Efficient Streaming Subgraph Isomorphism with Graph NeuralNetworks

Abstract

Queries to detect isomorphic subgraphs are important in graphbased data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do not scale to a dynamic setting of a continuous stream of queries. In this paper, we address the scalability challenges induced by a stream of subgraph isomorphism queries by caching and re-use of previous results. We first present a novel subgraph index based on graph embeddings that serves as the foundation for efficient stream processing. It enables not only effective caching and re-use of results, but also speeds-up traditional algorithms for subgraph isomorphism in case of cache misses. Moreover, we propose cache management policies that incorporate notions of reusability of query results. Experiments using real-world datasets demonstrate the effectiveness of our approach in handling isomorphic subgraph search for streams of queries.