Efficient and scalable labeled subgraph matching using SGMatch

Graphs are natural candidates for modeling application domains, such as social networks, pattern recognition, citation networks, or protein–protein interactions. One of the most challenging tasks in managing graphs is subgraph matching over data graphs, which attempts to find one-to-one correspondences, called solutions, among the query and data nodes. To compute solutions, most contemporary techniques use backtracking and recursion. An open research question is whether graphs can be matched based on parts and local solutions can be combined to reach a global matching. In this paper, we present an approach based on graph decomposition called SGMatch to match graphs. We represent graphs in smaller units called graphlets and develop a matching technique to leverage this representation. Pruning strategies use a new notion of edge covering called minimum hub cover and metadata, such as statistics and inverted indices, to reduce the number of matching candidates. Our evaluation of SGMatch versus contemporary algorithms, i.e., VF2, GraphQL, QuickSI, GADDI, or SPath, shows that SGMatch substantially improves the performance of current state-of-the-art techniques for larger query graphs with different structures, i.e., cliques, paths or subgraphs.