Abstract
Influence maximization is an important problem, which seeks a small set of key users who spread the influence widely into the network. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. The current studies treat nodes in social network with equal weights, and the influence possibility mainly decide by node degree. In this paper, we study the influence maximization problem in social networks and we improve the independent cascade model to realize the goal of different weights for different users, and the differentiation of influence probability. Meanwhile, We take advantage of the community structure to speed up the algorithm. Then, we propose a method called the reverse reachable index method based on random walk (RSRW) to select potential high-impact nodes from those communities. The experimental result on four actual data set shows that these improvements can greatly reduce the calculation time while ensuring the accuracy of the results.
Highlights
In recent years, social networks, such as Weibo, Twitter and Wechat, have connected all web users in the Internet and received great attention from all over the world
Meantime, based on the community structure of network, we propose a reverse reachable method based on random walk(RSRW) to select the high influential node from the community join to the candidate seed set, and we use classical greedy algorithm to select the top-k influential node as seeds and output
1) We propose a method for influence maximization in social network, which utilizing the community structure of social network and combine the benefit of greedy algorithm and heuristic algorithm
Summary
Social networks, such as Weibo, Twitter and Wechat, have connected all web users in the Internet and received great attention from all over the world. F. Cai et al.: CBIM-RSRW: Community-Based Method for Influence Maximization in Social Network network to leverage the relation between the spread of cascades and the community structure of social networks. 1) We propose a method for influence maximization in social network, which utilizing the community structure of social network and combine the benefit of greedy algorithm and heuristic algorithm. 2) We improve the traditional independent cascade model, Different weights are given to nodes according to their importance in social network. 3) We propose a method callde RSRW to select the potential high influence nodes from the community or social network.
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