Identifying a set of influential spreaders in complex networks

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.

Dynamic Rankings for Seed Selection in Complex Networks: Balancing Costs and Coverage

“…Recent studies optimised the usage of structural measures to refrain from selecting nodes in the same segments of network for better allocation of seeds. Solutions of this type are based on sequential seeding for better usage of natural diffusion processes [28], targeting communities to avoid seeding of nodes within the same communities with close intra connections [29] and a usage of voting mechanisms with decreased weights after detection of already activated nodes [30]. In other studies, a k-shell based approach was implemented in order to detect central nodes within the networks [31].…”

“…Its initial degree D = 6 from the beginning of the process is not taken into account. Nodes 28,29,30,25,24 are activated in natural process; (B2) stage 3 of the process with nodes 11, 6, 21, 16, 22, 19, 18 and 3 is activated in natural process and node 27 is selected as a seed. Newly selected seed activates nodes 2 and 4 with propagation probability PP = 1 and as a result all nodes in network are activated within assumed three stages and with the use of three seeds.…”

Multi-criteria decision support for planning and evaluation of performance of viral marketing campaigns in social networks

“…These types of solutions are based more on better use of processes of natural diffusion and use sequential seeding [27], avoid nodes from within the same communities with intra connections that are close by using target communities [28], use dynamic rankings with sequential seeding [29] and use mechanisms for voting that have lower weights once activated nodes have been detected [30]. Apart from basic centrality measures, the central nodes in networks can be detected using a k-shell based approach [31].…”

Seed Selection for Information Cascade in Multilayer Networks

“…Influential users or, activation seeds were selected using three network based state of the art methods Degree Centrality [1], K-Shell [9], and VoteRank [15], together with a machine learning method, ARL [7] as an efficient and accurate method for ranking users on social networks. We also included a Random sample of seeds as a baseline.…”