Archaeoinformatics - Data Science

DAAD PPP with HKU: Motif Discovery in Heterogeneous Information Networks

HIN motifs

In collaboration with Prof. Dr. Reynold Cheng, HKU  (Hong Kong University).

The main goal of this project is to discover interesting relationships (or “motif”) between nodes in a large heterogeneous information network (HIN).  HINs, which
represent complex relationships and interactions among real-world entities, are ubiquitous in bibliographical networks, communication networks, the World Wide Web, and social networks. Recent methods for discovering the vast amount of knowledge contained in HINs has gained attention in computer science, social science, physics, and biology. An HIN provides not only a general, natural, and rich representation of relationships between objects, but also follows a schema that describes important information about it. Specifically, an HIN is a typed graph, whose nodes and edges are tagged with “type labels” to indicate their meanings. The motif, essentially a subgraph of an HIN that connects two entities defined on the HIN schema, reveals interesting relationship information about entities, and is important to many applications.

Research Objectives:

  • Develop effective motif discovery algorithms designed for heterogeneous information networks (HINs).
  • Design online and real-time solutions for motif discovery in HINs

For more details, please contact Prof. Dr. Matthias Renz, Christian Beth, M.Sc.