Binary Search Approach for Largest Cascade Capacity of Complex Networks

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Author(s): Natarajan Meghanathan (Jackson State University, USA)
Copyright: 2023
Pages: 14
Source title: Encyclopedia of Data Science and Machine Learning
Source Author(s)/Editor(s): John Wang (Montclair State University, USA)
DOI: 10.4018/978-1-7998-9220-5.ch150

Keywords: Data Analysis and Statistics / Information Science Reference / Knowledge Management and Library Science / Machine Learning

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Abstract

The cascade capacity of a network is the largest threshold fraction of neighbors that should have made a unanimous decision to result in a complete information cascade. The current approach (based on the intra cluster densities of the bridge nodes of the clusters) used to determine the cascade capacity of a network is independent of the choice of the initial adopters. The authors claim that the above approach gives only a lower bound for the cascade capacity of a network and show that the cascade capacity of a network could be indeed larger if the number and topological positions of the nodes chosen as initial adopters are taken into consideration. In this context, they propose a binary search algorithm that could be used to determine the largest possible cascade capacity of a network for a given set of initial adopters. They run the algorithm on 60 real-world networks of diverse domains and observe the cascade capacity of the networks to increase with the percentage of nodes chosen as initial adopters as well as vary with the choice of the centrality metric used to choose the initial adopters.

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Binary Search Approach for Largest Cascade Capacity of Complex Networks

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