Analysis and visualization of social media networks

Malathi, A.; Radha, D.

doi:10.1109/csitss.2016.7779440

Cited by 12 publications

(6 citation statements)

References 9 publications

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“…Centrality means a function in which each node in a network is assigned a numeric value C(v). The concept is used to rank the nodes and is helpful to identify important disease in the networks [29].…”

Section: Resultsmentioning

confidence: 99%

Analysis of Human gene - Disease association as a Social network

Doke¹

2019

IJATCSE

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Interrelationship of diseases can be analyzed using a biological network graph. In the Network graph of human diseases, each node represents a disease and interrelationship between the diseases is represented by as an edge. Interrelationship between the diseases represent the commonality in the genes associated with diseases. The size of a node represents the number of genes that are associated with the disease. Representation of such biological network can be used to analyze the way other social networks are analyzed. The proposed work introduces an approach to analyze biological network in terms of social network in which causes of different life-threatening diseases such as colon cancer are identified by using different centrality measures.

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Section: Resultsmentioning

confidence: 99%

Analysis of Human gene - Disease association as a Social network

Doke¹

2019

IJATCSE

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“…The best modularity results for the Dolphins dataset after testing were 0.7446, which results are shown in Figure 4 and the community structure is shown in Table 4. Table 4, the results of the genetic algorithm show 8 communities for the Bootlenose dolphins dataset, while for nodes that occupy community 0 of them are nodes 0, 5,7,15,17,23,27,30,36,47,50,55, and 60, nodes that occupy community 1 of which are nodes 2, 22,24,31,35,39,42,45,53, and 56, nodes that occupy community 2 of which are nodes 10,12,14,16,18,20,21,29,33,34,37,38,40,43,44,46,49,52,58 The best modularity results for the Football dataset after testing were 0.7242, which results are shown in Figure 5 and the community structure is shown in Table 5. Table 5, nodes that occupy community 0 of which are nodes 17,20,…”

Section: Discussionmentioning

confidence: 99%

“…[32], the dataset was obtained from observations of a collection of social networking data, where the data are political book data purchased by Americans, consisting of 105 nodes and 441 edges. Where each node shows a political book, and the edge shows two books if bought by the same person [46].…”

Section: Figure 2 Framework Of Genetic Algorithmmentioning

confidence: 99%

Determining Community Structure and Modularity in Social Network using Genetic Algorithm

Aditama¹,

Sn²

2020

Indonesian J. Comput. Cybern. Syst.

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Research on determining community structure in complex networks has attracted a lot of attention in various applications, such as email networks and social networks. The popularity determines the structure of a community because it can analyze the structure.Meanwhile, to determine the structure of the community by maximizing the value of modularity is difficult. Therefore, a lot of research introduces new algorithms to solve problems in determining community structure and maximizing the value of modularity. Genetic Algorithm can provide effective solutions by combining exploration and exploitation.This study focuses on the Genetic Algorithm which added a cleanup feature in the process. The final results of this study are the results of a comparison of modularity values based on the determination of the community structure of the Genetic Algorithm, Girvan and Newman Algorithm, and the Louvain Algorithm. The best modularity values were obtained using the Genetic Algorithm which obtained 0.6833 results for Zachary's karate club dataset, 0.7446 for the Bottlenose dolphins dataset, 0.7242 for the American college football dataset, and 0.5892 for the Books about US politics dataset.

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“…Social network analysis and visualization have provided researchers a perceptive view of networks. Graphs showing nodes and their interactions can be used as a framework to analyze various types of relations and processes in biological as well as information systems [3]. The fundamental attribute that describes a network is the node degree that is defined as the number of interactions it has with several other nodes in a network.…”

Section: Introductionmentioning

confidence: 99%

Analytical Modeling of Real-World Social Network Parameters

Monika

Mangat

2020

Lecture Notes in Networks and Systems

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A social network is an interactive platform for massive communication. While analyzing a social network, its pictorial representation as a graph is required to explore and visualize it. Graph theory concepts are applied to social networks which are helpful in numerical formulations also. Social network graph theory is applicable to formulate and study various statistical parameters that can help in analyzing the social patterns of these networks. Various Python implementation commands from importing data to the computation of data for network statistics like the clustering coefficient and the degree distribution of nodes are described in this paper. These network statistics are significant as they can be used to unveil differences in the pattern of interconnections in a social network.

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Analysis and visualization of social media networks

Cited by 12 publications

References 9 publications

Analysis of Human gene - Disease association as a Social network

Analysis of Human gene - Disease association as a Social network

Determining Community Structure and Modularity in Social Network using Genetic Algorithm

Analytical Modeling of Real-World Social Network Parameters

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