research-article

The skew spectrum of graphs

Authors:

Karsten M. BorgwardtAuthors Info & Claims

ICML '08: Proceedings of the 25th international conference on Machine learning

Pages 496 - 503

https://doi.org/10.1145/1390156.1390219

Published: 05 July 2008 Publication History

Abstract

The central issue in representing graph-structured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We present a new system of invariant graph features which we call the skew spectrum of graphs. The skew spectrum is based on mapping the adjacency matrix of any (weigted, directed, unlabeled) graph to a function on the symmetric group and computing bispectral invariants. The reduced form of the skew spectrum is computable in O(n³) time, and experiments show that on several benchmark datasets it can outperform state of the art graph kernels.

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Shankar STowsley D(2022)Bosonic Random Walk Neural Networks for Graph LearningComplex Networks & Their Applications X10.1007/978-3-030-93413-2_34(401-411)Online publication date: 1-Jan-2022
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The skew spectrum of graphs

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Published In

cover image ACM Other conferences

ICML '08: Proceedings of the 25th international conference on Machine learning

July 2008

1310 pages

ISBN:9781605582054

DOI:10.1145/1390156

General Chair:
William Cohen
Carnegie Mellon University
,
Program Chairs:
Andrew McCallum
University of Massachusetts Amherst
,
Sam Roweis
University of Toronto and Google

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Microsoft Research: Microsoft Research
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Association for Computing Machinery

New York, NY, United States

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Published: 05 July 2008

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ICML '08: The 25th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

July 5 - 9, 2008

Helsinki, Finland

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Adams JPark Gvan der Aalst W(2023)Preserving complex object-centric graph structures to improve machine learning tasks in process miningEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106764125:COnline publication date: 1-Oct-2023
https://dl.acm.org/doi/10.1016/j.engappai.2023.106764
Shankar STowsley D(2022)Bosonic Random Walk Neural Networks for Graph LearningComplex Networks & Their Applications X10.1007/978-3-030-93413-2_34(401-411)Online publication date: 1-Jan-2022
https://doi.org/10.1007/978-3-030-93413-2_34
Yoshida TTakeuchi IKarasuyama M(2021)Distance metric learning for graph structured dataMachine Learning10.1007/s10994-021-06009-3110:7(1765-1811)Online publication date: 16-Jun-2021
https://doi.org/10.1007/s10994-021-06009-3
Chen YDeJong JHalverson TShuman D(2021)Signal Processing on the Permutahedron: Tight Spectral Frames for Ranked Data AnalysisJournal of Fourier Analysis and Applications10.1007/s00041-021-09878-327:4Online publication date: 9-Aug-2021
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Bai LCui LJiao YRossi LHancock E(2020)Learning Backtrackless Aligned-Spatial Graph Convolutional Networks for Graph ClassificationIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.3011866(1-1)Online publication date: 2020
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Yoshida TTakeuchi IKarasuyama MTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Learning Interpretable Metric between GraphsProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330845(1026-1036)Online publication date: 25-Jul-2019
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Dutta ARiba PLladós JFornés A(2019)Hierarchical stochastic graphlet embedding for graph-based pattern recognitionNeural Computing and Applications10.1007/s00521-019-04642-7Online publication date: 6-Dec-2019
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Boroojeny AShrestha ASharifi-Zarchi AGallagher SSahinalp SChitsaz H(2018)GTED: Graph Traversal Edit DistanceResearch in Computational Molecular Biology10.1007/978-3-319-89929-9_3(37-53)Online publication date: 18-Apr-2018
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