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Hyperedge Prediction Using Tensor Eigenvalue Decomposition

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Journal of the Indian Institute of Science Aims and scope

Abstract

Link prediction in graphs is studied by modeling the dyadic interactions among two nodes. The relationships can be more complex than simple dyadic interactions and could require the user to model super-dyadic associations among nodes. Such interactions can be modeled using a hypergraph, which is a generalization of a graph where a hyperedge can connect more than two nodes. In this work, we consider the problem of hyperedge prediction in a k-uniform hypergraph. We utilize the tensor-based representation of hypergraphs and propose a novel interpretation of the tensor eigenvectors. This is further used to propose a hyperedge prediction algorithm. The proposed algorithm utilizes the Fiedler eigenvector computed using tensor eigenvalue decomposition of hypergraph Laplacian. The Fiedler eigenvector is used to evaluate the construction cost of new hyperedges, which is further utilized to determine the most probable hyperedges to be constructed. The functioning and efficacy of the proposed method are illustrated using some example hypergraphs and a few real datasets. The code for the proposed method is available on https://github.com/d-maurya/hypred_tensorEVD.

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Notes

  1. https://github.com/d-maurya/hypred_tensorEVD.

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Acknowledgements

This work was partially supported by Intel research Grant RB/18-19/CSE/002/INTI/BRAV to BR.

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Authors and Affiliations

  1. Computer Science and Engineering Department, Robert Bosch Centre for Data Science and AI, Indian Institute of Technology Madras, Chennai, India

    Deepak Maurya & Balaraman Ravindran

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  1. Deepak Maurya
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  2. Balaraman Ravindran
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Correspondence to Deepak Maurya.

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Maurya, D., Ravindran, B. Hyperedge Prediction Using Tensor Eigenvalue Decomposition. J Indian Inst Sci 101, 443–453 (2021). https://doi.org/10.1007/s41745-021-00225-5

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