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We propose a generalized framework for influence maximization in large-scale,time evolving networks. Many real-life influence graphs such as socialnetworks, telephone networks, and IP traffic data exhibit dynamiccharacteristics, e.g., the underlying structure and communication patternsevolve with time. Correspondingly, we develop a dynamic framework for theinfluence maximization problem, where we perform effective local updates toquickly adjust the top-k influencers, as the structure and communicationpatterns in the network change. We design a novel N-Family approach (N=1, 2, 3,...) based on the maximum influence arborescence (MIA) propagation model withapproximation guarantee of (1-1/e). We then develop heuristic algorithms byextending the N-Family approach to other information propagation models (e.g.,independent cascade, linear threshold) and influence maximization algorithms(e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysisover several real-world, dynamic, and large-scale networks, we find that ourproposed solution, N-Family improves the updating time of the top-k influencersby 1-2 orders of magnitude, compared to state-of-the-art algorithms, whileensuring similar memory usage and influence spreads.

Fast Influence Maximization in Dynamic Graphs: A Local Updating Approach