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Temporal networks serve as abstractions of many real-world dynamic systems.
The sociology of georg simmel , volume 92892
Georg Simmel · 1950
Earlier work this paper cites.
The strength of weak ties
Mark S Granovetter · 1973
Earlier work this paper cites.
Computer performance modeling handbook
Stephen Lavenberg · 1983
Earlier work this paper cites.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
A theorem on Fourier-Stieltjes integrals
Salomon Bochner · 1992
Earlier work this paper cites.
Structure and function of the feed-forward loop network motif
Shmoolik Mangan and Uri Alon · 2003
Earlier work this paper cites.
Structure prediction in temporal networks using frequent subgraphs
Mayank Lahiri and Tanya Y Berger-Wolf · 2007
Earlier work this paper cites.
The role of edge weights in social networks: modelling structure and dynamics
Riitta Toivonen, Jussi M Kumpula, Jari Saramäki, Jukka-Pekka Onnela, János Kertész, and Kimmo Kaski · 2007
Earlier work this paper cites.
The graph neural network model
Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini · 2008
Earlier work this paper cites.
Temporal motifs in time-dependent networks
Lauri Kovanen, Márton Karsai, Kimmo Kaski, János Kertész, and Jari Saramäki · 2011
Earlier work this paper cites.
Temporal networks
Petter Holme and Jari Saramäki · 2012
Earlier work this paper cites.
Nonparametric link prediction in dynamic networks
Purnamrita Sarkar, Deepayan Chakrabarti, and Michael I Jordan · 2012
Earlier work this paper cites.
Temporal motifs reveal homophily, gender-specific patterns, and group talk in call sequences
Lauri Kovanen, Kimmo Kaski, János Kertész, and Jari Saramäki · 2013
Earlier work this paper cites.
Efficient graphlet counting for large networks
Nesreen K Ahmed, Jennifer Neville, Ryan A Rossi, and Nick Duffield · 2015
Earlier work this paper cites.
Higher-order organization of complex networks
Austin R Benson, David F Gleich, and Jure Leskovec · 2016
Earlier work this paper cites.
Variational graph auto-encoders
Thomas N Kipf and Max Welling · 2016
Earlier work this paper cites.
Reconstructing markov processes from independent and anonymous experiments
Silvio Micali and Zeyuan Allen Zhu · 2016
Earlier work this paper cites.
Link prediction in dynamic networks using graphlet
Mahmudur Rahman and Mohammad Al Hasan · 2016
Earlier work this paper cites.
Semi-supervised classification with graph convolutional networks
Thomas N Kipf and Max Welling · 2017
Cited alongside, same era.
Lectures on the Poisson process , volume 7
Günter Last and Mathew Penrose · 2017
Cited alongside, same era.
Inhomogeneous hypergraph clustering with applications
Pan Li and Olgica Milenkovic · 2017
Cited alongside, same era.
Motifs in temporal networks
Ashwin Paranjape, Austin R Benson, and Jure Leskovec · 2017
Cited alongside, same era.
Know-evolve: deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
E-lstm-d: A deep learning framework for dynamic network link prediction
Jinyin Chen, Jian Zhang, Xuanheng Xu, Chenbo Fu, Dan Zhang, Qingpeng Zhang, and Qi Xuan · 2019
Later among the works it cites.
Variational graph recurrent neural networks
Ehsan Hajiramezanali, Arman Hasanzadeh, Krishna Narayanan, Nick Duffield, Mingyuan Zhou, and Xiaoning Qian · 2019
Later among the works it cites.
Time2vec: Learning a vector representation of time
Seyed Mehran Kazemi, Rishab Goel, Sepehr Eghbali, Janahan Ramanan, Jaspreet Sahota, Sanjay Thakur, Stella Wu, Cathal Smyth, Pascal Poupart, and Marcus Brubaker · 2019
Later among the works it cites.
Predicting dynamic embedding trajectory in temporal interaction networks
Srijan Kumar, Xikun Zhang, and Jure Leskovec · 2019
Later among the works it cites.
Higher-order ranking and link prediction: From closing triangles to closing higher-order motifs
Ryan A Rossi, Anup Rao, Sungchul Kim, Eunyee Koh, Nesreen K Ahmed, and Gang Wu · 2019
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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
Cited alongside, same era.
Simplicial closure and higher-order link prediction
Austin R Benson, Rediet Abebe, Michael T Schaub, Ali Jadbabaie, and Jon Kleinberg · 2018
Cited alongside, same era.
Dynamic network embedding: An extended approach for skip-gram based network embedding
Lun Du, Yun Wang, Guojie Song, Zhicong Lu, and Junshan Wang · 2018
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Organization of feed-forward loop motifs reveals architectural principles in natural and engineered networks
Thomas E Gorochowski, Claire S Grierson, and Mario di Bernardo · 2018
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Anonymous walk embeddings
Sergey Ivanov and Evgeny Burnaev · 2018
Cited alongside, same era.
Deep dynamic network embedding for link prediction
Taisong Li, Jiawei Zhang, S Yu Philip, Yan Zhang, and Yonghong Yan · 2018
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Node embedding over temporal graphs
Uriel Singer, Ido Guy, and Kira Radinsky · 2019
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On the equivalence between positional node embeddings and structural graph representations
Balasubramaniam Srinivasan and Bruno Ribeiro · 2019
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Dyrep: Learning representations over dynamic graphs
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha · 2019
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Self-attention with functional time representation learning
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2019
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Evolution of resilience in protein interactomes across the tree of life
Marinka Zitnik, Marcus W Feldman, Jure Leskovec, et al · 2019
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dyngraph2vec: Capturing network dynamics using dynamic graph representation learning
Palash Goyal, Sujit Rokka Chhetri, and Arquimedes Canedo · 2020
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Dynamic graph convolutional networks
Franco Manessi, Alessandro Rozza, and Mario Manzo · 2020
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EvolveGCN: Evolving graph convolutional networks for dynamic graphs
Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao B Schardl, and Charles E Leiserson · 2020
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Temporal graph networks for deep learning on dynamic graphs
Emanuele Rossi, Ben Chamberlain, Fabrizio Frasca, Davide Eynard, Federico Monti, and Michael Bronstein · 2020
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DySAT: Deep neural representation learning on dynamic graphs via self-attention networks
Aravind Sankar, Yanhong Wu, Liang Gou, Wei Zhang, and Hao Yang · 2020
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Generic representation learning for dynamic social interaction
Yanbang Wang, Pan Li, Chongyang Bai, VS Subrahmanian, and Jure Leskovec · 2020
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Inductive representation learning on temporal graphs
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2020
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Revisiting graph neural networks for link prediction
Muhan Zhang, Pan Li, Yinglong Xia, Kai Wang, and Long Jin · 2020
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