Parallel Pivoted Subgraph Filtering with Partial Coding Trees on GPU

The pivoted subgraph isomorphism problem is a special subgraph isomorphism problem that focuses on the pivoted nodes rather than the entire subgraphs. The key challenge in adapting existing techniques to the pivoted problem is eliminating their redundant intermediate results. In this paper, we propose a GPU-based pivoted subgraph isomorphism filtering technique, where information of each node is encoded into a series of codes. When performing a pivot subgraph search, the candidate nodes satisfying the coding requirements are collected parallelly on GPU while others are filtered away. Then the final result can be effectively retrieved by a verification process on the filtered nodes. As demonstrated by the experimental results, our method dramatically reduces the processing time of the pivoted subgraph isomorphism problem. Compared to the state-of-the-art GPU-friendly subgraph matching method GpSM which also focuses on filtering effect, the algorithm’s execution time is halved, confirming that our approach can effectively process pivoted subgraph isomorphism queries.