Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …

Most objects and data in the real world are of multiple types, interconnected, forming
complex, heterogeneous but often semi-structured information networks. However, most …

We witness an unprecedented proliferation of knowledge graphs that record millions of
entities and their relationships. While knowledge graphs are structure-flexible and content …

Social networks continue to grow in size and the type of information hosted. We witness a
growing interest in clustering a social network of people based on both their social …

Information networks that can be extracted from many domains are widely studied recently.
Different functions for mining these networks are proposed and developed, such as ranking …

Recent studies have demonstrated the power of modeling real world data as heterogeneous
information networks (HINs) consisting of multiple types of entities and relations …

Multi-label classification of heterogeneous information networks has received renewed
attention in social network analysis. In this paper, we present an activity-edge centric multi …

In real-world graphs such as social networks, Semantic Web and biological networks, each
vertex usually contains rich information, which can be modeled by a set of tokens or …

Social influence analysis has shown great potential for strategic marketing decision. It is well
known that people influence one another based on both their social connections and the …

Utilizing graph analysis models and algorithms to exploit complex interactions over a
network of entities is emerging as an attractive network analytic technology. In this paper, we …