Interactively Visualize and Analyze Social Network Gephi

Wajahat, Ahsan; Nazir, Ahsan; Akhtar, Faheem; Qureshi, Sirajuddin; Ullah, Faheem; Razaque, Fahad; Shakeel, Anum

doi:10.1109/icomet48670.2020.9073812

Cited by 13 publications

(8 citation statements)

References 13 publications

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“…is view will depend on the visual spectrum and the steps taken by the fish in each process. (3) Steps. e first fish grows long due to the limits of its global vision, and then its growth slows down with respect to the chain of vision and seldom gets to the point where the growth of fish divides them into the areas of concern.…”

Section: Methodsmentioning

confidence: 99%

“…Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others [1,2]. Complex systems are usually represented as complex graphs or networks that show the connections, dependencies, and interactions between the entities or components [3]. Networks consist of nodes (vertices) linked (edges) with other nodes.…”

Section: Introductionmentioning

confidence: 99%

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SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks

et al. 2020

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Efficient community detection in a complex network is considered an interesting issue due to its vast applications in many prevailing areas such as biology, chemistry, linguistics, social sciences, and others. There are several algorithms available for network community detection. This study proposed the Sigmoid Fish Swarm Optimization (SiFSO) algorithm to discover efficient network communities. Our proposed algorithm uses the sigmoid function for various fish moves in a swarm, including Prey, Follow, Swarm, and Free Move, for better movement and community detection. The proposed SiFSO algorithm’s performance is tested against state-of-the-art particle swarm optimization (PSO) algorithms in Q-modularity and normalized mutual information (NMI). The results showed that the proposed SiFSO algorithm is 0.0014% better in terms of Q-modularity and 0.1187% better in terms of NMI than the other selected algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks

et al. 2020

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“…The cruise itinerary is a chain that starts from the departure port and stops at multiple ports of call (Alves & Santos, 2022). In order to deeply discuss the structural characteristics of the Arctic adventure cruise shipping network, this study uses Gephi software as the analysis tool (Wajahat et al, 2020) and uses the Fruchterman-Reingold algorithm to build and analyse the Arctic adventure cruise shipping network (Jacomy, Venturini, Heymann, & Bastian, 2014). The point size is distinguished by the point degree value, and the point map of the Arctic adventure cruise itinerary ports is obtained (Fig.…”

Section: Spatial Structure Of Arctic Adventure Cruise Shipping Networkmentioning

confidence: 99%

Arctic adventure cruise shipping network: Itinerary characteristics and spatial structure

Long,

Ren,

2023

Polar Record

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The frigid geographical environment in the Arctic has shaped the exploration attribute of the polar cruise shipping network. In this study, the typical characteristics and special structure of the Arctic adventure cruise shipping network are investigated by using the network analysis method based on the data of 172 adventure cruise itineraries in the Arctic. It is found that the Arctic adventure cruise itineraries are dominated by 8–17 days of medium itineraries, and the ratio of one-way itineraries to round-trip itineraries is about 1:1. There are differences in the centrality of different ports, forming two core ports Reykjavík and Longyearbyen and a sub-core port Kangerlussuaq. The overall contact strength of the Arctic adventure cruise shipping network is low. Under the joint influence of such factors as centrality and contact strength, the Arctic constitutes the dual-core clusters of Iceland and Svalbard Islands and a sub-core cluster of Greenland.

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“…Los datos de entrada de la herramienta fueron obtenidos a partir de los historiales de conversación de los involucrados, con su previa autorización. El valor de autoridad de HITS se calculó con ayuda de la herramienta Gephi (Wajahat et al, 2020), la cual se configuró para que se conectara a la base de datos de GRAPH-CHAT y, de esta forma, se pudiera acceder al grafo. El sociograma aplicado a estas personas fue diseñado por un experto en sociología.…”

Section: Obtención De Los Datos Y Cálculo Del Valor De Influenciaunclassified

Algorithm for measurement and analysis of authority and influence of users in social and professional networks

Pérez

Castillo

Valdés

2021

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The measurement and analysis of the authority and influence exercised by a person in an organization or social network, be it formal or informal, has been the subject of numerous researches in several fields of science. At present, this phenomenon has taken on greater connotation due to its irruption in the digital space and the importance of having this knowledge for decision-making in spheres such as politics, education and the dissemination of information. In this research, an algorithm was developed for the measurement and analysis of the authority and influence of users in social and professional networks. The study had a mixed approach, with correlational scope and experimental design. A random sample n = 30 specialists was used, which was carried out between May 2019 and October 2020. It was based on the premise that, in order to carry out an adequate measurement and analysis of authority and influence, the structure of the graph must be considered that represents the social network and the interactions that occur between users. As a result, the Total Authority algorithm is developed, a computer tool for the generation of the graph and a case study, which evaluates its relevance, operation and applicability, which shows satisfactory results in its comparison with the HITS algorithm and a sociogram.

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Interactively Visualize and Analyze Social Network Gephi

Cited by 13 publications

References 13 publications

SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks

SiFSO: Fish Swarm Optimization-Based Technique for Efficient Community Detection in Complex Networks

Arctic adventure cruise shipping network: Itinerary characteristics and spatial structure

Algorithm for measurement and analysis of authority and influence of users in social and professional networks

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