Abstract
Identifying a set of influential spreaders in complex networks plays a crucial role in effective information spreading. A simple strategy is to choose top-r ranked nodes as spreaders according to influence ranking method such as PageRank, ClusterRank and k-shell decomposition. Besides, some heuristic methods such as hill-climbing, SPIN, degree discount and independent set based are also proposed. However, these approaches suffer from a possibility that some spreaders are so close together that they overlap sphere of influence or time consuming. In this report, we present a simply yet effectively iterative method named VoteRank to identify a set of decentralized spreaders with the best spreading ability. In this approach, all nodes vote in a spreader in each turn, and the voting ability of neighbors of elected spreader will be decreased in subsequent turn. Experimental results on four real networks show that under Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models, VoteRank outperforms the traditional benchmark methods on both spreading rate and final affected scale. What’s more, VoteRank has superior computational efficiency.
Highlights
Many complex systems can be represented as complex networks[1, 2, 3, 4, 5, 6], in which, Many activities such as advertising over media and word-of-mouth on social networks can be described by information spreading on complex networks[5, 7, 8, 9, 10, 11]
We propose a yet effectively iterative method named VoteRank to choose a set of influential spreaders
It can be seen that from the source spreaders obtained by VoteRank, the information will spread faster than that from other methods
Summary
Many complex systems can be represented as complex networks[1, 2, 3, 4, 5, 6], in which, Many activities such as advertising over media and word-of-mouth on social networks can be described by information spreading on complex networks[5, 7, 8, 9, 10, 11]. Maximizing the scale of spreading is a common target. If a market manager want to advertise a new product on Twitter.com, she/he tries to choose a small number of users to provide them with free products in exchange for posting tweets about the product to influence their friends to buy the products. The task of market manager is to choose a few users such that the product information can be transmitted to more users and, more products can be sold . With the topology unchanged or changed slightly, the location of source spreaders determines the final scale of spreading on large degree. The problem of choosing initial nodes as source spreaders to achieve maximum scale of spreading is defined as influence maximization problem [12]. Our research focuses on the strategy of choosing a set of critical nodes as source spreaders in this report
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