SubGraph2Vec: Highly-Vectorized Tree-likeSubgraph Counting

TL;DR

SubGraph2Vec introduces a highly efficient, vectorized algorithm for counting tree-like subgraphs in large networks, significantly outperforming previous methods and scalable across CPU and GPU architectures.

Contribution

It presents a novel vectorized algorithm for subgraph counting, with implementations that drastically improve performance and scalability for tree-like subgraphs.

Findings

Achieves up to 660x speedup over state-of-the-art on a single node.

Scales to template size 20 in distributed mode with good scalability.

Portability to both CPU and GPU architectures.

Abstract

Subgraph counting aims to count occurrences of a template T in a given network G(V, E). It is a powerful graph analysis tool and has found real-world applications in diverse domains. Scaling subgraph counting problems is known to be memory bounded and computationally challenging with exponential complexity. Although scalable parallel algorithms are known for several graph problems such as Triangle Counting and PageRank, this is not common for counting complex subgraphs. Here we address this challenge and study connected acyclic graphs or trees. We propose a novel vectorized subgraph counting algorithm, named Subgraph2Vec, as well as both shared memory and distributed implementations: 1) reducing algorithmic complexity by minimizing neighbor traversal; 2) achieving a highly-vectorized implementation upon linear algebra kernels to significantly improve performance and hardware utilization. 3) Subgraph2Vec improves the overall performance over the state-of-the-art work by orders of magnitude and up to 660x on a single node. 4) Subgraph2Vec in distributed mode can scale up the template size to 20 and maintain good strong scalability. 5) enabling portability to both CPU and GPU.