Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Graph clustering, which aims to divide nodes in the graph into several distinct clusters, is a
fundamental yet challenging task. Benefiting from the powerful representation capability of …

Deep graph clustering has recently received significant attention due to its ability to enhance
the representation learning capabilities of models in unsupervised scenarios. Nevertheless …

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 …

Deep graph clustering, which aims to group nodes into disjoint clusters by neural networks
in an unsupervised manner, has attracted great attention in recent years. Although the …

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 …

Incomplete multi-view clustering (IMVC) methods typically encounter three drawbacks:(1)
intense time and/or space overheads;(2) intractable hyper-parameters;(3) non-zero variance …

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