Abstract
Abstract Social networks are one the main sources of information transmission nowadays. However, not all nodes in social networks are equal: in fact, some nodes are more influential than others, i.e., their information tends to spread more. Finding the most influential nodes in a network – the so-called Influence Maximization problem – is an NP-hard problem with great social and economical implications. Here, we introduce a framework based on Evolutionary Algorithms that includes various graph-aware techniques (spread approximations, domain-specific operators, and node filtering) that facilitate the optimization process. The framework can be applied straightforwardly to various social network datasets, e.g., those in the SNAP repository.
Highlights
Social networks are one the main sources of information transmission nowadays
We introduce a framework based on Evolutionary Algorithms that includes various graph-aware techniques that facilitate the optimization process
The fitness of a candidate solution can be estimated by Monte-Carlo simulations of probabilistic spread models, but the framework includes advanced graph-aware techniques such as approximations of the spread models, domain-specific initialization and mutations of candidate solutions, and node filtering
Summary
An evolutionary framework for maximizing influence propagation in social networks Giovanni Iacca a,∗, Kateryna Konotopska a, Doina Bucur b, Alberto Tonda c a Department of Information Engineering and Computer Science, University of Trento, Via Sommarive 9, 38123 Povo, Trento, Italy b Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Zilverling, Hallenweg 19, 7522NH Enschede, The Netherlands c INRA, UMR 782 GMPA, Avenue Lucien Brétignières, 78850 Thiverval-Grignon, France
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