Abstract
Influence maximization aims to find a subset of nodes in social networks and make the propagation of their influence maximized. Usually, greedy algorithms for LT model have long execution time. To solve this problem, based on Three Degrees of Influence Rule (TDIR) we proposed a heuristic algorithm TDIA. We used LT-A model and change the formula of attitude weight in the model by considering the impact of three degrees of influence on attitude. We conducted extensive experiments on two real-world signed social network datasets and the experiment results showed that TDIA has much shorter execution time than LT-A Greedy algorithm and its positive influence spread is close to the greedy algorithm.
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Lei, W., Yang, Q., Wang, H. (2016). Positive Influence Maximization Algorithm Based on Three Degrees of Influence. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_54
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