Simple contrastive graph clustering
Contrastive learning has recently attracted plenty of attention in deep graph clustering due to
its promising performance. However, complicated data augmentations and time-consuming …
its promising performance. However, complicated data augmentations and time-consuming …
Dink-net: Neural clustering on large graphs
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 …
deep neural networks, has achieved promising progress in recent years. However, the …
Learn from relational correlations and periodic events for temporal knowledge graph reasoning
Reasoning on temporal knowledge graphs (TKGR), aiming to infer missing events along the
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
timeline, has been widely studied to alleviate incompleteness issues in TKG, which is …
Substructure aware graph neural networks
Despite the great achievements of Graph Neural Networks (GNNs) in graph learning,
conventional GNNs struggle to break through the upper limit of the expressiveness of first …
conventional GNNs struggle to break through the upper limit of the expressiveness of first …
Knowledge graph contrastive learning based on relation-symmetrical structure
Knowledge graph embedding (KGE) aims at learning powerful representations to benefit
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …
various artificial intelligence applications. Meanwhile, contrastive learning has been widely …
Deep incomplete multi-view clustering with cross-view partial sample and prototype alignment
The success of existing multi-view clustering relies on the assumption of sample integrity
across multiple views. However, in real-world scenarios, samples of multi-view are partially …
across multiple views. However, in real-world scenarios, samples of multi-view are partially …
Mole-bert: Rethinking pre-training graph neural networks for molecules
Recent years have witnessed the prosperity of pre-training graph neural networks (GNNs)
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …
for molecules. Typically, atom types as node attributes are randomly masked and GNNs are …
Dealmvc: Dual contrastive calibration for multi-view clustering
Benefiting from the strong view-consistent information mining capacity, multi-view
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
contrastive clustering has attracted plenty of attention in recent years. However, we observe …
A Survey of Deep Graph Clustering: Taxonomy, Challenge, Application, and Open Resource
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 …
fundamental yet challenging task. Benefiting from the powerful representation capability of …
Convert: Contrastive graph clustering with reliable augmentation
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 field of unsupervised graph learning. The existing methods learn the sampling …