Graph Filtration Learning

A general neural network architecture for persistence diagrams and graph classification

Original

Carrière, M., Chazal, F., Ike, Y., Lacombe, T., Royer, M., and Umeda, Y · 1904

Earlier work this paper cites.

A reduction of a graph to a canonical form and an algebra arising during this reduction

Weisfeiler, B. and Lehman, A. A · 1968

Earlier work this paper cites.

Topological persistence and simplification

Edelsbrunner, H., Letcher, D., and Zomorodian, A · 2002

Earlier work this paper cites.

Algebraic Topology

Hatcher, A · 2002

Earlier work this paper cites.

The graph neural network model

Scarselli, F., Gori, M., Tsoi, A., hagenbuchner, M., and Monfardini, G · 2009

Earlier work this paper cites.

Efficient graphlet kernels for large graph comparison

Shervashidze, N., Vishwanathan, S., Petri, T., Mehlhorn, K., and Borgwardt, K · 2009

Earlier work this paper cites.

Computational Topology : An Introduction

Edelsbrunner, H. and Harer, J. L · 2010

Earlier work this paper cites.

Zigzag persistent homology in matrix multiplication time

Milosavljević, N., Morozov, D., and Skraba, P · 2011

Earlier work this paper cites.

Weisfeiler-lehmann graph kernels

Shervashidze, N., Schweitzer, P., van Leeuwen, E., Mehlhorn, K., and Borgwardt, K · 2011

Earlier work this paper cites.

Scalable kernels for graphs with continuous attributes

Feragen, A., Kasenburg, N., Petersen, J., Bruijne, M., and Borgwardt, K · 2013

Earlier work this paper cites.

Networks and cycles: A persistent homology approach to complex networks

Petri, G., Scolamiero, M., Donato, I., and Vaccarino, F · 2013

Earlier work this paper cites.

Clear and compress: Computing persistent homology in chunks

Bauer, U., Kerber, M., and Reininghaus, J · 2014

Cited alongside, same era.

Convolutional networks on graphs for learning molecular fingerprints

Duvenaud, D., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R · 2015

Cited alongside, same era.

Interaction networks for learning about objects, relations and physics

Battaglia, P., Pascanu, R., Lai, M., Rezende, D., and Kavukcuoglu, K · 2016

Cited alongside, same era.

Molecular graph convolutions: Moving beyond fingerprints

Kearns, S., McCloskey, K., Berndl, M., Pande, V., and Riley, P · 2016

Cited alongside, same era.

On valid optimal assignment kernels and applications to graph classification

Kriege, N., Giscard, P.-L., and Wilson, R · 2016

Cited alongside, same era.

Gated graph sequence neural networks

Li, Y., Tarlow, D., Brockschmidt, M., and Zemel, R · 2016

SchNet: A continuous-filter convolutional neural network for modeling quantum interactions

Schütt, K., Kindermans, P. J., Sauceda, H. E., Chmiela, S., Tkatchenko, A., and Müller., K. R · 2017

Later among the works it cites.

Deep sets

Zaheer, M., Kottur, S., Ravanbakhsh, S., Poczos, B., Salakhutdinov, R., and Smola, A. J · 2017

Later among the works it cites.

Dynamic graph cnn for learning on point clouds

Original

Wang, Y., Sun, Y., Liu, Z., Sarma, S., Bronstein, M., and Solomon, J · 2018

Later among the works it cites.

Representation learning on graphs with jumping knowledge networks

Xu, K., Li, C., Tian, Y., Sonobe, T., Kawarabayashi, K., and Jegelka, S · 2018

Later among the works it cites.

Hierarchical graph representation learning with differentiable pooling

Ying, R., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018

Later among the works it cites.