Graph neural networks (GNNs) have attracted extensive research attention in recent years
due to their capability to progress with graph data and have been widely used in practical …

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 …

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 …

Graph condensation, which reduces the size of a large-scale graph by synthesizing a small-
scale condensed graph as its substitution, has immediate benefits for various graph learning …

Federated learning is a new learning paradigm that decouples data collection and model
training via multi-party computation and model aggregation. As a flexible learning setting …

Federated learning shows a bright promise as a privacy-preserving collaborative learning
technique. However, prevalent solutions mainly focus on all private data sampled from the …

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

Personalized federated learning with differential privacy has been considered a feasible
solution to address non-IID distribution of data and privacy leakage risks. However, current …

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) …

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