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Temporal graphs represent the dynamic relationships among entities and occur in many real life application like social networks, e commerce, communication, road networks, biological systems, and many more.
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Tomas Mikolov, Kai Chen, Gregory S. Corrado, and Jeffrey Dean · 2013
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Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
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Yi-Hsuan Tsai and Ming-Hsuan Yang · 2014
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How memory generates heterogeneous dynamics in temporal networks
Christian L. Vestergaard, Mathieu Génois, and Alain Barrat · 2014
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Convolutional networks on graphs for learning molecular fingerprints
David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Alan Aspuru-Guzik, and Ryan P Adams · 2015
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Petter Holme · 2015
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Silu Huang, Ada Wai-Chee Fu, and Ruifeng Liu · 2015
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Diachronic word embeddings reveal statistical laws of semantic change
William L. Hamilton, Jure Leskovec, and Dan Jurafsky · 2016
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Enumerating maximal cliques in temporal graphs
Anne-Sophie Himmel, Hendrik Molter, Rolf Niedermeier, and Manuel Sorge · 2016
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Continuous control with deep reinforcement learning
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Traveling salesman problems in temporal graphs
Othon Michail and Paul G. Spirakis · 2016
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Asymmetric transitivity preserving graph embedding
Mingdong Ou, Peng Cui, Jian Pei, Ziwei Zhang, and Wenwu Zhu · 2016
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Temporal network embedding with micro- and macro-dynamics
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Node embedding over temporal graphs
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Dyrep: Learning representations over dynamic graphs
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Self-attention with functional time representation learning
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How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
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How powerful are graph neural networks?
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Semi-supervised classification with graph convolutional networks
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Motifs in temporal networks
Ashwin Paranjape, Austin R. Benson, and Jure Leskovec · 2017
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A tutorial on hawkes processes for events in social media
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Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin · 2017
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Position-aware graph neural networks
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A data-driven graph generative model for temporal interaction networks
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Beyond homophily in graph neural networks: Current limitations and effective designs
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A survey on embedding dynamic graphs
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Graph Learning for Combinatorial Optimization: A Survey of State-of-the-Art
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Inductive representation learning in temporal networks via causal anonymous walks
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A comprehensive survey on graph neural networks
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A temporal kernel approach for deep learning with continuous-time information
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