282 / 1971-01-01 00:00:00

Interest Based Influence Maximization Propagation On Micro-blogs Social Networks

5762,5761,5219

Influence maximization problem targets finding a set of K nodes which can produce the maximum influence range. Almost all of the previous works adopt the same information propagation model which uses a uniform active probability. However, because social network actually show the hobby of people in reality, users tend to forward the micro-blogs they interested in and discard those they do not care. So the traditional solutions of the influence maximization problem will be not accurate without taking the interest of users into account. To solve this problem, we improved the information propagation model based on the Independent Cascade model. According the interest degree of users on specific topic, it would prune the redundancy edges in the graph of social networks. Then we gave a novel IB Greedy algorithm to solve the influence maximization problem. The evaluation was carried on the data-sets which is collected through our crawling from the Tencent Weibo social network. Experiment results showed that the IB_Greedy performs better than the traditional Greedy algorithm and Random algorithm.