3 research outputs found
Large-scale multi-objective influence maximisation with network downscaling
Finding the most influential nodes in a network is a computationally hard
problem with several possible applications in various kinds of network-based
problems. While several methods have been proposed for tackling the influence
maximisation (IM) problem, their runtime typically scales poorly when the
network size increases. Here, we propose an original method, based on network
downscaling, that allows a multi-objective evolutionary algorithm (MOEA) to
solve the IM problem on a reduced scale network, while preserving the relevant
properties of the original network. The downscaled solution is then upscaled to
the original network, using a mechanism based on centrality metrics such as
PageRank. Our results on eight large networks (including two with 50k
nodes) demonstrate the effectiveness of the proposed method with a more than
10-fold runtime gain compared to the time needed on the original network, and
an up to time reduction compared to CELF
Improving Multi-objective Evolutionary Influence Maximization in Social Networks
In the context of social networks, maximizing influence means contacting the largest possible number of nodes starting from a set of seed nodes, and assuming a model for influence propagation. The real-world applications of influence maximization are of uttermost importance, and range from social studies to marketing campaigns. Building on a previous work on multi-objective evolutionary influence maximization, we propose improvements that not only speed up the optimization process considerably, but also deliver higher-quality results. State-of-the-art heuristics are run for different sizes of the seed sets, and the results are then used to initialize the population of a multi-objective evolutionary algorithm. The proposed approach is tested on three publicly available real-world networks, where we show that the evolutionary algorithm is able to improve upon the solutions found by the heuristics, while also converging faster than an evolutionary algorithm started from scratch