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pro vyhledávání: '"Xu, Jiejun"'
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation. However, existing GNN-based models on social recommendation suffer from serious problems of generalization and oversmoothnes
Externí odkaz:
http://arxiv.org/abs/2304.04994
There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks, from gene regulatory networks to online transaction networks. Despite
Externí odkaz:
http://arxiv.org/abs/2303.17743
Graph data widely exist in many high-impact applications. Inspired by the success of deep learning in grid-structured data, graph neural network models have been proposed to learn powerful node-level or graph-level representation. However, most of th
Externí odkaz:
http://arxiv.org/abs/1906.02319
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additiona
Externí odkaz:
http://arxiv.org/abs/1902.10307
Determining the geographic focus of digital media is an essential first step for modern geographic information retrieval. However, publicly-visible location annotations are remarkably sparse in online data. In this work, we demonstrate a method which
Externí odkaz:
http://arxiv.org/abs/1406.2392
Akademický článek
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Autor:
Xu, Jiejun, Lu, Tsai-Ching
Publikováno v:
2015 IEEE International Conference on Big Data (Big Data); 2015, p767-775, 9p
Publikováno v:
Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I; 2015, p888-900, 13p
Publikováno v:
Advances in Visual Computing: 11th International Symposium, ISVC 2015, Las Vegas, NV, USA, December 14-16, 2015, Proceedings, Part I; 2015, p419-431, 13p
Autor:
Xu, Jiejun, Lu, Tsai-Ching
Publikováno v:
Social Computing, Behavioral-Cultural Modeling & Prediction; 2015, p458-463, 6p