Fetching the paper…
Reading the bibliography…
Vector representations of graphs and relational structures, whether hand-crafted feature vectors or learned representations, enable us to apply standard data analysis and machine learning techniques to the structures.
Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis
J.B. Kruskal · 1964
Earlier work this paper cites.
The generation of a unique machine description for chemical structures—a technique developed at chemical abstracts service
H.L. Morgan · 1965
Earlier work this paper cites.
Operations with structures
L. Lovász · 1967
Earlier work this paper cites.
The reduction of a graph to canonical form and the algebgra which appears therein
B. Weisfeiler and A. Leman · 1968
Earlier work this paper cites.
On the cancellation law among finite relational structures
L. Lovász · 1971
Earlier work this paper cites.
Random graph isomorphism
L. Babai, P. Erdös, and S. Selkow · 1980
Earlier work this paper cites.
Partitioning a graph in O ( | A | log 2 | V | ) O(|A|\log_{2}|V|)
A. Cardon and M. Crochemore · 1982
Earlier work this paper cites.
Extensions of Lipschitz mappings into a Hilbert space
W. Johnson and J. Lindenstrauss · 1984
Earlier work this paper cites.
On Lipschitz embeddings of finite metric spaces in Hilbert spaces
J. Bourgain · 1985
Earlier work this paper cites.
Describing graphs: A first-order approach to graph canonization
N. Immerman and E. Lander · 1990
Earlier work this paper cites.
A note on compact graphs
G. Tinhofer · 1991
Earlier work this paper cites.
An optimal lower bound on the number of variables for graph identification
J. Cai, M. Fürer, and N. Immerman · 1992
Earlier work this paper cites.
Support-vector networks
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
The geometry of graphs and some of its algorithmic applications
N. Linial, E. London, and Y. Rabinovich · 1995
Earlier work this paper cites.
Long short-term memory
S. Hochreiter and J. Schmidhuber · 1997
Earlier work this paper cites.
Kernel principal component analysis
B. Schölkopf, A. Smola, and K.-R. Müller · 1997
Earlier work this paper cites.
A global geometric framework for nonlinear dimensionality reduction
J. Tenenbaum, V. De Silva, and J. Langford · 2000
Earlier work this paper cites.
Algorithmic applications of low-distortion geometric embeddings
P. Indyk · 2001
Earlier work this paper cites.
Diffusion kernels on graphs and other discrete input spaces
R. Kondor and J.D. Lafferty · 2002
Earlier work this paper cites.
Laplacian eigenmaps for dimensionality reduction and data representation
M. Belkin and P. Niyogi · 2003
Earlier work this paper cites.
On graph kernels: Hardness results and efficient alternatives
T. Gärtner, P. Flach, and S. Wrobel · 2003
Earlier work this paper cites.
Marginalized kernels between labeled graphs
H. Kashima, K. Tsuda, and A. Inokuchi · 2003
Earlier work this paper cites.
Expressivity versus efficiency of graph kernels
J. Ramon and T. Gärtner · 2003
Earlier work this paper cites.
Kernels and regularization on graphs
A.J. Smola and R. Kondor · 2003
Earlier work this paper cites.
The complexity of counting homomorphisms seen from the other side
V. Dalmau and P. Jonsson · 2004
Earlier work this paper cites.
Cyclic pattern kernels for predictive graph mining
T. Horváth, T. Gärtner, and S. Wrobel · 2004
Earlier work this paper cites.
Shortest-path kernels on graphs
K.M. Borgwardt and H.-P. Kriegel · 2005
Earlier work this paper cites.
Approximating the cut-norm via Grothendieck’s inequality
N. Alon and A. Naor · 2006
Earlier work this paper cites.
Graph limits and parameter testing
C. Borgs, J. Chayes, L. Lovász, V. Sós, B. Szegedy, and K. Vesztergombi · 2006
Earlier work this paper cites.
Limits of dense graph sequences
L. Lovász and B. Szegedy · 2006
Earlier work this paper cites.
Linear time low tree-width partitions and algorithmic consequences
J. Nešetřil and P. Ossona de Mendez · 2006
Earlier work this paper cites.
Finite Model Theory and Its Applications
E. Grädel, P.G. Kolaitis, L. Libkin, M. Marx, J. Spencer, M.Y. Vardi, Y. Venema, and S. Weinstein · 2007
Earlier work this paper cites.
On the maximum quadratic assignment problem
V. Nagarajan and M. Sviridenko · 2009
Earlier work this paper cites.
The graph neural network model
F. Scarselli, M. Gori, A.C. Tsoi, M. Hagenbuchner, and G. Monfardini · 2009
Earlier work this paper cites.
Efficient graphlet kernels for large graph comparison
N. Shervashidze, S. Vishwanathan, T. Petri, K. Mehlhorn, and K. Borgwardt · 2009
Earlier work this paper cites.
On recognizing graphs by numbers of homomorphisms
Z. Dvorák · 2010
Cited alongside, same era.
Graph echo state networks
C. Gallicchio and A. Micheli · 2010
Cited alongside, same era.
A three-way model for collective learning on multi-relational data
M. Nickel, V. Tresp, and H.-P. Kriegel · 2011
Cited alongside, same era.
Weisfeiler-Lehman graph kernels
N. Shervashidze, P. Schweitzer, E.J. van Leeuwen, K. Mehlhorn, and K.M. Borgwardt · 2011
Cited alongside, same era.
Approximate graph isomorphism
V. Arvind, J. Köbler, S. Kuhnert, and Y. Vasudev · 2012
Cited alongside, same era.
