A neighbour scale fixed approach for influence maximization in social networks Influence maximization is currently a most extensively researched topic in social network analysis. Existing approaches tackle this task by either pursuing the real influence strength of a node or designing proper measurements for estimating it. The degree is a popularly adopted influence strength metric, based on which a variety of methods have been developed. Though with good efficiency, degreebased methods suffer unsatisfactory accuracy since this metric only covers a limited considered scale over the whole network of interest and also lacks discriminatory power. In this paper, we propose a novel influence maximization method, named Fixed Neighbour Scale (FNS), which extracts useful information from multiple levels of neighbours for a target node to estimate its influence strength, rather than only considering directly connected neighbours as in degreebased methods. To facilitate the implementation of FNS, we also present a centrality measurement termed FNSdist, which estimates a nodexe2x80x99s influence strength by summing its multilevel neighboursxe2x80x99 weights that are mainly determined by their distances to the target node. Experiments conducted on nine networks of different sizes and categories show that the proposed FNS method achieves excellent and stable performance compared with other algorithms based on designing metrics for measuring influence strength. We also exhibit that FNSdist is a superior alternative centrality which is more proper and precise than the degree.

Targeted Influence Maximization under a MultifactorBased Information Propagation Model Abstract   Information propagation modeling and influence maximization are two important research problems in viral marketing. When marketing information is given, how can the seed nodes be efficiently identified to maximize the spread of the information through the network? To answer this question, we consider multiple factors in information propagation, such as information content, social influence and user authority, and propose a multifactorbased information propagation model (MFIP). Then, we utilize the firstorder influence of the nodes to approximate their influence and propose an efficient heuristic algorithm named weighted degree decrease (WDD) to select the seed nodes under the MFIP model. Experimental evaluations with four realworld social network datasets demonstrate the effectiveness and efficiency of our algorithm.