Representation Learning for Frequent Subgraph Mining

TL;DR

This paper introduces SPMiner, a neural network-based method that efficiently and accurately identifies frequent subgraph patterns in large networks, surpassing existing methods in speed and scalability, especially for larger motifs.

Contribution

The paper presents SPMiner, a novel neural approach combining graph neural networks and order embedding for scalable approximate frequent subgraph mining.

Findings

SPMiner nearly perfectly identifies 5-6 node motifs.

SPMiner is 100x faster than exact enumeration methods.

SPMiner reliably finds large 10-node and 20-node motifs with higher frequency than existing methods.

Abstract

Identifying frequent subgraphs, also called network motifs, is crucial in analyzing and predicting properties of real-world networks. However, finding large commonly-occurring motifs remains a challenging problem not only due to its NP-hard subroutine of subgraph counting, but also the exponential growth of the number of possible subgraphs patterns. Here we present Subgraph Pattern Miner (SPMiner), a novel neural approach for approximately finding frequent subgraphs in a large target graph. SPMiner combines graph neural networks, order embedding space, and an efficient search strategy to identify network subgraph patterns that appear most frequently in the target graph. SPMiner first decomposes the target graph into many overlapping subgraphs and then encodes each subgraph into an order embedding space. SPMiner then uses a monotonic walk in the order embedding space to identify frequent motifs. Compared to existing approaches and possible neural alternatives, SPMiner is more accurate, faster, and more scalable. For 5- and 6-node motifs, we show that SPMiner can almost perfectly identify the most frequent motifs while being 100x faster than exact enumeration methods. In addition, SPMiner can also reliably identify frequent 10-node motifs, which is well beyond the size limit of exact enumeration approaches. And last, we show that SPMiner can find large up to 20 node motifs with 10-100x higher frequency than those found by current approximate methods.