Community detection in attributed graph is of great application value and many related
methods have been continually presented. However, existing methods for community …

With the rise of social media platforms for marketing purposes, the central dilemma for
researchers and policymakers lies in choosing effective data analysis tools to improve …

Recently emerged heterophilous graph neural networks have significantly reduced the
reliance on the assumption of graph homophily where linked nodes have similar features …

As a typical latent factor model, Matrix Factorization (MF) has demonstrated its great
effectiveness in recommender systems. Users and items are represented in a shared low …

In online lending services, fraud prediction is an especially critical step to control loss risk
and improve processing efficiency. Unfortunately, it is definitely challenging since the ex …

Gang fraud, the major and primary security issue in online lending services, can be
efficiently solved by the data-driven paradigm that is recognized as a promising solution for …

Much of scientific progress stems from previously published findings, but searching through
the vast sea of scientific publications is difficult. We often rely on metrics of scholarly …

Document network is a kind of intriguing dataset which provides both topical (texts) and
topological (links) information. Most previous work assumes that documents closely linked …

Discovering semantic coherent topics from the large amount of user-generated content
(UGC) in social media would facilitate many downstream applications of intelligent …

Topic models have many important applications in fields such as Natural Language
Processing. Topic embedding modelling aims at introducing word and topic embeddings …