scholarly journals A Closeness Centrality Analysis Algorithm for Workflow-supported Social Networks

2013 ◽  
Vol 14 (5) ◽  
pp. 77-85 ◽  
Author(s):  
Sungjoo Park ◽  
Kwanghoon Pio Kim
Keyword(s):  
Social Networks ◽  
Closeness Centrality ◽  
Analysis Algorithm ◽  
Centrality Analysis

URL ATTACKS: Classification of URLs via Analysis and Learning

2016 ◽  
Vol 6 (3) ◽  
pp. 980
Author(s):  
M. Rajesh ◽  
R. Abhilash ◽  
R. Praveen Kumar
Keyword(s):  
Social Networks ◽  
Link Analysis ◽  
User Needs ◽  
Learning Methods ◽  
Analysis Algorithm ◽  
Day By Day

Social Networks such as Twitter, Facebook play a remarkable growth in recent years. The ratio of tweets or messages in the form of URLs increases day by day. As the number of URL increases, the probability of fabrication also gets increased using their HTML content as well as by the usage of tiny URLs. It is important to classify the URLs by means of some modern techniques. Conditional redirection method is used here by which the URLs get classified and also the target page that the user needs is achieved. Learning methods also introduced to differentiate the URLs and there by the fabrication is not possible. Also the classifiers will efficiently detect the suspicious URLs using link analysis algorithm.

Improvement of path analysis algorithm in social networks based on HBase

2013 ◽  
Vol 28 (3) ◽  
pp. 588-599 ◽  
Author(s):  
Yan Qiang ◽  
Bo Pei ◽  
Weili Wu ◽  
Juanjuan Zhao ◽  
Xiaolong Zhang ◽  
...  
Keyword(s):  
Social Networks ◽  
Path Analysis ◽  
Analysis Algorithm

scholarly journals The generalized centrality method for analyzing network cyberspace

2019 ◽  
pp. 55-64
Author(s):  
Natalia Nikolaevna Gorlushkina ◽  
Sergei Evgenievich Ivanov ◽  
Lubov Nikolaevna Ivanova
Keyword(s):  
Mathematical Model ◽  
Social Networks ◽  
Information Dissemination ◽  
Relative Weight ◽  
Graph Model ◽  
Graph Models ◽  
Number Of Factors ◽  
The Subject ◽  
Comparison Of The Results ◽  
Centrality Analysis

The subject of the research is the methods of network cyberspace analysis based on graph models. The analysis allows to find leaders of groups and communities, to find cohesive groups and visualize the results. The main methods of the graph theory used for cyberspace social networks are the methods of analyzing the centrality to determine the relative weight or importance of the vertices of the graph. There are known methods for analyzing centralities: by degree, by proximity, by mediation, by radiality, by eccentricity, by status, eigenvector, referential ranking. The disadvantage of these methods is that they are based only on one or several properties of the network participant. Based on the centrality analysis methods, a new generalized centrality method is proposed, taking into account such participant properties as the participant's popularity, the importance and speed of information dissemination in the cyberspace network. A mathematical model of a new method of generalized centrality has been developed. Comparison of the results of the presented method with the methods of the analysis of centralities is performed. As a visual example, a subgroup of cyberspace consisting of twenty participants, represented by a graph model, is analyzed. Comparative analysis showed the accuracy of the method of generalized centrality, taking into account at once a number of factors and properties of the network participant.

Hadoop-Based Distributed K-Shell Decomposition for Social Networks

2018 ◽  
pp. 125-145
Author(s):  
Katerina Pechlivanidou ◽  
Dimitrios Katsaros ◽  
Leandros Tassiulas
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Complex Network ◽  
Online Social Networks ◽  
Distributed Algorithm ◽  
Data Centers ◽  
Large Data ◽  
Complex Network Analysis ◽  
Centrality Analysis

Complex network analysis comprises a popular set of tools for the analysis of online social networks. Among these techniques, k-shell decomposition of a network is a technique that has been used for centrality analysis, for communities' discovery, for the detection of influential spreaders, and so on. The huge volume of input graphs and the environments where the algorithm needs to run, i.e., large data centers, makes none of the existing algorithms appropriate for the decomposition of graphs into shells. In this article, we develop for a distributed algorithm based on MapReduce for the k-shell decomposition of a graph. We furthermore, provide an implementation and assessment of the algorithm using real social network datasets. We analyze the tradeoffs and speedup of the proposed algorithm and conclude for its virtues and shortcomings.

A community-based approach to identify the most influential nodes in social networks

2016 ◽  
Vol 43 (2) ◽  
pp. 204-220 ◽  
Author(s):  
Maryam Hosseini-Pozveh ◽  
Kamran Zamanifar ◽  
Ahmad Reza Naghsh-Nilchi
Keyword(s):  
Social Networks ◽  
Information Diffusion ◽  
Closeness Centrality ◽  
Centrality Measures ◽  
Community Based ◽  
Spreading Process ◽  
Centrality Measure ◽  
Influence Spread ◽  
The Social ◽  
Influential Nodes

One of the important issues concerning the spreading process in social networks is the influence maximization. This is the problem of identifying the set of the most influential nodes in order to begin the spreading process based on an information diffusion model in the social networks. In this study, two new methods considering the community structure of the social networks and influence-based closeness centrality measure of the nodes are presented to maximize the spread of influence on the multiplication threshold, minimum threshold and linear threshold information diffusion models. The main objective of this study is to improve the efficiency with respect to the run time while maintaining the accuracy of the final influence spread. Efficiency improvement is obtained by reducing the number of candidate nodes subject to evaluation in order to find the most influential. Experiments consist of two parts: first, the effectiveness of the proposed influence-based closeness centrality measure is established by comparing it with available centrality measures; second, the evaluations are conducted to compare the two proposed community-based methods with well-known benchmarks in the literature on the real datasets, leading to the results demonstrate the efficiency and effectiveness of these methods in maximizing the influence spread in social networks.

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