Betweenness Centrality : Algorithms and Lower Bounds
Shiva Kintali

TL;DR
This paper introduces a randomized parallel algorithm and an algebraic approach for computing betweenness centrality in large networks, along with a lower bound proof for path-comparison algorithms.
Contribution
It presents novel algorithms for betweenness centrality computation and establishes a theoretical lower bound on their complexity.
Findings
Proposed a randomized parallel algorithm for betweenness centrality.
Developed an algebraic method for the same problem.
Proved a lower bound of O(nm) for path-comparison based algorithms.
Abstract
One of the most fundamental problems in large scale network analysis is to determine the importance of a particular node in a network. Betweenness centrality is the most widely used metric to measure the importance of a node in a network. In this paper, we present a randomized parallel algorithm and an algebraic method for computing betweenness centrality of all nodes in a network. We prove that any path-comparison based algorithm cannot compute betweenness in less than O(nm) time.
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Topological and Geometric Data Analysis
