3 citations · 4 across the 14 of their papers we have counts for
5 papers · 1 filter
Does Homophily Help in Robust Test-time Node Classification?
Yan Jiang, Ruihong Qiu, Zi Huang
Homophily, the tendency of nodes from the same class to connect, is a fundamental property of real-world graphs, underpinning structural and semantic patterns in domains such as ci…
ALSA: Anchors in Logit Space for Out-of-Distribution Accuracy Estimation
Chenzhi Liu, Mahsa Baktashmotlagh, Yanran Tang +2
Estimating model accuracy on unseen, unlabeled datasets is crucial for real-world machine learning applications, especially under distribution shifts that can degrade performance.…
GCondenser: Benchmarking Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
Large-scale graphs are valuable for graph representation learning, yet the abundant data in these graphs hinders the efficiency of the training process. Graph condensation (GC) all…
PUMA: Efficient Continual Graph Learning for Node Classification with Graph Condensation
Yilun Liu, Ruihong Qiu, Yanran Tang +2
When handling streaming graphs, existing graph representation learning models encounter a catastrophic forgetting problem, where previously learned knowledge of these models is eas…
CaT: Balanced Continual Graph Learning with Graph Condensation
Yilun Liu, Ruihong Qiu, Zi Huang
Continual graph learning (CGL) is purposed to continuously update a graph model with graph data being fed in a streaming manner. Since the model easily forgets previously learned k…