Deep graph clustering, which aims to group the nodes of a graph into disjoint clusters with
deep neural networks, has achieved promising progress in recent years. However, the …

Contrastive graph node clustering via learnable data augmentation is a hot research spot in
the field of unsupervised graph learning. The existing methods learn the sampling …

The problem of community-level information pathway prediction (CLIPP) aims at predicting
the transmission trajectory of content across online communities. A successful solution to …

Audiovisual data is everywhere in this digital age, which raises higher requirements for the
deep learning models developed on them. To well handle the information of the multi-modal …

Anchor-based multi-view graph clustering (AMVGC) has received abundant attention owing
to its high efficiency and the capability to capture complementary structural information …

Inductive relation reasoning for knowledge graphs, aiming to infer missing links between
brand-new entities, has drawn increasing attention. The models developed based on Graph …

Graph Neural Networks (GNNs) have achieved promising performance in semi-supervised
node classification in recent years. However, the problem of insufficient supervision …

Node classification is a popular and challenging task in graph neural networks, and existing
approaches are mainly developed for a single network. With the advances in domain …

Multi-View clustering has attracted broad attention due to its capacity to utilize consistent
and complementary information among views. Although tremendous progress has been …

Few-shot relation reasoning on knowledge graphs (FS-KGR) is an important and practical
problem that aims to infer long-tail relations and has drawn increasing attention these years …