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Social Network Analysis and Mining

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01.12.2019 | Original Article

A gradient-based methodology for optimizing time for influence diffusion in social networks

verfasst von: Jyoti Sunil More, Chelpa Lingam

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2019

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Abstract

In social network analysis, one of the significant problems is finding the most influential entities within the network, which has proved to be NP-hard. The problem of influence maximization in a social network is an optimization problem that ensures that the spread of influence in the network is maximized. Although many algorithms have been proposed for influence maximization, most of them provide influence spread, at the cost of execution time. Therefore, a novel methodology based on gradient approach is proposed in this paper to deal with the problem. This approach provides a balance between influence spread and execution time. In this research, the performance of the proposed algorithm has been compared with existing algorithms and observations of a better influence spread per second are presented. This task has significance in viral marketing, since the most influential entities can be targeted for endorsing new products in the market at a faster rate.

Vorheriger Artikel ParSoDA: high-level parallel programming for social data mining

Nächster Artikel Diachronic equivalence: an examination of the international news network

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Titel: A gradient-based methodology for optimizing time for influence diffusion in social networks
verfasst von: Jyoti Sunil More
Chelpa Lingam
Publikationsdatum: 01.12.2019
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2019
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-018-0548-4

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