Few-shot Node Classification (FSNC) is a challenge in graph representation learning where
only a few labeled nodes per class are available for training. To tackle this issue, meta …

Federated Learning (FL) enables multiple clients to collaboratively learn a machine learning
model without exchanging their own local data. In this way, the server can exploit the …

Graphs, as a fundamental data structure, have proven efficacy in modeling complex
relationships between objects and are therefore found in wide web applications. Graph …

Few-shot node classification, which aims to predict labels for nodes on graphs with only
limited labeled nodes as references, is of great significance in real-world graph mining …

Few-shot node classification aims at classifying nodes with limited labeled nodes as
references. Recent few-shot node classification methods typically learn from classes with …

Inspired by the success of foundation models in applications such as ChatGPT, as graph
data has been ubiquitous, one can envision the far-reaching impacts that can be brought by …

Inductive relation prediction, which handles unseen entities at the reasoning stage, has the
potential to complete continuously expanding knowledge graphs. Existing inductive …

Although Graph Neural Networks (GNNs) have been successful in node classification tasks,
their performance heavily relies on the availability of a sufficient number of labeled nodes …

Considering the extreme class imbalance in real-world graphs, increasing attention has
been paid to Few-Shot Node Classification (FSNC). However, existing methods in traditional …

Abstract Graph Neural Networks (GNNs) have shown superior performance in node
classification. However, GNNs perform poorly in the Few-Shot Node Classification (FSNC) …