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Graphs are essential representations of many real-world data such as social networks.
Dynamic graph models
Frank Harary and Gopal Gupta. 1997 · 1997
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
The TREC-8 Question Answering Track Report.. In Trec , Vol. 99. 77–82
Ellen M Voorhees et al · 1999
Earlier work this paper cites.
Birds of a feather: Homophily in social networks
Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001 · 2001
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions. In Proceedings of the 20th International conference on Machine learning (ICML-03) . 912–919
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty. 2003 · 2003
Earlier work this paper cites.
A new model for learning in graph domains. In Neural Networks, 2005. IJCNN’05. Proceedings. 2005 IEEE International Joint Conference on , Vol. 2. IEEE, 729–734
Marco Gori, Gabriele Monfardini, and Franco Scarselli. [n. d.] · 2005
Earlier work this paper cites.
Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In Proceedings of the 17th international conference on World Wide Web . ACM, 685–694
Yu-Ru Lin, Yun Chi, Shenghuo Zhu, Hari Sundaram, and Belle L Tseng. 2008 · 2008
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2009 · 2009
Earlier work this paper cites.
Modeling relationship strength in online social networks. In Proceedings of the 19th international conference on World wide web . ACM, 981–990
Rongjing Xiang, Jennifer Neville, and Monica Rogati. 2010 · 2010
Earlier work this paper cites.
Temporal link prediction using matrix and tensor factorizations
Daniel M Dunlavy, Tamara G Kolda, and Evrim Acar. 2011 · 2011
Earlier work this paper cites.
Time-varying graphs and dynamic networks
Arnaud Casteigts, Paola Flocchini, Walter Quattrociocchi, and Nicola Santoro. 2012 · 2012
Earlier work this paper cites.
Temporal networks
Petter Holme and Jari Saramäki. 2012 · 2012
Earlier work this paper cites.
mTrust: Discerning multi-faceted trust in a connected world. In Proceedings of the fifth ACM international conference on Web search and data mining . ACM, 93–102
J. Tang, H. Gao, and H. Liu. 2012 · 2012
Earlier work this paper cites.
Spectral networks and locally connected networks on graphs
Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2013 · 2013
Cited alongside, same era.
Konect: the koblenz network collection. In Proceedings of the 22nd International Conference on World Wide Web . ACM, 1343–1350
Jérôme Kunegis. 2013 · 2013
Cited alongside, same era.
Interaction networks for learning about objects, relations and physics. In Advances in neural information processing systems . 4502–4510
Peter Battaglia, Razvan Pascanu, Matthew Lai, Danilo Jimenez Rezende, et al · 2016
Cited alongside, same era.
A compositional object-based approach to learning physical dynamics
Michael B Chang, Tomer Ullman, Antonio Torralba, and Joshua B Tenenbaum. 2016 · 2016
Cited alongside, same era.
Convolutional neural networks on graphs with fast localized spectral filtering. In Advances in Neural Information Processing Systems . 3844–3852
Attributed network embedding for learning in a dynamic environment. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management . ACM, 387–396
Jundong Li, Harsh Dani, Xia Hu, Jiliang Tang, Yi Chang, and Huan Liu. 2017 · 2017
Later among the works it cites.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017 · 2017
Later among the works it cites.
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio. 2017 · 2017
Later among the works it cites.
TIMERS: Error-Bounded SVD Restart on Dynamic Networks
Ziwei Zhang, Peng Cui, Jian Pei, Xiao Wang, and Wenwu Zhu. 2017 · 2017
Later among the works it cites.
Relational inductive biases, deep learning, and graph networks
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Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016 · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks. In Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining . ACM, 855–864
Aditya Grover and Jure Leskovec. 2016 · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling. 2016 · 2016
Cited alongside, same era.
Patient subtyping via time-aware LSTM networks. In Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 65–74
Inci M Baytas, Cao Xiao, Xi Zhang, Fei Wang, Anil K Jain, and Jiayu Zhou. 2017 · 2017
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst. 2017 · 2017
Cited alongside, same era.
Streaming recommender systems. In Proceedings of the 26th International Conference on World Wide Web . International World Wide Web Conferences Steering Committee, 381–389
Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Mark A Hasegawa-Johnson, and Thomas S Huang. 2017 · 2017
Cited alongside, same era.
Neural message passing for quantum chemistry
Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017 · 2017
Cited alongside, same era.
Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems . 1024–1034
Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017 · 2017
Cited alongside, same era.
Peter W Battaglia, Jessica B Hamrick, Victor Bapst, Alvaro Sanchez-Gonzalez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, et al · 2018
Closest in time.
DynGEM: Deep Embedding Method for Dynamic Graphs
Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu. 2018 · 2018
Closest in time.
Toward online node classification on streaming networks
Ling Jian, Jundong Li, and Huan Liu. 2018 · 2018
Closest in time.
Streaming link prediction on dynamic attributed networks. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining . ACM, 369–377
Jundong Li, Kewei Cheng, Liang Wu, and Huan Liu. 2018 · 2018
Closest in time.
DepthLGP: Learning Embeddings of Out-of-Sample Nodes in Dynamic Networks. AAAI
Jianxin Ma, Peng Cui, and Wenwu Zhu. 2018 · 2018
Closest in time.
Graph networks as learnable physics engines for inference and control
Alvaro Sanchez-Gonzalez, Nicolas Heess, Jost Tobias Springenberg, Josh Merel, Martin Riedmiller, Raia Hadsell, and Peter Battaglia. 2018 · 2018
Closest in time.
Graph Convolutional Neural Networks for Web-Scale Recommender Systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec. 2018 · 2018
Closest in time.
Dynamic Network Embedding by Modeling Triadic Closure Process
Le-kui Zhou, Yang Yang, Xiang Ren, Fei Wu, and Yueting Zhuang. 2018 · 2018
Closest in time.