Subgraph mining in a large graph: A review

Abstract

Large graphs are often used to simulate and model complex systems in variousresearch and application fields. Because of its importance, frequent subgraphmining (FSM) in single large graphs is a vital issue, and recently, it hasattracted numerous researchers, and played an important role in various tasksfor both research and application purposes. FSM is aimed at finding all sub-graphs whose number of appearances in a large graph is greater than or equalto a given frequency threshold. In most recent applications, the underlyinggraphs are very large, such as social networks, and therefore algorithms forFSM from a single large graph have been rapidly developed, but all of themhave NP-hard (nondeterministic polynomial time) complexity with huge sea-rch spaces, and therefore still need a lot of time and memory to restore andprocess. In this article, we present an overview of problems of FSM, importantphases in FSM, main groups of FSM, as well as surveying many modernapplied algorithms. This includes many practical applications and is a funda-mental premise for many studies in the future.

This article is categorized under: Algorithmic Development > Association Rules Algorithmic Development > Structure Discovery