Fetching the paper…
Reading the bibliography…
Graph learning methods help utilize implicit relationships among data items, thereby reducing training label requirements and improving task performance.
On the shortest spanning subtree of a graph and the traveling salesman problem
Joseph B Kruskal · 1956
Earlier work this paper cites.
An information flow model for conflict and fission in small groups
Wayne W Zachary · 1977
Earlier work this paper cites.
Graph spanners
David Peleg and Alejandro A Schäffer · 1989
Earlier work this paper cites.
On sparse spanners of weighted graphs
Ingo Althöfer, Gautam Das, David Dobkin, Deborah Joseph, and José Soares · 1993
Earlier work this paper cites.
Using randomized sparsification to approximate minimum cuts
David R Karger · 1994
Earlier work this paper cites.
Approximating st minimum cuts in Õ( n 2 n^{2} ) time
András A Benczúr and David R Karger · 1996
Earlier work this paper cites.
Spectral graph theory
Fan RK Chung · 1997
Earlier work this paper cites.
The MNIST database of handwritten digits
Yann LeCun, Corinna Cortes, and Christopher J. C. Burges · 1998
Earlier work this paper cites.
Collective dynamics of ‘small-world’ networks
Duncan J Watts and Steven H Strogatz · 1998
Earlier work this paper cites.
On spectral clustering: Analysis and an algorithm
Andrew Ng, Michael Jordan, and Yair Weiss · 2001
Earlier work this paper cites.
Estimation and prediction for stochastic blockstructures
Krzysztof Nowicki and Tom A B Snijders · 2001
Earlier work this paper cites.
Bochner’s method for cell complexes and combinatorial ricci curvature
Robin Forman · 2003
Earlier work this paper cites.
The structure and function of complex networks
Mark EJ Newman · 2003
Earlier work this paper cites.
Semi-supervised learning using gaussian fields and harmonic functions
Xiaojin Zhu, Zoubin Ghahramani, and John D Lafferty · 2003
Earlier work this paper cites.
Growing well-connected graphs
Arpita Ghosh and Stephen Boyd · 2006
Earlier work this paper cites.
Modularity and community structure in networks
Mark EJ Newman · 2006
Earlier work this paper cites.
Using pagerank to locally partition a graph
Reid Andersen, Fan Chung, and Kevin Lang · 2007
Earlier work this paper cites.
Characterization of complex networks: A survey of measurements
L da F Costa, Francisco A Rodrigues, Gonzalo Travieso, and Paulino Ribeiro Villas Boas · 2007
Earlier work this paper cites.
Fast unfolding of communities in large networks
Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre · 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.
Graph sparsification by effective resistances
Daniel A Spielman and Nikhil Srivastava · 2008
Cited alongside, same era.
Twice-ramanujan sparsifiers
Joshua D Batson, Daniel A Spielman, and Nikhil Srivastava · 2009
Cited alongside, same era.
Expanders via random spanning trees
Navin Goyal, Luis Rademacher, and Santosh Vempala · 2009
Cited alongside, same era.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Cited alongside, same era.
Ricci curvature of markov chains on metric spaces
Yann Ollivier · 2009
Cited alongside, same era.
Graph sparsification by edge-connectivity and random spanning trees
Wai Shing Fung and Nicholas JA Harvey · 2010
Cited alongside, same era.
Pitfalls of graph neural network evaluation
Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan Günnemann · 2018
Later among the works it cites.
Verse: Versatile graph embeddings from similarity measures
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, and Emmanuel Müller · 2018
Later among the works it cites.
The lottery ticket hypothesis: Finding sparse, trainable neural networks
Jonathan Frankle and Michael Carbin · 2019
Later among the works it cites.
Diffusion improves graph learning
Johannes Gasteiger, Stefan Weißenberger, and Stephan Günnemann · 2019
Later among the works it cites.
Spectral graph complexity
Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alexander Bronstein, and Emmanuel Müller · 2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A general framework for graph sparsification
Wai Shing Fung, Ramesh Hariharan, Nicholas JA Harvey, and Debmalya Panigrahi · 2011
Cited alongside, same era.
Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
Cited alongside, same era.
Spectral sparsification of graphs
Daniel A Spielman and Shang-Hua Teng · 2011
Cited alongside, same era.
Ollivier’s ricci curvature, local clustering and curvature-dimension inequalities on graphs
Jürgen Jost and Shiping Liu · 2014
Cited alongside, same era.
Deepwalk: Online learning of social representations
Bryan Perozzi, Rami Al-Rfou, and Steven Skiena · 2014
Cited alongside, same era.
Optimizing network robustness by edge rewiring: a general framework
Hau Chan and Leman Akoglu · 2016
Cited alongside, same era.
Chen Cai and Yusu Wang · 2020
Later among the works it cites.
Node embeddings and exact low-rank representations of complex networks
Sudhanshu Chanpuriya, Cameron Musco, Konstantinos Sotiropoulos, and Charalampos Tsourakakis · 2020
Later among the works it cites.
Open graph benchmark: Datasets for machine learning on graphs
Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec · 2020
Later among the works it cites.
Graph neural networks exponentially lose expressive power for node classification
Kenta Oono and Taiji Suzuki · 2020
Later among the works it cites.
The impossibility of low-rank representations for triangle-rich complex networks
C Seshadhri, Aneesh Sharma, Andrew Stolman, and Ashish Goel · 2020
Later among the works it cites.
A unified lottery ticket hypothesis for graph neural networks
Tianlong Chen, Yongduo Sui, Xuxi Chen, Aston Zhang, and Zhangyang Wang · 2021
Later among the works it cites.
Hierarchical agglomerative graph clustering in nearly-linear time
Laxman Dhulipala, David Eisenstat, Jakub Łącki, Vahab Mirrokni, and Jessica Shi · 2021
Later among the works it cites.
Diffwire: Inductive graph rewiring via the lovász bound
Adrián Arnaiz-Rodríguez, Ahmed Begga, Francisco Escolano, and Nuria Oliver · 2022
Later among the works it cites.
Oversquashing in GNNs through the lens of information contraction and graph expansion
Pradeep Kr Banerjee, Kedar Karhadkar, Yu Guang Wang, Uri Alon, and Guido Montúfar · 2022
Later among the works it cites.
Stars: Tera-scale graph building for clustering and learning
CJ Carey, Jonathan Halcrow, Rajesh Jayaram, Vahab Mirrokni, Warren Schudy, and Peilin Zhong · 2022
Later among the works it cites.
Andreea Deac, Marc Lackenby, and Petar Veličković · 2022
Later among the works it cites.
Discrete curvature on graphs from the effective resistance
Karel Devriendt and Renaud Lambiotte · 2022
Later among the works it cites.
Graphworld: Fake graphs bring real insights for gnns
John Palowitch, Anton Tsitsulin, Brandon Mayer, and Bryan Perozzi · 2022
Later among the works it cites.
Understanding over-squashing and bottlenecks on graphs via curvature
Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein · 2022
Later among the works it cites.
FoSR: First-order spectral rewiring for addressing oversquashing in gnns
Kedar Karhadkar, Pradeep Kr Banerjee, and Guido Montúfar · 2023
Closest in time.