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 …

Federated learning has emerged as a promising paradigm for privacy-preserving
collaboration among different parties. Recently, with the popularity of federated learning, an …

Personalized Federated Graph Learning (pFGL) facilitates the decentralized training of
Graph Neural Networks (GNNs) without compromising privacy while accommodating …

Graph Neural Networks (GNNs) are widely employed to derive meaningful node
representations from graphs. Despite their success, deep GNNs frequently grapple with the …

Effective epidemic forecasting is critical for public health strategies and efficient medical
resource allocation, especially in the face of rapidly spreading infectious diseases. However …

Enhancing the reasoning capabilities of large language models (LLMs) remains a key
challenge, especially for tasks that require complex, multi-step decision-making. Humans …

While tokenized graph Transformers have demonstrated strong performance in node
classification tasks, their reliance on a limited subset of nodes with high similarity scores for …

Training high-quality deep models necessitates vast amounts of data, resulting in
overwhelming computational and memory demands. Recently, data pruning, distillation, and …

Spectral Graph Neural Networks effectively handle graphs with different homophily levels,
with low-pass filter mining feature smoothness and high-pass filter capturing differences …