Homophily principle,\ie {} nodes with the same labels or similar attributes are more likely to
be connected, has been commonly believed to be the main reason for the superiority of …

Graph neural networks (GNNs) are models that capture the dependencies between graph
data by passing messages between graph nodes and they have been widely used to …

Graph-structured data exhibits universality and widespread applicability across diverse
domains, such as social network analysis, biochemistry, financial fraud detection, and …

Graph Neural Networks (GNNs) have established themselves as state-of-the-art for many
machine learning applications such as the analysis of social and medical networks. Several …

Graph Neural Networks (GNNs) have gained significant attention owing to their ability to
handle graph-structured data and the improvement in practical applications. However, many …

Graph convolutional networks (GCNs) have been attracting widespread attentions due to
their encouraging performance and powerful generalizations. However, few work provide a …

With the ever-growing interest in modeling complex graph structures, graph neural networks
(GNN) provide a generalized form of exploiting non-Euclidean space data. However, the …

Graphs are structured data that models complex relations between real-world entities.
Heterophilic graphs, where linked nodes are prone to be with different labels or dissimilar …

Learning graph embeddings is a crucial task in graph mining tasks. An effective graph
embedding model can learn low-dimensional representations from graph-structured data for …

Recommender systems can be privacy-sensitive. To protect users' private historical
interactions, federated learning has been proposed in distributed learning for user …