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

Graph neural networks (GNNs) based methods have achieved impressive performance on
node clustering task. However, they are designed on the homophilic assumption of graph …

Graph-level representations are critical in various real-world applications, such as predicting
the properties of molecules. However, in practice, precise graph annotations are generally …

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 …

Graph clustering has received significant attention in recent years due to the breakthrough of
graph neural networks (GNNs). However, GNNs frequently assume strong data homophily …

In many brain network studies, brain functional connectivity data is extracted from
neuroimaging data and then used for disease prediction. For now, brain disease data not …

Graphs are powerful representations for relations among objects, which have attracted
plenty of attention in both academia and industry. A fundamental challenge for graph …

In the era of User Generated Content (UGC), authors (IDs) of texts widely exist and play a
key role in determining the topic categories of texts. Existing text clustering efforts are mainly …

Meta-learning has been introduced in the recommendation domain, and a possible direction
to extend graph-based meta-learning is how to exploit higher-order information between …

In recent years, reconstructing features and learning node representations by graph
autoencoders (GAE) have attracted much attention in deep graph node clustering. However …