An evolutionary framework for maximizing influence propagation in social networks

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, domainspecific 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.