Visual Analysis of Heterogeneous Networks

Most real-life networks are not only massive but also highly heterogeneous, involving more than one type of entity, more than one type of relationship, and multiple attributes associated with both entities and relationships. Many of the existing methods and tools don't take into account these additional modalities that make networks highly heterogenous and complex-such methods and tools are mostly limited to dealing with much simpler networks (e.g., single-mode networks or bi-modal affiliation networks). In this paper we present a novel method for visual analysis of heterogenous networks. The method is based on a visual approach to analysis that allows the user to define complex structural patterns to query the network, and further explore the returned network patterns intuitively.