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Social Network Analysis and Mining

Erschienen in:

01.12.2015 | Original Article

Towards intelligent control of influence diffusion in social networks

verfasst von: Andrew Runka, Tony White

Erschienen in: Social Network Analysis and Mining | Ausgabe 1/2015

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Abstract

Control of the flow of information in large-scale non-deterministic social networks is a complex problem requiring both a search for the optimal connection of a control system to the network, and a means of determining the required control signals. This paper formalizes the Network Control Problem (NCP) as a means of relating the field of diverse social network control subproblems. Additionally, this paper defines a novel NCP subproblem, the \(\theta \)-Consensus Avoidance Problem (\(\theta \)-CAP), as a next step towards solving the general NCP. Benchmark results for the \(\theta \)-CAP using Artificial Neural Networks, Evolutionary Neural Networks, and heuristic methods are presented, and interesting areas of the problem space are identified.

Vorheriger Artikel GLMLE: graph-limit enabled fast computation for fitting exponential random graph models to large social networks

Nächster Artikel Name disambiguation from link data in a collaboration graph using temporal and topological features

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Titel: Towards intelligent control of influence diffusion in social networks
verfasst von: Andrew Runka
Tony White
Publikationsdatum: 01.12.2015
Verlag: Springer Vienna
Erschienen in: Social Network Analysis and Mining / Ausgabe 1/2015
Print ISSN: 1869-5450
Elektronische ISSN: 1869-5469
DOI: https://doi.org/10.1007/s13278-015-0248-2

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