40 citations · 89 across the 42 of their papers we have counts for
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Transductive Linear Probing: A Novel Framework for Few-Shot Node Classification
Zhen Tan, Song Wang, Kaize Ding +2
Few-shot node classification is tasked to provide accurate predictions for nodes from novel classes with only few representative labeled nodes. This problem has drawn tremendous at…
Interpreting Unfairness in Graph Neural Networks via Training Node Attribution
Yushun Dong, Song Wang, Jing Ma +2
Graph Neural Networks (GNNs) have emerged as the leading paradigm for solving graph analytical problems in various real-world applications. Nevertheless, GNNs could potentially ren…
Graph Few-shot Learning with Task-specific Structures
Song Wang, Chen Chen, Jundong Li
Graph few-shot learning is of great importance among various graph learning tasks. Under the few-shot scenario, models are often required to conduct classification given limited la…
On Structural Explanation of Bias in Graph Neural Networks
Yushun Dong, Song Wang, Yu Wang +2
Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the \emph{de facto} solution in a variety of decision-m…
Task-Adaptive Few-shot Node Classification
Song Wang, Kaize Ding, Chuxu Zhang +2
Node classification is of great importance among various graph mining tasks. In practice, real-world graphs generally follow the long-tail distribution, where a large number of cla…
FAITH: Few-Shot Graph Classification with Hierarchical Task Graphs
Song Wang, Yushun Dong, Xiao Huang +2
Few-shot graph classification aims at predicting classes for graphs, given limited labeled graphs for each class. To tackle the bottleneck of label scarcity, recent works propose t…
Fairness in Graph Mining: A Survey
Yushun Dong, Jing Ma, Song Wang +2
Graph mining algorithms have been playing a significant role in myriad fields over the years. However, despite their promising performance on various graph analytical tasks, most o…