Improved gravity model for identifying the influential nodes

2022 ◽  
Author(s):  
Yun Chen ◽  
Qiang Guo ◽  
Min Liu ◽  
Jianguo Liu
Keyword(s):  
Dynamic Analysis ◽  
Correlation Coefficient ◽  
Gravity Model ◽  
Betweenness Centrality ◽  
Network Dynamic ◽  
Degree Centrality ◽  
Interaction Frequency ◽  
Eigenvector Centrality ◽  
Effective Distance ◽  
Influential Nodes

Abstract Identifying the influential nodes in network is essential for network dynamic analysis. In this letter, inspired by the gravity model, we present an improved gravity model (EDGM) to identify the influential nodes in network through the effective distance. Firstly, we calculate the degree of nodes. Then we construct the effective distance combined with the interaction frequency between nodes, so as to establish the effective distance gravity model. Comparing with the susceptible-infected model, the results show that the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 2.36\% for the gravity model. Compared with other methods, the Kendall' s $\tau$ correlation coefficient of EDGM could enhanced by 11.55%, 17.29%, 7.17% and 10.00% for the degree centrality, betweenness centrality, eigenvector centrality, and PageRank respectively. The results show that the improved gravity model could effectively identify the influential nodes in network.

Centrality and Partial Correlation Coefficient-Based Assortativity Analysis of Real-World Networks

2018 ◽  
Vol 62 (9) ◽  
pp. 1247-1264 ◽  
Author(s):  
Natarajan Meghanathan
Keyword(s):  
Correlation Coefficient ◽  
Real World ◽  
Betweenness Centrality ◽  
Partial Correlation ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
First Order ◽  
Centrality Metrics ◽  
Partial Correlation Coefficient

Abstract The assortativity index (A. Index) of a complex network has been hitherto computed as the Pearson’s correlation coefficient of the remaining degree centrality (R-DEG) of the first-order neighbors (i.e. end vertices of the edges) in the network. In this paper, we seek to evaluate the assortativity of real-world networks with respect to prototypical centrality metrics (in addition to R-DEG) such as eigenvector centrality (EVC), betweenness centrality (BWC) and closeness centrality (CLC). Unlike R-DEG, the centrality values of the vertices with respect to these three metrics are influenced by the centrality values of the vertices in the neighborhood. We propose to use the notion of ‘Partial Correlation Coefficient’ to nullify the influence of the second-order neighbors (i.e. vertices that are two hops away) and quantify the assortativity of the first-order neighbors with respect to a particular centrality metric (such as EVC, BWC and CLC). We conduct an exhaustive assortativity analysis on a suite of 70 real-world networks of diverse degree distributions. We observe real-world networks to be more assortative (A. Index > 0) with respect to CLC and EVC and relatively more dissortative (A. Index < 0) with respect to BWC and R-DEG.

Use of Centrality Metrics for Ranking of Courses Based on Their Relative Contribution in a Curriculum Network Graph

2018 ◽  
pp. 129-159
Keyword(s):  
Betweenness Centrality ◽  
Directed Acyclic Graph ◽  
Degree Centrality ◽  
Weighted Sum ◽  
Eigenvector Centrality ◽  
Network Graph ◽  
Curriculum Assessment ◽  
Acyclic Graph ◽  
Relative Contribution ◽  
Centrality Metrics

The author proposes a centrality and topological sort-based formulation to quantify the relative contribution of courses in a curriculum network graph (CNG), a directed acyclic graph, comprising of the courses (as vertices), and their pre-requisites (captured as directed edges). The centrality metrics considered are out-degree and in-degree centrality along with betweenness centrality and eigenvector centrality. The author normalizes the values obtained for each centrality metric as well as the level numbers of the vertices in a topological sort of the CNG. The contribution score for a vertex is the weighted sum of the normalized values for the vertex. The author observes the betweenness centrality of the vertices (courses) to have the largest influence in the relative contribution scores of the courses that could be used as a measure of the weights to be given to the courses for curriculum assessment and student ranking as well as to cluster courses with similar contribution.

