2020/08/05 by Yizhang He, He, Yizhang, Kai Wang +7 · 1 citation
Computer Science · Physics and Astronomy · #Advanced Graph Neural Networks #Caching and Content Delivery #Complex Network Analysis Techniques #Databases (cs.DB) #FOS: Computer and information sciences #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2008.04054
openalex publication_date 2020/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We propose a novel cohesive subgraph model called τ-strengthened (α,β)-core (denoted as (α,β)τ-core), which is the first to consider both tie strength and vertex engagement on bipartite graphs. An edge is a strong tie if contained in at least τ butterflies (2×2-bicliques). (α,β)τ-core requires each vertex on the upper or lower level to have at least α or β strong ties, given strength level τ. To retrieve the vertices of (α,β)τ-core optimally, we construct index Iα,β,τ to store all (α,β)τ-cores. Effective optimization techniques are proposed to improve index construction. To make our idea practical on large graphs, we propose 2D-indexes Iα,β, Iβ,τ, and Iα,τ that selectively store the vertices of (α,β)τ-core for some α,β, and τ. The 2D-indexes are more space-efficient and require less construction time, each of which can support (α,β)τ-core queries. As query efficiency depends on input parameters and the choice of 2D-index, we propose a learning-based hybrid computation paradigm by training a feed-forward neural network to predict the optimal choice of 2D-index that minimizes the query time. Extensive experiments show that (1) (α,β)τ-core is an effective model capturing unique and important cohesive subgraphs; (2) the proposed techniques significantly improve the efficiency of index construction and query processing.