Algorithmic meta theorems for circuit classes of constant and logarithmic depth
M. Elberfeld, A. Jakoby, and T. Tantau · 2012
Cited alongside, same era.
Using word embedding to enable semantic queries in relational databases
R. Bordawekar and O. Shmueli · 2017
Later among the works it cites.
Homomorphisms are a good basis for counting small subgraphs
R. Curticapean, H. Dell, and D. Marx · 2017
Later among the works it cites.
Descriptive Complexity, Canonisation, and Definable Graph Structure Theory
M. Grohe · 2017
Later among the works it cites.
Inductive representation learning on large graphs
W. Hamilton, R. Ying, and J. Leskovec · 2017
Later among the works it cites.
Representation learning on graphs: methods and applications
W.L. Hamilton, R. Ying, and J. Leskovec · 2017
Later among the works it cites.
Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
L. Lovász · 2012
Cited alongside, same era.
Distributed large-scale natural graph factorization
A. Ahmed, N. Shervashidze, S. Narayanamurthy, V. Josifovski, and A.J. Smola · 2013
Cited alongside, same era.
Sherali–Adams relaxations and indistinguishability in counting logics
A. Atserias and E. Maneva · 2013
Cited alongside, same era.
Translating embeddings for modeling multi-relational data
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko · 2013
Cited alongside, same era.
Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G.S. Corrado, and J. Dean · 2013
Cited alongside, same era.
Coinciding walk kernels: Parallel absorbing random walks for learning with graphs and few labels
M. Neumann, R. Garnett, and K. Kersting · 2013
Cited alongside, same era.
Later among the works it cites.
Globalized Weisfeiler-Lehman graph kernels: Global-local feature maps of graphs
C. Morris, K. Kersting, and P. Mutzel · 2017
Later among the works it cites.
graph2vec: Learning distributed representations of graphs
A. Narayanan, M. Chandramohan, R. Venkatesan, L. Chen, Y. Liu, and S. Jaiswal · 2017
Later among the works it cites.
Column networks for collective classification
T. Pham, T. Tran, D. Phung, and S. Venkatesh · 2017
Later among the works it cites.
Knowledge graph embedding: A survey of approaches and applications
Q. Wang, Z. Mao, B. Wang, and L. Guo · 2017
Later among the works it cites.
Definable ellipsoid method, sums-of-squares proofs, and the isomorphism problem
A. Atserias and J. Ochremiak · 2018
Later among the works it cites.
Tree-depth, quantifier elimination, and quantifier rank
Y. Chen and J. Flum · 2018
Later among the works it cites.
Lovász meets Weisfeiler and Leman
H. Dell, M. Grohe, and G. Rattan · 2018
Later among the works it cites.
Spectral graph similarity
T. Gervens · 2018
Later among the works it cites.
Graph similarity and approximate isomorphism
M. Grohe, G. Rattan, and G. Woeginger · 2018
Later among the works it cites.
Adversarially regularized graph autoencoder for graph embedding
S. Pan, R. Hu, G. Long, J. Jiang, L. Yao, and C. Zhang · 2018
Later among the works it cites.
Modeling relational data with graph convolutional networks
M . Schlichtkrull, T.N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling · 2018
Later among the works it cites.
Quantum and non-signalling graph isomorphisms
A. Atserias, Laura Mančinska, D.E. Roberson, R. Šámal, S. Severini, and A. Varvitsiotis · 2019
Later among the works it cites.
Color refinement, homomorphisms, and hypergraphs
J. Böker · 2019
Later among the works it cites.
The complexity of homomorphism indistinguishability
J. Böker, Y. Chen, M. Grohe, and G. Rattan · 2019
Later among the works it cites.
Exploiting latent information in relational databases via word embedding and application to degrees of disclosure
R. Bordawekar and O. Shmueli · 2019
Later among the works it cites.
A finite-model-theoretic view on propositional proof complexity
E. Grädel, M. Grohe, B. Pago, and W. Pakusa · 2019
Later among the works it cites.
A unifying view of explicit and implicit feature maps of graph kernels
N. Kriege, M. Neumann, C. Morris, K. Kersting, and P. Mutzel · 2019
Later among the works it cites.
N.M. Kriege, F.D Johansson, and C. Morris · 2019
Later among the works it cites.
A simple proof of the universality of invariant/equivariant graph neural networks
T. Maehara and H. NT · 2019
Later among the works it cites.
Quantum isomorphism is equivalent to equality of homomorphism counts from planar graphs
L. Mančinska and D.E. Roberson · 2019
Later among the works it cites.
Weisfeiler and leman go neural: Higher-order graph neural networks
C. Morris, M. Ritzert, M. Fey, W. Hamilton, J.E. Lenssen, G. Rattan, and M. Grohe · 2019
Later among the works it cites.
Graph neural networks for maximum constraint satisfaction
J. Tönshoff, M. Ritzert, H. Wolf, and M. Grohe · 2019
Later among the works it cites.
A comprehensive survey on graph neural networks
Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and P.S. Yu · 2019
Later among the works it cites.
Capsule graph neural network
Z. Xinyi and L. Chen · 2019
Later among the works it cites.
How powerful are graph neural networks?
K. Xu, W. Hu, J. Leskovec, and S. Jegelka · 2019
Later among the works it cites.
The logical expressiveness of graph neural networks
P. Barceló, E.V. Kostylev, M. Monet, J. Pérez, J. Reutter, and J.P. Silva · 2020
Closest in time.
Counting bounded tree depth homomorphisms
M. Grohe · 2020
Closest in time.
Counting bounded tree depth homomorphisms
M. Grohe · 2020
Closest in time.
M. Grohe, P. Schweitzer, and D. Wiebking · 2020
Closest in time.