Detecting a community in a network is a matter of discerning the distinct features and
connections of a group of members that are different from those in other communities. The …

Masked Autoencoders learn strong visual representations and achieve state-of-the-art
results in several independent modalities, yet very few works have addressed their …

Contrastive deep graph clustering, which aims to divide nodes into disjoint groups via
contrastive mechanisms, is a challenging research spot. Among the recent works, hard …

Benefiting from the intrinsic supervision information exploitation capability, contrastive
learning has achieved promising performance in the field of deep graph clustering recently …

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 learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …

Online class-incremental continual learning aims to learn new classes continually from a
never-ending and single-pass data stream, while not forgetting the learned knowledge of old …

Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …

Contrastive Learning (CL) has emerged as a dominant technique for unsupervised
representation learning which embeds augmented versions of the anchor close to each …

Abstract Compositional Zero-Shot Learning (CZSL) aims to recognize unseen compositions
formed from seen state and object during training. Since the same state may be various in …