479 citations · 502 across the 4 of their papers we have counts for
5 papers
Rethinking the Setting of Semi-supervised Learning on Graphs
Ziang Li, Ming Ding, Weikai Li +4
We argue that the present setting of semisupervised learning on graphs may result in unfair comparisons, due to its potential risk of over-tuning hyper-parameters for models. In th…
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…
Controllable Multi-Interest Framework for Recommendation
Yukuo Cen, Jianwei Zhang, Xu Zou +3
Recently, neural networks have been widely used in e-commerce recommender systems, owing to the rapid development of deep learning. We formalize the recommender system as a sequent…
Towards Knowledge-Based Recommender Dialog System
Qibin Chen, Junyang Lin, Yichang Zhang +4
In this paper, we propose a novel end-to-end framework called KBRD, which stands for Knowledge-Based Recommender Dialog System. It integrates the recommender system and the dialog…
Representation Learning for Attributed Multiplex Heterogeneous Network
Yukuo Cen, Xu Zou, Jianwei Zhang +3
Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and ca…