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Yanqiao Zhu
Researcher at Chinese Academy of Sciences
Publications - 52
Citations - 2580
Yanqiao Zhu is an academic researcher from Chinese Academy of Sciences. The author has contributed to research in topics: Computer science & Graph (abstract data type). The author has an hindex of 12, co-authored 33 publications receiving 805 citations. Previous affiliations of Yanqiao Zhu include Tongji University & Association for Computing Machinery.
Papers
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Journal ArticleDOI
Session-Based Recommendation with Graph Neural Networks
TL;DR: Wang et al. as discussed by the authors proposed Session-based Recommendation with Graph Neural Networks (SR-GNN) to capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
Proceedings ArticleDOI
Graph Contrastive Learning with Adaptive Augmentation
TL;DR: This paper proposes a novel graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph that consistently outperforms existing state-of-the-art baselines and even surpasses some supervised counterparts.
Posted Content
Deep Graph Contrastive Representation Learning.
TL;DR: This paper proposes a novel framework for unsupervised graph representation learning by leveraging a contrastive objective at the node level, and generates two graph views by corruption and learns node representations by maximizing the agreement of node representations in these two views.
Proceedings ArticleDOI
Graph Contrastive Learning with Adaptive Augmentation
TL;DR: Wang et al. as mentioned in this paper proposed a graph contrastive representation learning method with adaptive augmentation that incorporates various priors for topological and semantic aspects of the graph to improve the performance.
Proceedings ArticleDOI
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a target attentive graph neural network (TAGNN) model for session-based recommendation, which adaptively activates different user interests with respect to varied target items.