Mining Frequent Neighborhood Patterns in Large Labeled Graphs

TL;DR

This paper introduces a novel approach for mining frequent neighborhood patterns in large labeled graphs, addressing the limitations of traditional subgraph support measures in single-graph databases.

Contribution

It proposes shifting from subgraph to neighborhood pattern mining to preserve the downward-closure property and semantic interpretability in single-graph scenarios.

Findings

Algorithms are feasible on large real-life graphs.

Mining reveals new, interesting knowledge.

Supports efficient pattern discovery in single-graph databases.

Abstract

Over the years, frequent subgraphs have been an important sort of targeted patterns in the pattern mining literatures, where most works deal with databases holding a number of graph transactions, e.g., chemical structures of compounds. These methods rely heavily on the downward-closure property (DCP) of the support measure to ensure an efficient pruning of the candidate patterns. When switching to the emerging scenario of single-graph databases such as Google Knowledge Graph and Facebook social graph, the traditional support measure turns out to be trivial (either 0 or 1). However, to the best of our knowledge, all attempts to redefine a single-graph support resulted in measures that either lose DCP, or are no longer semantically intuitive. This paper targets mining patterns in the single-graph setting. We resolve the "DCP-intuitiveness" dilemma by shifting the mining target from frequent subgraphs to frequent neighborhoods. A neighborhood is a specific topological pattern where a vertex is embedded, and the pattern is frequent if it is shared by a large portion (above a given threshold) of vertices. We show that the new patterns not only maintain DCP, but also have equally significant semantics as subgraph patterns. Experiments on real-life datasets display the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered in prior works.