A matrix alignment approach for link prediction

Jerry Scripps; Pang-Ning Tan; Feilong Chen; Abdol-Hossein Esfahanian

doi:10.1109/ICPR.2008.4761444

ICPR 2008 19th International Conference on Pattern Recognition

A matrix alignment approach for link prediction

Year: 2008

DOI Bookmark: 10.1109/ICPR.2008.4761444

Authors

Jerry Scripps, Computer Science and Engineering, Michigan State University, E. Lansing, 48824, USA
Pang-Ning Tan, Computer Science and Engineering, Michigan State University, E. Lansing, 48824, USA
Feilong Chen, Computer Science and Engineering, Michigan State University, E. Lansing, 48824, USA
Abdol-Hossein Esfahanian, Computer Science and Engineering, Michigan State University, E. Lansing, 48824, USA

Abstract

This paper introduces a new discriminative learning technique for link prediction based on the matrix alignment approach. Our algorithm automatically determines the most predictive features of the link structure by aligning the adjacency matrix of a network with weighted similarity matrices computed from node attributes and neighborhood topological features. Experimental results on a variety of network data have demonstrated the effectiveness of this approach.

Like what you’re reading?

Already a member?

Get this article FREE with a new membership!

Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
2009 IEEE International Conference on Data Mining Workshops
Link prediction using matrix factorization with bagging
2016 IEEE/ACIS 15th International Conference on Computer and Information Science (ICIS)
Link prediction approach to collaborative filtering
Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries
A Matrix Alignment Approach for Collective Classification
2009 International Conference on Advances in Social Network Analysis and Mining
Compressed Adjacency Matrices: Untangling Gene Regulatory Networks
IEEE Transactions on Visualization & Computer Graphics
An Approach to Cold-Start Link Prediction: Establishing Connections between Non-Topological and Topological Information
IEEE Transactions on Knowledge & Data Engineering
Visual Matrix Clustering of Social Networks
IEEE Computer Graphics and Applications
Temporal Link Prediction for Cancer Networks using Structural Consistency Regularized Non-negative Matrix Factorization
2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Non-Negative Matrix Factorization for Link Prediction Preserving Row and Column Spaces
2023 IEEE International Conference on Data Mining (ICDM)
Method for Estimating the Eigenvectors of a Scaled Laplacian Matrix Using the Resonance of Oscillation Dynamics on Networks
2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)

A matrix alignment approach for link prediction

Authors

Abstract

Related Articles