Yanqiao Zhu

University of California at Los Angeles
0000-0003-2205-5304
https://sxkdz.github.io
Publications27
h-index10
Citations1,116
Highly Influential Citations252
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Session-based Recommendation with Graph Neural Networks
TLDR
In the proposed method, session sequences are modeled as graph-structured data and GNN can capture complex transitions of items, which are difficult to be revealed by previous conventional sequential methods.
View PDF on arXiv
Deep Graph Contrastive Representation Learning
TLDR
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.
View PDF on arXiv
Graph Contrastive Learning with Adaptive Augmentation
TLDR
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.
View on ACM
TAGNN: Target Attentive Graph Neural Networks for Session-based Recommendation
TLDR
In TAGNN, target-aware attention adaptively activates different user interests with respect to varied target items and the learned interest representation vector varies with different target items, greatly improving the expressiveness of the model.
View on ACM
Hierarchical Graph Convolutional Networks for Semi-supervised Node Classification
TLDR
A novel deep Hierarchical Graph Convolutional Network (H-GCN) for semi-supervised node classification, which first repeatedly aggregates structurally similar nodes to hyper-nodes and then refines the coarsened graph to the original to restore the representation for each node.
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An Empirical Study of Graph Contrastive Learning
TLDR
This work identifies several critical design considerations within a general GCL paradigm, including augmentation functions, contrasting modes, contrastive objectives, and negative mining techniques, and develops an easy-to-use library PyGCL, featuring modularized CL components, standardized evaluation, and experiment management.
View PDF on arXiv
Deep Graph Structure Learning for Robust Representations: A Survey
TLDR
A general paradigm of Graph Structure Learning is formulated, and state-ofthe-art methods classified by how they model graph structures are reviewed, followed by applications that incorporate the idea of GSL in other graph tasks.
Active Learning for Wireless IoT Intrusion Detection
TLDR
The fundamental challenges against the design of a successful intrusion detection system for a wireless IoT network are presented and the rudimentary concepts of active learning are reviewed and its employment in the diverse applications of wireless intrusion detection are proposed.
View on IEEE
CAGNN: Cluster-Aware Graph Neural Networks for Unsupervised Graph Representation Learning
TLDR
A novel cluster-aware graph neural network (CAGNN) model for unsupervised graph representation learning using self-supervised techniques, which gains over 7% improvements in terms of accuracy on node clustering over state-of-the-arts.
View PDF on arXiv
GraphAIR: Graph Representation Learning with Neighborhood Aggregation and Interaction
View PDF on arXiv
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