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

Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …

Recently, the topic of community search (CS) has gained plenty of attention. Given a query
vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on …

A heterogeneous information network (HIN) is a graph model in which objects and edges
are annotated with types. Large and complex databases, such as YAGO and DBLP, can be …

Heterogeneous information networks usually contain different kinds of nodes and
distinguishing types of relations, which can preserve more information than homogeneous …

The graph embedding paradigm projects nodes of a graph into a vector space, which can
facilitate various downstream graph analysis tasks such as node classification and …

Most real-world data can be modeled as heterogeneous information networks (HINs)
consisting of vertices of multiple types and their relationships. Search for similar vertices of …

Default user detection plays one of the backbones in credit risk forecasting and
management. It aims at, given a set of corresponding features, eg, patterns extracted from …

Traditional fact checking by experts and analysts cannot keep pace with the volume of newly
created information. It is important and necessary, therefore, to enhance our ability to …

Given a knowledge base (KB), rule mining finds rules such as “If two people are married,
then they live (most likely) in the same place”. Due to the exponential search space, rule …