7 citations · 12 across the 3 of their papers we have counts for
5 papers
Unleashing the Power of Emojis in Texts via Self-supervised Graph Pre-Training
Zhou Zhang, Dongzeng Tan, Jiaan Wang +2
Emojis have gained immense popularity on social platforms, serving as a common means to supplement or replace text. However, existing data mining approaches generally either comple…
Better with Less: A Data-Active Perspective on Pre-Training Graph Neural Networks
Jiarong Xu, Renhong Huang, Xin Jiang +4
Pre-training on graph neural networks (GNNs) aims to learn transferable knowledge for downstream tasks with unlabeled data, and it has recently become an active research area. The…
Graph Robustness Benchmark: Benchmarking the Adversarial Robustness of Graph Machine Learning
Qinkai Zheng, Xu Zou, Yuxiao Dong +5
Adversarial attacks on graphs have posed a major threat to the robustness of graph machine learning (GML) models. Naturally, there is an ever-escalating arms race between attackers…
Blindfolded Attackers Still Threatening: Strict Black-Box Adversarial Attacks on Graphs
Jiarong Xu, Yizhou Sun, Xin Jiang +4
Adversarial attacks on graphs have attracted considerable research interests. Existing works assume the attacker is either (partly) aware of the victim model, or able to send queri…
Unsupervised Adversarially-Robust Representation Learning on Graphs
Jiarong Xu, Yang Yang, Junru Chen +4
Unsupervised/self-supervised pre-training methods for graph representation learning have recently attracted increasing research interests, and they are shown to be able to generali…