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Frustrated random walks: A fast method to compute node distances on hypergraphs.

Enzhi Li1, Scott Nickleach2, Bilal Fadlallah2

  • 1<a href="https://ror.org/04mv4n011">Amazon</a>, San Diego, California 92122, USA.

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|September 19, 2024
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Summary
This summary is machine-generated.

Calculating hypergraph node distances is crucial for applications like recommender systems. This study introduces frustrated random walks (FRW) for accurate and efficient distance computation, outperforming existing methods for specific use cases.

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Area of Science:

  • Graph theory
  • Network science
  • Data mining

Background:

  • Hypergraphs generalize graphs by allowing edges to connect multiple nodes, representing complex relationships like attribute sharing.
  • Accurate and efficient computation of node distances in hypergraphs is an open challenge with significant implications for various applications.
  • Node distance estimation is vital for tasks such as identifying nearest neighbors in recommender systems and targeted advertising.

Purpose of the Study:

  • To propose a novel method for computing node distances on hypergraphs.
  • To address the limitations of simple random walks on complex hypergraphs.
  • To evaluate the efficiency and accuracy of the proposed method against existing techniques.

Main Methods:

  • Utilizing expected hitting times of random walks for hypergraph node distance calculation.
  • Introducing frustrated random walks (FRW) to improve accuracy on complex hypergraphs.
  • Benchmarking FRW against DeepWalk for performance comparison.

Main Results:

  • Frustrated random walks (FRW) provide accurate hypergraph node distance estimations.
  • FRW demonstrates a significant computational advantage over DeepWalk when the number of target nodes is small.
  • The time complexity of FRW is approximately linear for large, sparse hypergraphs, offering superior scalability.

Conclusions:

  • Frustrated random walks (FRW) offer an effective solution for computing hypergraph node distances.
  • FRW presents a computationally efficient alternative to methods like DeepWalk, particularly for targeted analyses.
  • The proposed method enhances the utility of hypergraphs in applications requiring neighbor identification and relationship analysis.