Abstract
Inspired by lots of applications like viral marketing of products and transmitting information in a network, ranking the spreading ability of nodes in the network has been widely studied. At present, the above problem is mostly studied on unsigned networks which only contain positive relationships (e.g., friend or trust) between users. In real-world networks, there usually exist both positive relationships and negative relationships (e.g., foe or distrust) between users. Based on this, we aim to find the influential spreaders in a signed network which meet the requirement of real scene. Moreover, when the spreading only aims to affect a specific group of nodes instead of all nodes, such as promoting cigarette, a new problem called localized targets spreading problem was come up with. Localized targets spreading problem has been studied on unsigned networks, but it is still open for signed networks. Thus, in this paper, we propose a new method, called local influence matrix (LIM) method, which aims to find the seed nodes set with maximum positive influence on a specific group of targets but with minimum influence on the non-target nodes in signed social networks. Simulation results show that our method performs well on real networks.
Highlights
In recent years, a variety of attention has been paid to investigating the spreading ability of nodes in complex networks
There are numerous studies having been done on this issue and a series of methods have been proposed, such as degree centrality (DC) [1], betweenness centrality (BC) [2] and k-shell decomposition (KS) [3] etc
The top k nodes under the five ranking methods are selected to be the seeds, whose infecting ability are tested by the Independent Cascade (IC)-S model
Summary
A variety of attention has been paid to investigating the spreading ability of nodes in complex networks. There are numerous studies having been done on this issue and a series of methods have been proposed, such as degree centrality (DC) [1], betweenness centrality (BC) [2] and k-shell decomposition (KS) [3] etc. In addition to these famous methods, many researchers proposed other novel methods [4, 5]. If the information is passed from the node with maximum out-degree to the targets, the seed node is very likely to lose its spreading ability during the propagation process. The Localized Targets in Signed Networks following problem comes up naturally: how to identify the most influential nodes in a network which can activate the given localized targets as often as possible while activating the nontarget nodes as little as possible?
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