  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.