Computationally Light vs. Computationally Heavy Centrality Metrics

2018 ◽  
pp. 34-65
Keyword(s):  
Betweenness Centrality ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Strongly Correlated ◽  
Eigenvector Centrality ◽  
Correlation Measure ◽  
Centrality Metrics ◽  
Two Measures ◽  
Local Clustering Coefficient ◽  
Spearman’S Correlation

In this chapter, the authors analyze the correlation between the computationally light degree centrality (DEG) and local clustering coefficient complement-based degree centrality (LCC'DC) metrics vs. the computationally heavy betweenness centrality (BWC), eigenvector centrality (EVC), and closeness centrality (CLC) metrics. Likewise, they also analyze the correlation between the computationally light complement of neighborhood overlap (NOVER') and the computationally heavy edge betweenness centrality (EBWC) metric. The authors analyze the correlations at three different levels: pair-wise (Kendall's correlation measure), network-wide (Spearman's correlation measure), and linear regression-based prediction (Pearson's correlation measure). With regards to the node centrality metrics, they observe LCC'DC-BWC to be the most strongly correlated at all the three levels of correlation. For the edge centrality metrics, the authors observe EBWC-NOVER' to be strongly correlated with respect to the Spearman's correlation measure, but not with respect to the other two measures.

scholarly journals Efficiency centrality in time-varying graphs

2020 ◽  
Vol 12 (1) ◽  
pp. 5-21
Author(s):  
Péter Marjai ◽  
Attila Kiss
Keyword(s):  
Complex Networks ◽  
Betweenness Centrality ◽  
Cascade Model ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Time Varying ◽  
Independent Cascade Model ◽  
Independent Cascade ◽  
Influential Nodes ◽  
Time Varying Graphs

AbstractOne of the most studied aspect of complex graphs is identifying the most influential nodes. There are some local metrics like degree centrality, which is cost-effiective and easy to calculate, although using global metrics like betweenness centrality or closeness centrality can identify influential nodes more accurately, however calculating these values can be costly and each measure has it’s own limitations and disadvantages. There is an ever-growing interest in calculating such metrics in time-varying graphs (TVGs), since modern complex networks can be best modelled with such graphs. In this paper we are investigating the effectiveness of a new centrality measure called efficiency centrality in TVGs. To evaluate the performance of the algorithm Independent Cascade Model is used to simulate infection spreading in four real networks. To simulate the changes in the network we are deleting and adding nodes based on their degree centrality. We are investigating the Time-Constrained Coverage and the magnitude of propagation resulted by the use of the algorithm.

scholarly journals Peran Pers Sebagai Aktor Gerakan Digital Tagar #SolidaritasUntukNTT di Twitter

2021 ◽  
Vol 5 (1) ◽  
pp. 98
Author(s):  
Gema Nusantara Bakry ◽  
Ika Merdekawati Kusmayadi
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Personal Network

Peristiwa banjir bandang yang diakibatkan Siklon Seroja telah mengundang perhatian dan simpati masyarakat Indonesia. Berbagai upaya telah dilakukan untuk berkontribusi dalam upaya penanggulangan dampak yang diterima oleh masyarakat NTT. Salah satu upaya yang dilakukan oleh masyarakat adalah mengampanyekan gerakan sosial digital #SolidaritasUntukNTT di Twitter. Gerakan sosial digital melalui pesan-pesan tertentu dapat menggugah kesadaran bagi penggunanya. Untuk mengetahui efektivitas penyebaran pesan dalam gerakan sosial digital dapat divisualisasikan menggunakan metode Social Network Analysis (SNA).  Penelitian ini bertujuan untuk memvisualisasikan peran pers dalam mendistribusikan pesan gerakan sosial digital dengan tagar #SolidaritasUntukNTT. Metode penelitian yang digunakan adalah analisis jaringan sosial dengan teori graf di Twitter. Hasil analisis dan visualisasi jaringan dilakukan di aplikasi Gephi dengan algoritma Yifan Hu untuk melihat distribusi pola pesan dan peran pers pada tagar #SolidaritasUntukNTT. Penelitian ini menggambarkan tipe jaringan two mode yang terdiri dari interaksi antara individu dan organisasi dengan pola komunikasi radial personal network yang memiliki ciri jaringan terbuka dan kohesivitas yang rendah dengan arah relasi directed dan asimetris. Analisis peran pers diukur melalui sentralitas aktor untuk mengetahui degree centrality, closeness centrality, betweenness centrality dan eigenvector centrality. Aktor @vice_id diketahui sebagai aktor yang memiliki degree dan eigenvector centrality tertinggi dibandingkan dengan aktor pers lainnya. Aktor @idntimes dan @detikcom memiliki nilai closeness dan betweenness centrality yang lebih tinggi dari media lainnya. Analisis jaringan sosial memberikan pemahaman terkait distribusi pesan dalam media sosial untuk mengetahui efektivitas pesan yang didistribusikan oleh beberapa aktor jaringan, khususnya peran pers dalam mengampanyekan gerakan sosial di media. Oleh karena itu, metode SNA dapat digunakan untuk penelitian jurnalisme data. 

