Efficient Algorithms for Computing Random Walk Centrality

📝 Original Info

• Title: Efficient Algorithms for Computing Random Walk Centrality
• ArXiv ID: 2510.20604
• Date: 2025-10-23
• Authors: ** 정보가 제공되지 않았습니다. (논문에 명시된 저자 정보를 입력해 주세요.) **

📝 Abstract

Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes. Despite its ability to capture rich graph structural information and its wide range of applications, computing this measure for large networks remains impractical due to the computational demands of existing methods. In this paper, we present a novel formulation of random walk centrality, underpinning two scalable algorithms: one leveraging approximate Cholesky factorization and sparse inverse estimation, while the other sampling rooted spanning trees. Both algorithms operate in near-linear time and provide strong approximation guarantees. Extensive experiments on large real-world networks, including one with over 10 million nodes, demonstrate the efficiency and approximation quality of the proposed algorithms.

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