research-article

Learning spectral graph transformations for link prediction

Authors:

Jérôme Kunegis,

Andreas LommatzschAuthors Info & Claims

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

Pages 561 - 568

https://doi.org/10.1145/1553374.1553447

Published: 14 June 2009 Publication History

Abstract

We present a unified framework for learning link prediction and edge weight prediction functions in large networks, based on the transformation of a graph's algebraic spectrum. Our approach generalizes several graph kernels and dimensionality reduction methods and provides a method to estimate their parameters efficiently. We show how the parameters of these prediction functions can be learned by reducing the problem to a one-dimensional regression problem whose runtime only depends on the method's reduced rank and that can be inspected visually. We derive variants that apply to undirected, weighted, unweighted, unipartite and bipartite graphs. We evaluate our method experimentally using examples from social networks, collaborative filtering, trust networks, citation networks, authorship graphs and hyperlink networks.

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Cited By

Yu YTosyali ABaek JJeong M(2024)A Novel Similarity-Based Link Prediction Approach for Transaction NetworksIEEE Transactions on Engineering Management10.1109/TEM.2022.314603771(981-992)Online publication date: 2024
https://doi.org/10.1109/TEM.2022.3146037
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Published In

cover image ACM Other conferences

ICML '09: Proceedings of the 26th Annual International Conference on Machine Learning

June 2009

1331 pages

ISBN:9781605585161

DOI:10.1145/1553374

General Chair:
Andrea Danyluk
Williams College
,
Program Chairs:
Léon Bottou
NEC Laboratories America
,
Michael Littman
Rutgers University

Copyright © 2009 Copyright 2009 by the author(s)/owner(s).

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NSF
Microsoft Research: Microsoft Research
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 June 2009

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Sponsor:

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ICML '09: The 26th Annual International Conference on Machine Learning held in conjunction with the 2007 International Conference on Inductive Logic Programming

June 14 - 18, 2009

Quebec, Montreal, Canada

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Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Yu YTosyali ABaek JJeong M(2024)A Novel Similarity-Based Link Prediction Approach for Transaction NetworksIEEE Transactions on Engineering Management10.1109/TEM.2022.314603771(981-992)Online publication date: 2024
https://doi.org/10.1109/TEM.2022.3146037
Hu ZSun JLi ZSun G(2024)PckGNN: Optimizing Aggregation Operators with Packing Strategies in Graph Neural Networks2024 IEEE International Parallel and Distributed Processing Symposium (IPDPS)10.1109/IPDPS57955.2024.00010(2-13)Online publication date: 27-May-2024
https://doi.org/10.1109/IPDPS57955.2024.00010
Xiao GXia LChen YYang W(2024)PUSHGNN: A Low-communication Runtime System for GNN Acceleration on Multi-GPUs2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825530(4078-4085)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825530
Kovács BKojaku SPalla GFortunato S(2024)Iterative embedding and reweighting of complex networks reveals community structureScientific Reports10.1038/s41598-024-68152-w14:1Online publication date: 26-Jul-2024
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Kojaku SRadicchi FAhn YFortunato S(2024)Network community detection via neural embeddingsNature Communications10.1038/s41467-024-52355-w15:1Online publication date: 1-Nov-2024
https://doi.org/10.1038/s41467-024-52355-w
Lou XLiu GLi J(2024)Heterogeneous graph neural network with graph-data augmentation and adaptive denoisingApplied Intelligence10.1007/s10489-024-05363-854:5(4411-4424)Online publication date: 26-Mar-2024
https://doi.org/10.1007/s10489-024-05363-8
Yin PYan XZhou JFu QCai ZCheng JTang BWang MSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)DGI: An Easy and Efficient Framework for GNN Model EvaluationProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599805(5439-5450)Online publication date: 6-Aug-2023
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Guo JDu LChen XMa XFu QHan SZhang DZhang YSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative FilteringProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599450(602-613)Online publication date: 6-Aug-2023
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Jiang XZhu RJi PLi S(2023)Co-Embedding of Nodes and Edges With Graph Neural NetworksIEEE Transactions on Pattern Analysis and Machine Intelligence10.1109/TPAMI.2020.302976245:6(7075-7086)Online publication date: 1-Jun-2023
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