Graph autoencoders (GAE) and variational graph autoencoders (VGAE) emerged as
powerful methods for link prediction. Their performances are less impressive on community …

Cancer is rarely the straightforward consequence of an abnormality in a single gene, but
rather reflects a complex interplay of many genes, represented as gene modules. Here, we …

A central issue in rumor surveillance and management is decoding the complex dynamics of
rumor propagation, with an emphasis on predicting diffusion cascades. Recent studies focus …

It has become a cardinal task to identify fake information (misinformation) on social media,
because it has significantly harmed the government and the public. There are many spam …

Fairness is a key issue in many real decision-making applications. Existing Graph Neural
Network (GNN) models, designed for making non-discrimination decisions, are dependent …

Text-enhanced and implicit reasoning methods are proposed for answering questions over
incomplete knowledge graph (KG), whereas prior studies either rely on external resources …

Hierarchy is an important and commonly observed topological property in real-world graphs
that indicate the relationships between supervisors and subordinates or the organizational …

In previous research, the prevailing assumption was that Graph Neural Networks (GNNs)
precisely depicted the interconnections among nodes within the graph's architecture …

Cancer is a complex disease driven by mutations in the genes that play critical roles in
cellular processes. The identification of cancer driver genes is crucial for understanding …

The team discovery task is concerned with finding a group of experts from a collaboration
network who would collectively cover a desirable set of skills. Most prior work for team …