413 citations · 1k across the 7 of their papers we have counts for
9 papers
The Devil is in the Conflict: Disentangled Information Graph Neural Networks for Fraud Detection
Zhixun Li, Dingshuo Chen, Qiang Liu +1
Graph-based fraud detection has heretofore received considerable attention. Owning to the great success of Graph Neural Networks (GNNs), many approaches adopting GNNs for fraud det…
Adversarial Contrastive Learning for Evidence-aware Fake News Detection with Graph Neural Networks
Junfei Wu, Weizhi Xu, Qiang Liu +2
The prevalence and perniciousness of fake news have been a critical issue on the Internet, which stimulates the development of automatic fake news detection in turn. In this paper,…
Improving Molecular Pretraining with Complementary Featurizations
Yanqiao Zhu, Dingshuo Chen, Yuanqi Du +3
Molecular pretraining, which learns molecular representations over massive unlabeled data, has become a prominent paradigm to solve a variety of tasks in computational chemistry an…
STAN: Spatio-Temporal Attention Network for Next Location Recommendation
Yingtao Luo, Qiang Liu, Zhaocheng Liu
The next location recommendation is at the core of various location-based applications. Current state-of-the-art models have attempted to solve spatial sparsity with hierarchical g…
Graph Contrastive Learning with Adaptive Augmentation
Yanqiao Zhu, Yichen Xu, Feng Yu +3
Recently, contrastive learning (CL) has emerged as a successful method for unsupervised graph representation learning. Most graph CL methods first perform stochastic augmentation o…
Disentangled Item Representation for Recommender Systems
Zeyu Cui, Feng Yu, Shu Wu +2
Item representations in recommendation systems are expected to reveal the properties of items. Collaborative recommender methods usually represent an item as one single latent vect…