Scalable probabilistic truss decomposition using central limit theorem and H-index.

ABSTRACT: Truss decomposition is a popular notion of hierarchical dense substructures in graphs. In a nutshell, k-truss is the largest subgraph in which every edge is contained in at least k triangles. Truss decomposition aims to compute k-trusses for each possible value of k. There are many works that study truss decomposition in deterministic graphs. However, in probabilistic graphs, truss decomposition is significantly more challenging and has received much less attention; state-of-the-art approaches do not scale well to large probabilistic graphs. Finding the tail probabilities of the number of triangles that contain each edge is a critical challenge of those approaches. This is achieved using dynamic programming which has quadratic run-time and thus not scalable to r

SUBMITTER: Esfahani F 

PROVIDER: S-EPMC9310023 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature