scholarly journals Parameterized Approximability of Maximizing the Spread of Influence in Networks

2013 ◽  
pp. 543-554 ◽  
Author(s):  
Cristina Bazgan ◽  
Morgan Chopin ◽  
André Nichterlein ◽  
Florian Sikora
Keyword(s):  
Spread Of Influence

scholarly journals Influence Maximization in Social Networks using Deterministic Crowding Algorithm

2019 ◽  
Vol 8 (11) ◽  
pp. 4258-4265
Keyword(s):  
Social Network ◽  
Information Diffusion ◽  
Influence Maximization ◽  
Maximization Problem ◽  
Linear Threshold ◽  
The Social ◽  
Spread Of Influence ◽  
Influential Users ◽  
High Influence ◽  
Influence Maximization Problem

In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

Scalable Influence Maximization Meets Efficiency and Effectiveness in Large-Scale Social Networks

2020 ◽  
Vol 30 (08) ◽  
pp. 1079-1096
Author(s):  
Liqing Qiu ◽  
Shuang Zhang ◽  
Chunmei Gu ◽  
Xiangbo Tian
Keyword(s):  
Social Networks ◽  
Large Scale ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Scalable Algorithm ◽  
Influence Spread ◽  
Efficiency And Effectiveness ◽  
Spread Of Influence ◽  
Influential Nodes ◽  
Comparison Algorithms

Influence maximization is a problem that aims to select top [Formula: see text] influential nodes to maximize the spread of influence in social networks. The classical greedy-based algorithms and their improvements are relatively slow or not scalable. The efficiency of heuristic algorithms is fast but their accuracy is unacceptable. Some algorithms improve the accuracy and efficiency by consuming a large amount of memory usage. To overcome the above shortcoming, this paper proposes a fast and scalable algorithm for influence maximization, called K-paths, which utilizes the influence tree to estimate the influence spread. Additionally, extensive experiments demonstrate that the K-paths algorithm outperforms the comparison algorithms in terms of efficiency while keeping competitive accuracy.

scholarly journals Parameterized approximability of maximizing the spread of influence in networks

2014 ◽  
Vol 27 ◽  
pp. 54-65 ◽  
Author(s):  
Cristina Bazgan ◽  
Morgan Chopin ◽  
André Nichterlein ◽  
Florian Sikora
Keyword(s):  
Spread Of Influence

scholarly journals Maximizing the Spread of Influence via Generalized Degree Discount

PLoS ONE ◽  
2016 ◽  
Vol 11 (10) ◽  
pp. e0164393 ◽  
Author(s):  
Xiaojie Wang ◽  
Xue Zhang ◽  
Chengli Zhao ◽  
Dongyun Yi
Keyword(s):  
Spread Of Influence

scholarly journals Seed Selection for Spread of Influence in Social Networks: Temporal vs. Static Approach

2014 ◽  
Vol 32 (3-4) ◽  
pp. 213-235 ◽  
Author(s):  
Radosław Michalski ◽  
Tomasz Kajdanowicz ◽  
Piotr Bródka ◽  
Przemysław Kazienko
Keyword(s):  
Social Networks ◽  
Seed Selection ◽  
Spread Of Influence ◽  
Static Approach ◽  
Selection For

scholarly journals Media Leaders Mining Based on Android News Apps

2019 ◽  
Vol 2 (1) ◽  
pp. 4
Author(s):  
Sijia Wang ◽  
Miao Zhang
Keyword(s):  
New Media ◽  
Rapid Development ◽  
Experimental Studies ◽  
Mobile Internet ◽  
Major Influence ◽  
Data Sets ◽  
The Public ◽  
Spread Of Influence ◽  
Rapid Dissemination ◽  
Media Reports

<p align="justify">With the rapid development of the mobile Internet, the mobile news apps have become the most important way for the public to obtain news. As a new media carrier and communication platform,the mobile news apps can promote the rapid dissemination of information and the rapid spread of influence.  Some media have a major influence  on the direction of other media reports and the behavioral decisions of the public. These media can be regarded as media leaders. Media leaders are very important in the dissemination of news. By identifying media leaders, companies or governments can promote sales or guide public opinion separately. This article believes that media leaders mainly achieve their own influence by publishing news, so this article uses the news published by the mobile news apps as an entry point. This paper firstly solves the problem of data crawling in mobile news apps, and proposes a data crawling method based on reverse analysis, and obtains the data source. Then, reconstruct the reprinting path of the news, and carry out accurate traceability. Finally, cluster the news based on LDA, and propose an algorithm for mining media leaders from three aspects: influence, activity and preference. Experimental studies of data sets have shown that our algorithms can effectively identify media leaders.</p>

scholarly journals On the Non-progressive Spread of Influence through Social Networks

2012 ◽  
pp. 315-326 ◽  
Author(s):  
MohammadAmin Fazli ◽  
Mohammad Ghodsi ◽  
Jafar Habibi ◽  
Pooya Jalaly Khalilabadi ◽  
Vahab Mirrokni ◽  
...  
Keyword(s):  
Social Networks ◽  
Spread Of Influence

Identification of multi-spreader users in social networks for viral marketing

2016 ◽  
Vol 43 (3) ◽  
pp. 412-423 ◽  
Author(s):  
Amir Sheikhahmadi ◽  
Mohammad Ali Nematbakhsh
Keyword(s):  
Social Networks ◽  
Complex Networks ◽  
Hybrid Method ◽  
Viral Marketing ◽  
Interesting Problem ◽  
Large Numbers ◽  
Arduous Task ◽  
Accurate Performance ◽  
Spread Of Influence ◽  
Maximum Spreading

Identifying high spreading power nodes is an interesting problem in social networks. Finding super spreader nodes becomes an arduous task when the nodes appear in large numbers, and the number of existing links becomes enormous among them. One of the methods that is used for identifying the nodes is to rank them based on k-shell decomposition. Nevertheless, one of the disadvantages of this method is that it assigns the same rank to the nodes of a shell. Another disadvantage of this method is that only one indicator is fairly used to rank the nodes. k-Shell is an approach that is used for ranking separate spreaders, yet it does not have enough efficiency when a group of nodes with maximum spreading needs to be selected; therefore, this method, alone, does not have enough efficiency. Accordingly, in this study a hybrid method is presented to identify the super spreaders based on k-shell measure. Afterwards, a suitable method is presented to select a group of superior nodes in order to maximize the spread of influence. Experimental results on seven complex networks show that our proposed methods outperforms other well-known measures and represents comparatively more accurate performance in identifying the super spreader nodes.

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