Harvester: Influence Optimization in Symmetric Interaction Networks

The problem of optimizing influence diffusion in a network has applications in areas such as marketing, disease control, social media analytics, and more. In all cases, an initial set of influencers are chosen so as to optimize influence propagation. While a lot of research has been devoted to the influence maximization problem, most solutions proposed to date apply on directed networks, considering the undirected case to be solvable as a special case. In this paper, we propose a novel algorithm, Harvester, that achieves results of higher quality than the state of the art on symmetric interaction networks, leveraging the particular characteristics of such networks. Harvester is based on the aggregation of instances of live-edge graphs, from which we compute the influence potential of each node. We show that this technique can be applied for both influence maximization under a known seed size and also for the dual problem of seed minimization under a target influence spread. Our experimental study with real data sets demonstrates that: (a) Harvester outperforms the state-of-the-art method, IMM, in terms of both influence spread and seed size; and (b) its variant for the seed minimization problem yields good seed size estimates, reducing the number of required trial influence spread estimations by a factor of two; and (c) it is scalable with growing graph size and robust to variant edge influence probabilities.