Social influence determination on big data streams in an online social network

摘要

Social networks have become a good place to promote products and also to campaign for causes. Maximizing the spread of information in an online social network at a least cost has attracted the attention of publicist's. In general, influence user ranking methods are derived either by a network's topological features or by user features but not both. Existing Influence Maximization Problem (IMP) operates as a modification of greedy algorithms that cannot scale streaming data. Which are time consuming and cannot handle large networks because it requires heavy Monte-Carlo simulation. This is also an NP hard problem in both linear threshold and independent cascade models. Our proposed work aims to address IMP through a Rank-based sampling approach in the Map-Reduce environment. This novel technique combines user and topological features of the network enabling it to handle real-time streaming data. Our experiment of influenced rank-based sampling approach to influence maximization is compared to the greedy approach with and without sampling that exhibits an accuracy of 82%. Performance analysis in terms of running time is reduced from O(n (3)) to O(k n). Where 'k' is the size of the sample dataset and 'n' is the number of user's.