Spreading to localized targets in complex networks

Abstract

As an important type of dynamics on complex networks, spreading is widely used to model many real processes such as the epidemic contagion and information propagation. One of the most significant research questions in spreading is to rank the spreading ability of nodes in the network. To this end, substantial effort has been made and a variety of effective methods have been proposed. These methods usually define the spreading ability of a node as the number of finally infected nodes given that the spreading is initialized from the node. However, in many real cases such as advertising and news propagation, the spreading only aims to cover a specific group of nodes. Therefore, it is necessary to study the spreading ability of nodes towards localized targets in complex networks. In this paper, we propose a reversed local path algorithm for this problem. Simulation results show that our method outperforms the existing methods in identifying the influential nodes with respect to these localized targets. Moreover, the influential spreaders identified by our method can effectively avoid infecting the non-target nodes in the spreading process.

Competition‐Based Benchmarking of Influence Ranking Methods in Social Networks

“…As spreading is used to model real-world processes such as epidemic contagion and information propagation [2,3,20,22,63], our paper aims to improve current methodology in validating and comparing state-of-the-art ranking methods in the social network context. Numerous alternative ranking Figure 4: Coverage performance (0-100%) of each ranking method cumulated over all synthetic, respectively, all real-world datasets.…”

“…Higher differences are better. 9 Complexity methods have been developed, relying on classic graph centralities, localized targets [63], optimal percolation [43], and so on. While the challenge at hand remains partially unsolved, it is argued that insights are uncovered only through the optimal collective interplay of all the influencers in a network [43].…”

“…Accordingly, alternate opinion assigning (AOA) must be modified to fit the k opinion sources. The recent study discusses the importance of targeting specific localized targets, rather than obtaining a high coverage of the network [63]. Our method can be easily implemented to measure the target coverage during or at the end of a spreading simulation.…”

Structure-oriented prediction in complex networks

“…Therefore, it is necessary to study the spreading influence of nodes toward localized targets in complex networks. A reversed local path algorithm [72] is devised for this problem. The basic idea is inspired by computing the paths up to length 3 starting from the target nodes to other nodes.…”

A geo-computational algorithm for exploring the structure of diffusion progression in time and space

“…They attempted to link epidemic models with spatial theory and had some success in revealing underlying mechanisms of movement of disease through time and space. Aside from modeling diffusion from space-time characteristics, recent studies have used graph theory and complex network analysis to explicitly model relationships between the events 26 – 28 . Transmission relationships were modeled at various scales of networks, which included individual social networks 29 – 31 , meta-population and sub-population networks 32 , 33 , buildings network 34 , and cities or countries networks 35 , 36 , by converting the objects of study into nodes and the contacts or interactions between them into links.…”