Abstract
The new generation of social networks contains billions of nodes and edges. Managing and mining this data is a new academic and industrial challenge. Influence maximization is the problem of finding a set of nodes in a social network that result in the highest amount of influence diffusion. Many research works have been developed, which focus exclusively on the efficiency of algorithms, but overlook some features of social network data such as time sensitivity and the practicality in a large scale. Furthermore, the new era of 'big data' is changing dramatically right before our eyes - the increase of big data growth gives all researchers many challenges as well as opportunities. This paper proposes two new models TIC and TLT and considers the time constraint during the influence spreading process in practice. Empirical studies on different synthetic and real large scale social networks demonstrate that our models together with solutions on both Hadoop and Spark platforms are more practical as well as providing a regulatory mechanism for enhancing influence maximization. Not only that but also outperforming most existing alternative algorithms.
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