With the widespread use of information technologies, information networks are becoming
increasingly popular to capture complex relationships across various disciplines, such as …

Attributed networks are ubiquitous and form a critical component of modern information
infrastructure, where additional node attributes complement the raw network structure in …

Existing embedding methods for attributed networks aim at learning low-dimensional vector
representations for nodes only but not for both nodes and attributes, resulting in the fact that …

Performing anomaly detection on attributed networks concerns with finding nodes whose
patterns or behaviors deviate significantly from the majority of reference nodes. Its success …

Representation learning in heterogeneous networks faces challenges due to
heterogeneous structural information of multiple types of nodes and relations, and also due …

Random walks are widely adopted in various network analysis tasks ranging from network
embedding to label propagation. It could capture and convert geometric structures into …

Networks have been widely used as the data structure for abstracting real-world systems as
well as organizing the relations among entities. Network embedding models are powerful …

Anomaly detection on attributed networks is widely used in online shopping, financial
transactions, communication networks, and so on. However, most existing works trying to …

Many recent network embedding algorithms use negative sampling (NS) to approximate a
variant of the computationally expensive Skip-Gram neural network architecture (SGA) …

Deep generative models (DGMs) have achieved remarkable advances. Semi-supervised
variational auto-encoders (SVAE) as a classical DGM offers a principled framework to …