Graph neural networks (GNNs) have shown their superiority in modeling graph data. Owing
to the advantages of federated learning, federated graph learning (FGL) enables clients to …

Graph-level anomaly detection (GLAD) aims to identify graphs that exhibit notable
dissimilarity compared to the majority in a collection. However, current works primarily focus …

Unsupervised graph representation learning (UGRL) has drawn increasing research
attention and achieved promising results in several graph analytic tasks. Relying on the …

Real-world graphs generally have only one kind of tendency in their connections. These
connections are either homophilic-prone or heterophily-prone. While graphs with homophily …

Federated Graph Learning is a privacy-preserving collaborative approach for training a
shared model on graph-structured data in the distributed environment. However, in real …

Federated learning (FL) is an effective machine learning paradigm where multiple clients
can train models based on heterogeneous data in a decentralized manner without …

Graph Neural Networks (GNNs) have exhibited impressive performance in many graph
learning tasks. Nevertheless, the performance of GNNs can deteriorate when the input …

Knowledge graphs (KGs), as a structured form of knowledge representation, have been
widely applied in the real world. Recently, few-shot knowledge graph completion (FKGC) …

Federated graph learning collaboratively learns a global graph neural network with
distributed graphs, where the non-independent and identically distributed property is one of …

While graph neural networks (GNNs) dominate the state-of-the-art for exploring graphs in
real-world applications, they have been shown to be vulnerable to a growing number of …