graphs-6

Hypergraph Matching

3260 papers • 126 benchmarks • 313 datasets

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Most implemented papers

Ranking with Fairness Constraints

L. E. Celis, D. Straszak, Nisheeth K. Vishnoi•Fri Apr 21 2017

This work studies the following variant of the traditional ranking problem when the objective satisfies properties that appear in common ranking metrics such as Discounted Cumulative Gain, Spearman's rho or Bradley-Terry.

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Simplicial Complex based Point Correspondence between Images warped onto Manifolds

Charu Sharma, Manohar Kaul•Sat Jul 04 2020

This paper proposes a constrained quadratic assignment problem (QAP) that matches each p-skeleton of the simplicial complexes, iterating from the highest to the lowest dimension, and significantly outperform existing state-of-the-art spherical matching methods on a diverse set of datasets.

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Hypergraph Neural Networks for Hypergraph Matching

Haibin Ling, Yong Xu, Xiaowei Liao•Thu Sep 30 2021

Hypergraph matching is a useful tool to find feature correspondence by considering higher-order structural information. Recently, the employment of deep learning has made great progress in the matching of graphs, suggesting its potential for hypergraphs. Hence, in this paper, we present the first, to our best knowledge, unified hypergraph neural network (HNN) solution for hypergraph matching. Specifically, given two hypergraphs to be matched, we first construct an association hypergraph over them and convert the hypergraph matching problem into a node classification problem on the association hypergraph. Then, we design a novel hypergraph neural network to effectively solve the node classification problem. Being end-to-end trainable, our proposed method, named HNN-HM, jointly learns all its components with improved optimization. For evaluation, HNN-HM is tested on various benchmarks and shows a clear advantage over state-of-the-arts.

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