21 citations · 49 across the 8 of their papers we have counts for
10 papers
An Empirical Study of Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Qiang Liu +1
Graph Contrastive Learning (GCL) establishes a new paradigm for learning graph representations without human annotations. Although remarkable progress has been witnessed recently,…
Relation-aware Heterogeneous Graph for User Profiling
Qilong Yan, Yufeng Zhang, Qiang Liu +2
User profiling has long been an important problem that investigates user interests in many real applications. Some recent works regard users and their interacted objects as entitie…
Structure-Aware Hard Negative Mining for Heterogeneous Graph Contrastive Learning
Yanqiao Zhu, Yichen Xu, Hejie Cui +3
Recently, heterogeneous Graph Neural Networks (GNNs) have become a de facto model for analyzing HGs, while most of them rely on a relative large number of labeled data. In this wor…
DyGCN: Dynamic Graph Embedding with Graph Convolutional Network
Zeyu Cui, Zekun Li, Shu Wu +4
Graph embedding, aiming to learn low-dimensional representations (aka. embeddings) of nodes, has received significant attention recently. Recent years have witnessed a surge of eff…
Mining Latent Structures for Multimedia Recommendation
Jinghao Zhang, Yanqiao Zhu, Qiang Liu +3
Multimedia content is of predominance in the modern Web era. Investigating how users interact with multimodal items is a continuing concern within the rapid development of recommen…
Disentangled Self-Attentive Neural Networks for Click-Through Rate Prediction
Yichen Xu, Yanqiao Zhu, Feng Yu +2
Click-Through Rate (CTR) prediction, whose aim is to predict the probability of whether a user will click on an item, is an essential task for many online applications. Due to the…