2020

Inductive Representation Learning on Temporal Graphs

Xu, Da, Ruan, Chuanwei, Korpeoglu, Evren et al.

Understand

Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks.

The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns.
The node embeddings, which are now functions of time, should represent both the static node features and the evolving topological structures.
Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture.

Built on

Linguistic inquiry and word count: Liwc 2001
James W Pennebaker, Martha E Francis, and Roger J Booth · 2001
Earlier work this paper cites.
Random features for large-scale kernel machines
Ali Rahimi and Benjamin Recht · 2008
Earlier work this paper cites.
Auto-encoding variational bayes
Original
Diederik P Kingma and Max Welling · 2013
Earlier work this paper cites.
Introduction to abstract harmonic analysis
Lynn H Loomis · 2013
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Original
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio · 2014
Earlier work this paper cites.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Earlier work this paper cites.
Deep convolutional networks on graph-structured data
Original
Mikael Henaff, Joan Bruna, and Yann LeCun · 2015
Earlier work this paper cites.
Variational inference with normalizing flows
Original
Danilo Jimenez Rezende and Shakir Mohamed · 2015
Earlier work this paper cites.
End-to-end memory networks
Sainbayar Sukhbaatar, Jason Weston, Rob Fergus, et al · 2015
Earlier work this paper cites.
Line: Large-scale information network embedding
Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei · 2015
Earlier work this paper cites.

Similar

Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Cited alongside, same era.
Density estimation using real nvp
Original
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2016
Cited alongside, same era.
node2vec: Scalable feature learning for networks
Aditya Grover and Jure Leskovec · 2016
Cited alongside, same era.
Structural deep network embedding
Daixin Wang, Peng Cui, and Wenwu Zhu · 2016
Cited alongside, same era.
Geometric deep learning on graphs and manifolds using mixture model cnns
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodola, Jan Svoboda, and Michael M Bronstein · 2017
Cited alongside, same era.
Dynamic edge-conditioned filters in convolutional neural networks on graphs
Martin Simonovsky and Nikos Komodakis · 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.
Graph attention networks
Original
Petar Veličković, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio · 2017
Cited alongside, same era.
Relational inductive biases, deep learning, and graph networks
Original
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.
Dyngem: Deep embedding method for dynamic graphs
Original
Palash Goyal, Nitin Kamra, Xinran He, and Yan Liu · 2018
Cited alongside, same era.
Inductive representation learning on large graphs
Will Hamilton, Zhitao Ying, and Jure Leskovec
Cited in the paper.
Representation learning on graphs: Methods and applications
Original
William L Hamilton, Rex Ying, and Jure Leskovec
Cited in the paper.

Then

Deep dynamic network embedding for link prediction
Taisong Li, Jiawei Zhang, S Yu Philip, Yan Zhang, and Yonghong Yan · 2018
Later among the works it cites.
Streaming graph neural networks
Original
Yao Ma, Ziyi Guo, Eric Zhao Zhaochun Ren, and Dawei Yin Jiliang Tang · 2018
Later among the works it cites.
Continuous-time dynamic network embeddings
Giang Hoang Nguyen, John Boaz Lee, Ryan A Rossi, Nesreen K Ahmed, Eunyee Koh, and Sungchul Kim · 2018
Later among the works it cites.
Dylink2vec: Effective feature representation for link prediction in dynamic networks
Original
Mahmudur Rahman, Tanay Kumar Saha, Mohammad Al Hasan, Kevin S Xu, and Chandan K Reddy · 2018
Later among the works it cites.
Representation learning over dynamic graphs
Original
Rakshit Trivedi, Mehrdad Farajtabar, Prasenjeet Biswal, and Hongyuan Zha · 2018
Later among the works it cites.
Graph convolutional neural networks for web-scale recommender systems
Rex Ying, Ruining He, Kaifeng Chen, Pong Eksombatchai, William L Hamilton, and Jure Leskovec · 2018
Later among the works it cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Later among the works it cites.
Generative graph convolutional network for growing graphs
Da Xu, Chuanwei Ruan, Kamiya Motwani, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2019
Later among the works it cites.
Product knowledge graph embedding for e-commerce
Da Xu, Chuanwei Ruan, Evren Korpeoglu, Sushant Kumar, and Kannan Achan · 2020
Closest in time.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…