Multi-objective Evolutionary Algorithms for Influence Maximization in Social Networks

As the pervasiveness of social networks increases, new NP-hard related problems become interesting for the optimization community. The objective of influence maximization is to contact the largest possible number of nodes in a network, starting from a small set of seed nodes, and assuming a model for information propagation. This problem is of utmost practical importance for applications ranging from social studies to marketing. The influence maximization problem is typically formulated assuming that the number of the seed nodes is a parameter. Differently, in this paper, we choose to formulate it in a multi-objective fashion, considering the minimization of the number of seed nodes among the goals, and we tackle it with an evolutionary approach. As a result, we are able to identify sets of seed nodes of different size that spread influence the best, providing factual data to trade-off costs with quality of the result. The methodology is tested on two real-world case studies, using two different influence propagation models, and compared against state-of-the-art heuristic algorithms. The results show that the proposed approach is almost always able to outperform the heuristics.

[1]  Pablo Moscato,et al.  Handbook of Memetic Algorithms , 2011, Studies in Computational Intelligence.

[2]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[3]  Éva Tardos,et al.  Maximizing the Spread of Influence through a Social Network , 2015, Theory Comput..

[4]  Azadeh Iranmehr,et al.  Trust Management for Semantic Web , 2009, 2009 Second International Conference on Computer and Electrical Engineering.

[5]  Jing Liu,et al.  Community Detection from Signed Social Networks Using a Multi-objective Evolutionary Algorithm , 2015 .

[6]  Jing Liu,et al.  A Multiobjective Evolutionary Algorithm Based on Similarity for Community Detection From Signed Social Networks , 2014, IEEE Transactions on Cybernetics.

[7]  Giovanni Squillero,et al.  Evolving assembly programs: how games help microprocessor validation , 2005, IEEE Transactions on Evolutionary Computation.

[8]  Clara Pizzuti,et al.  A Multiobjective Genetic Algorithm to Find Communities in Complex Networks , 2012, IEEE Transactions on Evolutionary Computation.

[9]  Giovanni Squillero,et al.  MicroGP—An Evolutionary Assembly Program Generator , 2005, Genetic Programming and Evolvable Machines.

[10]  Giovanni Squillero,et al.  A Framework for Automated Detection of Power-related Software Errors in Industrial Verification Processes , 2010, J. Electron. Test..

[11]  Jon Kleinberg,et al.  Maximizing the spread of influence through a social network , 2003, KDD '03.

[12]  Giovanni Squillero,et al.  Operator Selection using Improved Dynamic Multi-Armed Bandit , 2015, GECCO.

[13]  Byung Ro Moon,et al.  Multiobjective evolutionary algorithms for dynamic social network clustering , 2010, GECCO '10.

[14]  Wei Chen,et al.  Scalable influence maximization for independent cascade model in large-scale social networks , 2012, Data Mining and Knowledge Discovery.

[15]  Wei Chen,et al.  Efficient influence maximization in social networks , 2009, KDD.

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Gao Cong,et al.  Simulated Annealing Based Influence Maximization in Social Networks , 2011, AAAI.

[18]  R. K. Ursem.  Multi-objective Optimization using Evolutionary Algorithms , 2009 .

[19]  Jacob Goldenberg,et al.  Talk of the Network: A Complex Systems Look at the Underlying Process of Word-of-Mouth , 2001 .

[20]  Jure Leskovec,et al.  {SNAP Datasets}: {Stanford} Large Network Dataset Collection , 2014 .

[21]  Doina Bucur,et al.  Influence Maximization in Social Networks with Genetic Algorithms , 2016, EvoApplications.

[22]  Doina Bucur,et al.  The impact of topology on energy consumption for collection tree protocols: An experimental assessment through evolutionary computation , 2014, Appl. Soft Comput..

[23]  Doina Bucur,et al.  The tradeoffs between data delivery ratio and energy costs in wireless sensor networks: a multi-objectiveevolutionary framework for protocol analysis , 2014, GECCO.

[24]  Giovanni Squillero,et al.  Bayesian Network Structure Learning from Limited Datasets through Graph Evolution , 2012, EuroGP.