研究论文
DySAT
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, Hao Yang
University of Illinois Urbana-Champaign Visa (United States)
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摘要与影响
摘要 · 完整
Learning node representations in graphs is important for many applications such as link prediction, node classification, and community detection. Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it challenging to learn informative node representations over time.
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关键指标
638
被引次数 · OpenAlex
27.86
领域内被引倍数
同类平均 = 1
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前 0.3%
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学术脉络
学科主题
计算机 / AIAdvanced Graph Neural Networks
Complex Network Analysis Techniques · Data Quality and Management
参考文献 28
International Conference on Learning Representations (ICLR 2013)
被引 6,254豊 松尾 · 人工知能学会誌 = Journal of Japanese Society for Artificial Intelligence · 2013
A Global Geometric Framework for Nonlinear Dimensionality Reduction
被引 13,856Joshua B. Tenenbaum, Vin de Silva, John C. Langford · Science · 2000
Dynamic social network analysis using latent space models
被引 436Purnamrita Sarkar, Andrew Moore · ACM SIGKDD Explorations Newsletter · 2005
此处列出前 3 条
引用本文 638
A Comprehensive Survey on Graph Anomaly Detection With Deep Learning
被引 834Xiaoxiao Ma, Jia Xin Wu, Shan Xue · IEEE Transactions on Knowledge and Data Engineering · 2021
A General Survey on Attention Mechanisms in Deep Learning
被引 695Gianni Brauwers, Flavius Frăsincar · IEEE Transactions on Knowledge and Data Engineering · 2021
Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
被引 334Joakim Skarding, Bogdan Gabryś, Katarzyna Musiał · IEEE Access · 2021
按被引量排序，此处列出前 3 条