Detecting Outlier Patterns with Query-based Artificially Generated Searching Conditions

TL;DR

This paper presents a novel, query-based method for efficiently discovering and ranking network motifs in large heterogeneous networks by leveraging user queries and meta-path semantics.

Contribution

It introduces a new approach that uses user-defined queries and meta-paths to improve motif detection speed and relevance in complex networks.

Findings

Effective motif discovery in real-world networks

Robustness to different similarity measures

Faster detection through query constraints

Abstract

In the age of social computing, finding interesting network patterns or motifs is significant and critical for various areas such as decision intelligence, intrusion detection, medical diagnosis, social network analysis, fake news identification, national security, etc. However, sub-graph matching remains a computationally challenging problem, let alone identifying special motifs among them. This is especially the case in large heterogeneous real-world networks. In this work, we propose an efficient solution for discovering and ranking human behavior patterns based on network motifs by exploring a user's query in an intelligent way. Our method takes advantage of the semantics provided by a user's query, which in turn provides the mathematical constraint that is crucial for faster detection. We propose an approach to generate query conditions based on the user's query. In particular, we use meta paths between nodes to define target patterns as well as their similarities, leading to efficient motif discovery and ranking at the same time. The proposed method is examined on a real-world academic network, using different similarity measures between the nodes. The experiment result demonstrates that our method can identify interesting motifs, and is robust to the choice of similarity measures.