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DualGNN: Dual Graph Neural Network for Multimedia Recommendation
IEEE transactions on multimedia, 2023One of the important factors affecting micro-video recommender systems is to model the multi-modal user preference on the micro-video. Despite the remarkable performance of prior arts, they are still limited by fusing the user preference derived from ...
Qifan Wang +6 more
semanticscholar +1 more source
Traffic Flow Prediction via Spatial Temporal Graph Neural Network
The Web Conference, 2020Traffic flow analysis, prediction and management are keystones for building smart cities in the new era. With the help of deep neural networks and big traffic data, we can better understand the latent patterns hidden in the complex transportation ...
Xiaoyang Wang +7 more
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A survey of graph neural network based recommendation in social networks
Neurocomputing, 2023Xiao Li, Li Sun, Mengjie Ling, Yan Peng
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Graph Neural Network for Fraud Detection via Spatial-Temporal Attention
IEEE Transactions on Knowledge and Data Engineering, 2022Card fraud is an important issue and incurs a considerable cost for both cardholders and issuing banks. Contemporary methods apply machine learning-based approaches to detect fraudulent behavior from transaction records.
Dawei Cheng +3 more
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A Graph Neural Network-Based Digital Twin for Network Slicing Management
IEEE Transactions on Industrial Informatics, 2022Network slicing has emerged as a promising networking paradigm to provide resources tailored for Industry 4.0 and diverse services in 5G networks. However, the increased network complexity poses a huge challenge in network management due to virtualized ...
Haozhe Wang, Yulei Wu, G. Min, W. Miao
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Graph neural network approaches for drug-target interactions.
Current Opinion in Structural Biology, 2022Developing new drugs remains prohibitively expensive, time-consuming, and often involves safety issues. Accurate prediction of drug-target interactions (DTIs) can guide the drug discovery process and thus facilitate drug development.
Zehong Zhang +8 more
semanticscholar +1 more source
Graph Mining with Graph Neural Networks
Proceedings of the 14th ACM International Conference on Web Search and Data Mining, 2021Graphs are ubiquitous data structures in various fields, such as social media, transportation, linguistics and chemistry. To solve downstream graph-related tasks, it is of great significance to learn effective representations for graphs. My research strives to help meet this demand; due to the huge success of deep learning methods, especially graph ...
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