Abstract
We live in a world of social networks. Our everyday choices are often influenced by social interactions. Word of mouth, meme diffusion on the Internet, and viral marketing are all examples of how social networks can affect our behaviour. In many practical applications, it is of great interest to determine which nodes have the highest influence over the network, i.e., which set of nodes will, indirectly, reach the largest audience when propagating information. These nodes might be, for instance, the target for early adopters of a product, the most influential endorsers in political elections, or the most important investors in financial operations, just to name a few examples. Here, we tackle the NP-hard problem of influence maximization on social networks by means of a Genetic Algorithm. We show that, by using simple genetic operators, it is possible to find in feasible runtime solutions of high-influence that are comparable, and occasionally better, than the solutions found by a number of known heuristics (one of which was previously proven to have the best possible approximation guarantee, in polynomial time, of the optimal solution). The advantages of Genetic Algorithms show, however, in them not requiring any assumptions about the graph underlying the network, and in them obtaining more diverse sets of feasible solutions than current heuristics.
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Notes
- 1.
For any node n, we denote by in-degree(n) the number of edges incoming to n, and by out-degree(n) the number of edges outgoing from n. Unlike some of the related literature, which works with undirected rather than directed graphs, in our algorithms we make the distinction between the two degree counts explicit.
- 2.
This number of repetitions was chosen as a practical compromise between the confidence interval that it affords, and the overall computational complexity of the Genetic Algorithm. With regards to the accuracy of the fitness estimation, 100 simulation repetitions give a 95 % confidence interval for the average in the approximate range of [3, 10] nodes influenced, for all our experiments. Increasing the number of repetitions to 10000 would give a 95 % confidence interval for the average that is \(\le 1\) nodes influenced in all experimental cases, but requires far longer runtimes.
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Bucur, D., Iacca, G. (2016). Influence Maximization in Social Networks with Genetic Algorithms. In: Squillero, G., Burelli, P. (eds) Applications of Evolutionary Computation. EvoApplications 2016. Lecture Notes in Computer Science(), vol 9597. Springer, Cham. https://doi.org/10.1007/978-3-319-31204-0_25
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DOI: https://doi.org/10.1007/978-3-319-31204-0_25
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