2020

Geom-GCN: Geometric Graph Convolutional Networks

Pei, Hongbin, Wei, Bingzhe, Chang, Kevin Chen-Chuan et al.

Understand

Message-passing neural networks (MPNNs) have been successfully applied to representation learning on graphs in a variety of real-world applications.

However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs.
Few studies have noticed the weaknesses from different perspectives.
From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses.

Built on

A global geometric framework for nonlinear dimensionality reduction
Joshua B Tenenbaum, Vin De Silva, and John C Langford · 2000
Earlier work this paper cites.
Latent space approaches to social network analysis
Peter D Hoff, Adrian E Raftery, and Mark S Handcock · 2002
Earlier work this paper cites.
Assortative mixing in networks
Mark EJ Newman · 2002
Earlier work this paper cites.
Visualizing data using t-sne
Laurens van der Maaten and Geoffrey Hinton · 2008
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.
Collective classification in network data
Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad · 2008
Earlier work this paper cites.
Social influence analysis in large-scale networks
Jie Tang, Jimeng Sun, Chi Wang, and Zi Yang · 2009
Earlier work this paper cites.
Intrinsic shape context descriptors for deformable shapes
Iasonas Kokkinos, Michael M Bronstein, Roee Litman, and Alex M Bronstein · 2012
Earlier work this paper cites.
Query-driven active surveying for collective classification
Galileo Namata, Ben London, Lise Getoor, and Bert Huang · 2012
Earlier work this paper cites.
Adam: A method for stochastic optimization
Original
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Convolutional neural networks on graphs with fast localized spectral filtering
Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst · 2016
Earlier work this paper cites.
Gated graph sequence neural networks
Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard S. Zemel · 2016
Earlier work this paper cites.

Similar

subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs
Original
Annamalai Narayanan, Mahinthan Chandramohan, Lihui Chen, Yang Liu, and Santhoshkumar Saminathan · 2016
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Michael M Bronstein, Joan Bruna, Yann LeCun, Arthur Szlam, and Pierre Vandergheynst · 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
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
Thomas N. Kipf and Max Welling · 2017
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.
Machine learning meets complex networks via coalescent embedding in the hyperbolic space
Alessandro Muscoloni, Josephine Maria Thomas, Sara Ciucci, Ginestra Bianconi, and Carlo Vittorio Cannistraci · 2017
Cited alongside, same era.
Poincaré embeddings for learning hierarchical representations
Maximilian Nickel and Douwe Kiela · 2017
Cited alongside, same era.
struc2vec : Learning node representations from structural identity
Leonardo Filipe Rodrigues Ribeiro, Pedro H. P. Saverese, and Daniel R. Figueiredo · 2017
Cited alongside, same era.
Graph attention networks
Original
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Liò, and Yoshua Bengio · 2017
Cited alongside, same era.
Characterizing and discovering spatiotemporal social contact patterns for healthcare
Bo Yang, Hongbin Pei, Hechang Chen, Jiming Liu, and Shang Xia · 2017
Cited alongside, same era.
A comprehensive survey of graph embedding: Problems, techniques, and applications
Hongyun Cai, Vincent W Zheng, and Kevin Chen-Chuan Chang · 2018
Cited alongside, same era.

Then

Large-scale learnable graph convolutional networks
Hongyang Gao, Zhengyang Wang, and Shuiwang Ji · 2018
Later among the works it cites.
Covariant compositional networks for learning graphs
Risi Kondor, Hy Truong Son, Horace Pan, Brandon M. Anderson, and Shubhendu Trivedi · 2018
Later among the works it cites.
Learning continuous hierarchies in the lorentz model of hyperbolic geometry
Maximilian Nickel and Douwe Kiela · 2018
Later among the works it cites.
Group sparse bayesian learning for active surveillance on epidemic dynamics
Hongbin Pei, Bo Yang, Jiming Liu, and Lei Dong · 2018
Later among the works it cites.
A united approach to learning sparse attributed network embedding
Hao Wang, Enhong Chen, Qi Liu, Tong Xu, Dongfang Du, Wen Su, and Xiaopeng Zhang · 2018
Later among the works it cites.
Representation learning on graphs with jumping knowledge networks
Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka · 2018
Later among the works it cites.
Supervised community detection with line graph neural networks
Zhengdao Chen, Lisha Li, and Joan Bruna · 2019
Later among the works it cites.
Community detection on networks with ricci flow
Chien-Chun Ni, Yu-Yao Lin, Feng Luo, and Jie Gao · 2019
Later among the works it cites.
Multi-scale attributed node embedding
Original
Benedek Rozemberczki, Carl Allen, and Rik Sarkar · 2019
Later among the works it cites.
MCNE: an end-to-end framework for learning multiple conditional network representations of social network
Hao Wang, Tong Xu, Qi Liu, Defu Lian, Enhong Chen, Dongfang Du, Han Wu, and Wen Su · 2019
Later among the works it cites.
How powerful are graph neural networks?
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka · 2019
Later among the works it cites.

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…