A novel method for identifying influential nodes in complex networks based on gravity model

2021 ◽  
Author(s):  
Yuan Jiang ◽  
Song-Qing Yang ◽  
Yu-Wei Yan ◽  
Tian-Chi Tong ◽  
Ji-Yang Dai
Keyword(s):  
Complex Networks ◽  
Gravity Model ◽  
Evaluation Criteria ◽  
Effective Distance ◽  
Network Characteristics ◽  
Structural Hole ◽  
Shortest Distance ◽  
Novel Method ◽  
Influential Nodes ◽  
Local Topology

Abstract How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.

scholarly journals A network analysis of character relations in Etienne van Heerden’s Toorberg

Literator ◽  
2013 ◽  
Vol 34 (2) ◽  
Author(s):  
Burgert A. Senekal
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Betweenness Centrality ◽  
Theoretical Framework ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Traditional Concept ◽  
Family Ties

Etienne van Heerden’s Toorberg can be approached as a modern, postcolonial farm novel, partly because it challenges the concept of lineage of inheritance, which is characteristic of the traditional farm novel. Lineage of inheritance implies a strong family bond, and it is therefore instructive to investigate how family ties function within this novel. The article views family ties within Toorberg using Social Network Analysis (SNA), a largely unknown theoretical framework that can also be applied within the study of literature. It is shown how characters’ positions in this network can be calculated in terms of degree centrality, closeness centrality, Eigenvector centrality and betweenness centrality, and how these measures expose the way in which this novel undermines the traditional concept of inheritance.

Centrality Metrics, Measures, and Real-World Network Graphs

2018 ◽  
pp. 1-33
Keyword(s):  
Real World ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Clustering Coefficient ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Centrality Metrics ◽  
Edge Betweenness ◽  
Correlation Measures ◽  
Local Clustering Coefficient

This chapter provides an introduction to various node and edge centrality metrics that are studied throughout this book. The authors describe the procedure to compute these metrics and illustrate the same with an example. The node centrality metrics described are degree centrality (DEG), eigenvector centrality (EVC), betweenness centrality (BWC), closeness centrality (CLC), and the local clustering coefficient complement-based degree centrality (LCC'DC). The edge centrality metrics described are edge betweenness centrality (EBWC) and neighborhood overlap (NOVER). The authors then describe the three different correlation measures—Pearson's, Spearman's, and Kendall's measures—that are used in this book to analyze the correlation between any two centrality metrics. Finally, the authors provide a brief description of the 50 real-world network graphs that are studied in some of the chapters of this book.

Centrality-Based Assortativity Analysis of Complex Network Graphs

2018 ◽  
pp. 66-77
Keyword(s):  
Complex Network ◽  
Real World ◽  
Betweenness Centrality ◽  
Closeness Centrality ◽  
Degree Centrality ◽  
Eigenvector Centrality ◽  
Centrality Metrics

In this chapter, the author analyzes the assortativity of real-world networks based on centrality metrics (such as eigenvector centrality, betweenness centrality, and closeness centrality) other than degree centrality. They seek to evaluate the levels of assortativity (assortative, dissortative, neutral) observed for real-world networks with respect to the different centrality metrics and assess the similarity in these levels. The author observes real-world networks are more likely to be neutral (neither assortative nor dissortative) with respect to both R-DEG and BWC, and more likely to be assortative with respect to EVC and CLC. They observe the chances of a real-world network to be dissortative with respect to these centrality metrics to be very minimal. The author also assesses the extent to which they can use the assortativity index (A.Index) values obtained with a computationally light centrality metric to rank the networks in lieu of the A.Index values obtained with a computationally heavy centrality metric.

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