BFS based distributed algorithm for parallel local directed sub-graph enumeration

TL;DR

This paper introduces VDMC, a fully distributed, GPU-optimized algorithm for counting directed sub-graph motifs around each vertex in large graphs, enabling detailed local structure analysis at unprecedented scale.

Contribution

The paper presents VDMC, a novel distributed algorithm for efficiently counting directed sub-graph motifs per vertex in large graphs, addressing a gap in existing methods.

Findings

VDMC accurately estimates motif counts compared to analytical models.

VDMC is highly efficient on CPU and GPU for large-scale graphs.

The method enables detailed local graph structure analysis in graphs with millions of edges.

Abstract

Estimating the frequency of sub-graphs is of importance for many tasks, including sub-graph isomorphism, kernel-based anomaly detection, and network structure analysis. While multiple algorithms were proposed for full enumeration or sampling-based estimates, these methods fail in very large graphs. Recent advances in parallelization allow for estimates of total sub-graphs counts in very large graphs. The task of counting the frequency of each sub-graph associated with each vertex also received excellent solutions for undirected graphs. However, there is currently no good solution for very large directed graphs. We here propose VDMC (Vertex specific Distributed Motif Counting) -- a fully distributed algorithm to optimally count all the 3 and 4 vertices connected directed graphs (sub-graph motifs) associated with each vertex of a graph. VDMC counts each motif only once and its efficacy is linear in the number of counted motifs. It is fully parallelized to be efficient in GPU-based computation. VDMC is based on three main elements: 1) Ordering the vertices and only counting motifs containing increasing order vertices, 2) sub-ordering motifs based on the average length of the BFS composing the motif, and 3) removing isomorphisms only once for the entire graph. We here compare VDMC to analytical estimates of the expected number of motifs and show its accuracy. VDMC is available as a highly efficient CPU and GPU code with a novel data structure for efficient graph manipulation. We show the efficacy of VDMC and real-world graphs. VDMC allows for the precise analysis of sub-graph frequency around each vertex in large graphs and opens the way for the extension of methods until now limited to graphs of thousands of edges to graphs with millions of edges and above. GIT: https://github.com/louzounlab/graph-measures