Graph anomaly detection via multi-scale contrastive learning networks with augmented view
Graph anomaly detection (GAD) is a vital task in graph-based machine learning and has
been widely applied in many real-world applications. The primary goal of GAD is to capture …
been widely applied in many real-world applications. The primary goal of GAD is to capture …
Graph embedding contrastive multi-modal representation learning for clustering
Multi-modal clustering (MMC) aims to explore complementary information from diverse
modalities for clustering performance facilitating. This article studies challenging problems in …
modalities for clustering performance facilitating. This article studies challenging problems in …
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 …
Attribute-missing graph clustering network
Deep clustering with attribute-missing graphs, where only a subset of nodes possesses
complete attributes while those of others are missing, is an important yet challenging topic in …
complete attributes while those of others are missing, is an important yet challenging topic in …
Hierarchically contrastive hard sample mining for graph self-supervised pretraining
Contrastive learning has recently emerged as a powerful technique for graph self-
supervised pretraining (GSP). By maximizing the mutual information (MI) between a positive …
supervised pretraining (GSP). By maximizing the mutual information (MI) between a positive …
Spatial-spectral graph contrastive clustering with hard sample mining for hyperspectral images
Hyperspectral image (HSI) clustering is a fundamental yet challenging task that groups
image pixels with similar features into distinct clusters. Among various approaches …
image pixels with similar features into distinct clusters. Among various approaches …
Self-consistent contrastive attributed graph clustering with pseudo-label prompt
Attributed graph clustering, which learns node representation from node attribute and
topological graph for clustering, is a fundamental and challenging task for multimedia …
topological graph for clustering, is a fundamental and challenging task for multimedia …
Incomplete graph learning via attribute-structure decoupled variational auto-encoder
Graph Neural Networks (GNNs) conventionally operate under the assumption that node
attributes are entirely observable. Their performance notably deteriorates when confronted …
attributes are entirely observable. Their performance notably deteriorates when confronted …
Rare: Robust masked graph autoencoder
Masked graph autoencoder (MGAE) has emerged as a promising self-supervised graph pre-
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …
training (SGP) paradigm due to its simplicity and effectiveness. However, existing efforts …
Multi-view graph imputation network
Graph data in the real world is often accompanied by the problem of missing attributes.
Recently, self-supervised graph representation learning, implementing data imputation …
Recently, self-supervised graph representation learning, implementing data imputation …