An improved Community-based Greedy algorithm for solving the influence maximization problem in social networks

The influence maximization problem is to find a subset of vertexes that maximize the spread of information in a network. The Community-based Greedy algorithm (CGA) is one of the many that approximates the optimal solution of this problem. This algorithm divides the social network into communities, and then it takes into account for each node only its influence inside the cluster to which it belongs. Our method improves this algorithms with two modifications. We replace the clustering method of the CGA with a commonly used algorithm, namely the Louvain method, which runs by even one magnitude faster. We performed measurements to test how this replacement affects the running time and the precision of the algorithm. The results show that our variant significantly reduces the running time and the precision loss is less than five percent.