Link Prediction in Social Networks by Neutrosophic Graph

Mahapatra, Rupkumar; Samanta, Sovan; Pal, Madhumangal; Xin, Qin

doi:10.2991/ijcis.d.201015.002

Cited by 36 publications

(15 citation statements)

References 11 publications

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“…are helpful to analyze the performance of a social network. (27)(28)(29) Since PPFG model has four or more attribute values associated with vertices, a better prediction about the quality of Pvertices possible in PPFG model. The computation of the attribute values for the P-edges in PPFG model reduces the error to the minimum in our prediction regarding strength of connectivity.…”

Section: Application Of Plithogenic Product Fuzzy Graph In Social Networkmentioning

confidence: 99%

A Study on Plithogenic Product Fuzzy Graphs with Application in Social network

Bharathi¹

2021

IJST

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Objectives:The main objective of this study is to define Plithogenic product fuzzy graphs (PPFGs), and introduce its properties. Method: PPFGs is newly introduced as a new graphical model where P-vertices are characterized by four or more attributes and the attribute values of P-edges are computed using the product operator. Findings: Theoretical discussions and results related to PPFGs, and subgraphs, paths, cycles, trees, bridge and cut vertex in PPFGs are demonstrated with examples. A social network model based on the notion of PPFGs has been presented and analyzed to show the utility and the advantage of Plithogenic product fuzzy graph model. Novelty: Strong and weak P-vertices, and highly strong, strong and weak P-edges are identified to analyze the strength of connectivity between different units. P-order, P-size, P-vertex range, P-edge range, degree, total degree, and average P-weight of P-vertices are computed to examine proximity, significance and centrality of units.

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Section: Application Of Plithogenic Product Fuzzy Graph In Social Networkmentioning

confidence: 99%

A Study on Plithogenic Product Fuzzy Graphs with Application in Social network

Bharathi¹

2021

IJST

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“…Mahapatra et al [16][17][18][19] introduced the edge colouring of a fuzzy graph and radio k colouring in fuzzy graphs. There are different types of centralities available in the literature.…”

Section: Introductionmentioning

confidence: 99%

A Study on Semi-directed Graphs for Social Media Networks

Samanta¹,

Pal²,

Mahapatra³

et al. 2021

IJCIS

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In the literature of graph theory, networks are represented as directed graphs or undirected graphs and a mixed of both combinations. In today's era of computing, networks like brain and facebook that do not belong to any of the mentioned networks category and in fact, it belongs to the combination of both networks which have connections as directed as well as undirected. To represent such networks, semi-directed graphs have been studied in this paper that provides the detailed mathematical fundamentals related to better understand the conceptualization for social media networks. This paper also discusses the suitable matrices analyze for the representation of the graphs. Few new terminologies like incidence number, complete-incidence related to semi-directed graphs and counter isomorphism of semi-directed graphs have been inculcated. A centrality measure, namely incidence centrality, has also been proposed based on incidence number on neighbors in social media networks.

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“…Hansen et al [11] and Sotskov et al [12] introduced the mixed graph colouring. Mahapatra et al [13][14][15][16][17][18][19][20][21][22][23] introduced the edge colouring of a fuzzy graph and radio k colouring in fuzzy graphs. In this article, the colouring of directed fuzzy graphs is also proposed.…”

Section: Introductionmentioning

confidence: 99%

Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

Mahapatra¹,

Samanta²,

Pal³

et al. 2021

Computers, Materials &Amp; Continua

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Graph colouring is the system of assigning a colour to each vertex of a graph. It is done in such a way that adjacent vertices do not have equal colour. It is fundamental in graph theory. It is often used to solve real-world problems like traffic light signalling, map colouring, scheduling, etc. Nowadays, social networks are prevalent systems in our life. Here, the users are considered as vertices, and their connections/interactions are taken as edges. Some users follow other popular users' profiles in these networks, and some don't, but those non-followers are connected directly to the popular profiles. That means, along with traditional relationship (information flowing), there is another relation among them. It depends on the domination of the relationship between the nodes. This type of situation can be modelled as a directed fuzzy graph. In the colouring of fuzzy graph theory, edge membership plays a vital role. Edge membership is a representation of flowing information between end nodes of the edge. Apart from the communication relationship, there may be some other factors like domination in relation. This influence of power is captured here. In this article, the colouring of directed fuzzy graphs is defined based on the influence of relationship. Along with this, the chromatic number and strong chromatic number are provided, and related properties are investigated. An application regarding COVID-19 infection is presented using the colouring of directed fuzzy graphs.

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Link Prediction in Social Networks by Neutrosophic Graph

Cited by 36 publications

References 11 publications

A Study on Plithogenic Product Fuzzy Graphs with Application in Social network

A Study on Plithogenic Product Fuzzy Graphs with Application in Social network

A Study on Semi-directed Graphs for Social Media Networks

Colouring of COVID-19 Affected Region Based on Fuzzy Directed Graphs

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