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Non-backtracking random walks and a weighted Ihara's theorem

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arxiv 1603.05553 v1 pith:FCBWWRTO submitted 2016-03-17 math.CO

classification math.CO
keywords non-backtrackingrandommatrixwalkwalksgraphsgraphihara
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We study the mixing rate of non-backtracking random walks on graphs by looking at non-backtracking walks as walks on the directed edges of a graph. A result known as Ihara's Theorem relates the adjacency matrix of a graph to a matrix related to non-backtracking walks on the directed edges. We prove a weighted version of Ihara's Theorem which relates the transition probability matrix of a non-backtracking walk to the transition matrix for the usual random walk. This allows us to determine the spectrum of the transition probability matrix of a non-backtracking random walk in the case of regular graphs and biregular graphs. As a corollary, we obtain a result of Alon et. al. that in most cases, a non-backtracking random walk on a regular graph has a faster mixing rate than the usual random walk. In addition, we obtain an analogous result for biregular graphs.

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  1. Tokenphormer: Structure-aware Multi-token Graph Transformer for Node Classification

    cs.LG 2024-12 conditional novelty 6.0 of 10

    Tokenphormer combines walk-tokens, SGPM-tokens, and hop-tokens in a graph transformer and reports improved node classification accuracy on several benchmark graphs.

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