A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure

Abstract

In complex networks, it is of great theoretical and practical significance to identify a set of critical spreaders which help to control the spreading process. Some classic methods are proposed to identify multiple spreaders. However, they sometimes have limitations for the networks with community structure because many chosen spreaders may be clustered in a community. In this paper, we suggest a novel method to identify multiple spreaders from communities in a balanced way. The network is first divided into a great many super nodes and then k spreaders are selected from these super nodes. Experimental results on real and synthetic networks with community structure show that our method outperforms the classic methods for degree centrality, k-core and ClusterRank in most cases.

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].…”

Identification of top-K nodes in large networks using Katz centrality

“…Other works include that of He et al [19], where top-K nodes are identified using influence maximization strategies in complex networks using community structure and in Liu et al [20] propose a method to identify the top-K nodes based on a specific topic.…”