15 citations · 26 across the 11 of their papers we have counts for
11 papers
Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
Longfeng Wu, Bowen Lei, Dongkuan Xu +1
Rare categories abound in a number of real-world networks and play a pivotal role in a variety of high-stakes applications, including financial fraud detection, network intrusion d…
GPatcher: A Simple and Adaptive MLP Model for Alleviating Graph Heterophily
Shuaicheng Zhang, Haohui Wang, Si Zhang +1
While graph heterophily has been extensively studied in recent years, a fundamental research question largely remains nascent: How and to what extent will graph heterophily affect…
Mastering Long-Tail Complexity on Graphs: Characterization, Learning, and Generalization
Haohui Wang, Baoyu Jing, Kaize Ding +6
In the context of long-tail classification on graphs, the vast majority of existing work primarily revolves around the development of model debiasing strategies, intending to mitig…
EvoluNet: Advancing Dynamic Non-IID Transfer Learning on Graphs
Haohui Wang, Yuzhen Mao, Yujun Yan +8
Non-IID transfer learning on graphs is crucial in many high-stakes domains. The majority of existing works assume stationary distribution for both source and target domains. Howeve…
FairGen: Towards Fair Graph Generation
Lecheng Zheng, Dawei Zhou, Hanghang Tong +3
There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks,…
Augmenting Knowledge Transfer across Graphs
Yuzhen Mao, Jianhui Sun, Dawei Zhou
Given a resource-rich source graph and a resource-scarce target graph, how can we effectively transfer knowledge across graphs and ensure a good generalization performance? In many…
Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning
Linfeng Liu, Xu Han, Dawei Zhou +1
Subgraph similarity search, one of the core problems in graph search, concerns whether a target graph approximately contains a query graph. The problem is recently touched by neura…
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Dawei Zhou, Lecheng Zheng, Dongqi Fu +2
Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) witho…
Towards Defending against Adversarial Examples via Attack-Invariant Features
Dawei Zhou, Tongliang Liu, Bo Han +3
Deep neural networks (DNNs) are vulnerable to adversarial noise. Their adversarial robustness can be improved by exploiting adversarial examples. However, given the continuously ev…
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Dawei Zhou, Nannan Wang, Xinbo Gao +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. A range of adversarial defense techniques have been proposed to mitigate the interference of adversarial noise, amo…
Removing Adversarial Noise in Class Activation Feature Space
Dawei Zhou, Nannan Wang, Chunlei Peng +4
Deep neural networks (DNNs) are vulnerable to adversarial noise. Preprocessing based defenses could largely remove adversarial noise by processing inputs. However, they are typical…