Deprecation based greedy strategy for target set selection in large scale social networks

2015 ◽  
Vol 316 ◽  
pp. 107-122 ◽  
Author(s):  
Suman Kundu ◽  
Sankar K. Pal
Algorithms ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 32 ◽  
Author(s):  
Miriam Di Ianni ◽  
Giovanna Varricchio

It is well-documented that social networks play a considerable role in information spreading. The dynamic processes governing the diffusion of information have been studied in many fields, including epidemiology, sociology, economics, and computer science. A widely studied problem in the area of viral marketing is the target set selection: in order to market a new product, hoping it will be adopted by a large fraction of individuals in the network, which set of individuals should we “target” (for instance, by offering them free samples of the product)? In this paper, we introduce a diffusion model in which some of the neighbors of a node have a negative influence on that node, namely, they induce the node to reject the feature that is supposed to be spread. We study the target set selection problem within this model, first proving a strong inapproximability result holding also when the diffusion process is required to reach all the nodes in a couple of rounds. Then, we consider a set of restrictions under which the problem is approximable to some extent.


2021 ◽  
pp. 371-380
Author(s):  
Zhecheng Qiang ◽  
Eduardo L. Pasiliao ◽  
Qipeng P. Zheng

2014 ◽  
Vol 535 ◽  
pp. 1-15 ◽  
Author(s):  
Ferdinando Cicalese ◽  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Martin Milanič ◽  
Ugo Vaccaro

Author(s):  
Ferdinando Cicalese ◽  
Gennaro Cordasco ◽  
Luisa Gargano ◽  
Martin Milanič ◽  
Ugo Vaccaro

2021 ◽  
Vol 5 (1) ◽  
pp. 14
Author(s):  
Christos Makris ◽  
Georgios Pispirigos

Nowadays, due to the extensive use of information networks in a broad range of fields, e.g., bio-informatics, sociology, digital marketing, computer science, etc., graph theory applications have attracted significant scientific interest. Due to its apparent abstraction, community detection has become one of the most thoroughly studied graph partitioning problems. However, the existing algorithms principally propose iterative solutions of high polynomial order that repetitively require exhaustive analysis. These methods can undoubtedly be considered resource-wise overdemanding, unscalable, and inapplicable in big data graphs, such as today’s social networks. In this article, a novel, near-linear, and highly scalable community prediction methodology is introduced. Specifically, using a distributed, stacking-based model, which is built on plain network topology characteristics of bootstrap sampled subgraphs, the underlined community hierarchy of any given social network is efficiently extracted in spite of its size and density. The effectiveness of the proposed methodology has diligently been examined on numerous real-life social networks and proven superior to various similar approaches in terms of performance, stability, and accuracy.


2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Shuang Zhao ◽  
Xiapu Luo ◽  
Xiaobo Ma ◽  
Bo Bai ◽  
Yankang Zhao ◽  
...  

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.


2011 ◽  
Vol 10 (4) ◽  
pp. 45-53 ◽  
Author(s):  
Nicholas D. Lane ◽  
Ye Xu ◽  
Hong Lu ◽  
Andrew T. Campbell ◽  
Tanzeem Choudhury ◽  
...  

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