Distributed Algorithm for High-Utility Subgraph Pattern Mining Over Big Data Platforms

Abstract

Frequent subgraph pattern mining (FSM) finds subgraph patterns that occur in a graph database with a frequency that is more than a given threshold. In FSM, the notion of occurrence captures the presence or absence of a node and an edge in a binary fashion and considers relevance of each edge or node as same. However, an edge or a node may have different relevancy score. Therefore, the utility of a pattern should be defined using the relevance score of participating edges or nodes. This paper defines the utility notion of a pattern using this idea and presents algorithms to mine high-utility patterns from a given graph database. A significant issue in high-utility pattern mining is that the antimonotonic property no longer holds contrary to the FSM. Hence pruning of the search space becomes a daunting task. To address this issue, we incorporate a function to estimate an upper-bound utility of a pattern object that also satisfies the anti-monotonic property. This paper presents three optimization heuristics for the solution on a distributed platform, namely, a novel use of bloom filter to avoid exploration of non-candidates, avoidance of sending database information with each pattern, and avoidance of sending pattern embeddings with each pattern. The experimental study on Apache Spark shows the effectiveness of our proposed optimization strategies.