Fast Estimation of Closeness Centrality Ranking

Saxena, Akrati; Gera, Ralucca; Iyengar, S. R. S.

doi:10.1145/3110025.3110064

Cited by 18 publications

(7 citation statements)

References 38 publications

(32 reference statements)

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“…We study the structural behavior of closeness centrality and find that the reverse closeness centrality rank versus closeness centrality follows a sigmoid curve in social networks as shown in Figure 2 [81,82]. In reverse closeness centrality ranking a node having the highest closeness centrality has the lowest rank and vice versa.…”

Section: Closeness Rank Estimationmentioning

confidence: 97%

Global Rank Estimation

Saxena,

Iyengar

2017

Preprint

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In real world complex networks, the importance of a node depends on two important parameters: 1. characteristics of the node, and 2. the context of the given application. The current literature contains several centrality measures that have been defined to measure the importance of a node based on the given application requirements. These centrality measures assign a centrality value to each node that denotes its importance index. But in real life applications, we are more interested in the relative importance of the node that can be measured using its centrality rank based on the given centrality measure. To compute the centrality rank of a node, we need to compute the centrality value of all the nodes and compare them to get the rank. This process requires the entire network. So, it is not feasible for real-life applications due to the large size and dynamic nature of real world networks.In the present project, we aim to propose fast and efficient methods to estimate the global centrality rank of a node without computing the centrality value of all nodes. These methods can be further extended to estimate the rank without having the entire network. The proposed methods use the structural behavior of centrality measures, sampling techniques, or the machine learning models. In this work, we also discuss how to apply these methods for degree and closeness centrality rank estimation.

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Section: Closeness Rank Estimationmentioning

confidence: 97%

Global Rank Estimation

Saxena,

Iyengar

2017

Preprint

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“…Apart from this, there is a significant correlation between performance in the second exam and the students' betweenness and degree centralities. Existing methods can be used to estimate the centrality rank of a student in a class without computing the centrality value of all students [46] [47] [48].…”

Section: Academic Performance Versus Social Networkmentioning

confidence: 99%

A Survey on Studying the Social Networks of Students

Saxena,

Reddy

et al. 2019

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Do studies show that physical and online students' social networks support education? Analyzing interactions between students in schools and universities can provide a wealth of information. Studies on students' social networks can help us understand their behavioral dynamics, the correlation between their friendships and academic performance, community and group formation, information diffusion, and so on. Educational goals and holistic development of students with various academic abilities and backgrounds can be achieved by incorporating the findings attained by the studies in terms of knowledge propagation in classroom and spread of delinquent behaviors. Moreover, we use Social Network Analysis (SNA) to identify isolated students, ascertain the group study culture, analyze the spreading of various habits like smoking, drinking, and so on. In this paper, we present a review of the research showing how analysis of students' social networks can help us identify how improved educational methods can be used to make learning more inclusive at both school and university levels and achieve holistic development of students through expansion of their social networks, as well as control the spread of delinquent behaviors.

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“…where d(u, v) is the shortest distance between u and v. Locating public facilities over a transportation network such that they are easily accessible to everyone or identifying people with ideal social network location for information dissemination or network influence can be mentioned as scenarios in which identifying high closeness centralities is of great interest [1][2][3]. In these scenarios, we are mainly interested in efficiently and accurately detecting top-k high closeness centrality nodes in the network, while their exact relative order compared to each other, as well as the actual closeness centrality values, are not so important.…”

mentioning

confidence: 99%

“…Our contributions in this paper are two-fold: (1) We propose a local (ego-centric) metric which can be computed in a distributed manner at each node. The computation can be carried out requiring each node to have only local knowledge of its immediate neighborhood.…”

mentioning

confidence: 99%

Compressive closeness in networks

2019

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Distributed algorithms for network science applications are of great importance due to today's large real-world networks. In such algorithms, a node is allowed only to have local interactions with its immediate neighbors. This is because the whole network topological structure is often unknown to each node. Recently, distributed detection of central nodes, concerning different notions of importance, within a network has received much attention. Closeness centrality is a prominent measure to evaluate the importance (influence) of nodes, based on their accessibility, in a given network. In this paper, first, we introduce a local (ego-centric) metric that correlates well with the global closeness centrality;however, it has very low computational complexity. Second, we propose a compressive sensing (CS)-based framework to accurately recover high closeness centrality nodes in the network utilizing the proposed local metric. Both ego-centric metric computation and its aggregation via CS are efficient and distributed, using only local interactions between neighboring nodes. Finally, we evaluate the performance of the proposed method through extensive experiments on various synthetic and real-world networks. The results show that the proposed local metric correlates with the global closeness centrality, better than the current local metrics. Moreover, the results demonstrate that the proposed CS-based method outperforms the state-of-the-art methods with notable improvement.

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Fast Estimation of Closeness Centrality Ranking

Cited by 18 publications

References 38 publications

Global Rank Estimation

Global Rank Estimation

A Survey on Studying the Social Networks of Students

Compressive closeness in networks

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