Finding the resistance distance and eigenvector centrality from the network’s eigenvalues

Gutiérrez, Caracé; Gancio, Juan; Cabeza, Cécilia; Rubido, Nicolás

doi:10.1016/j.physa.2021.125751

Cited by 7 publications

(3 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A measure to quantify the topological distance between pairs of nodes is the resistance distance [22,23,24,25]. For nodes i and j, it can be determined by…”

Section: Definitions and Notationmentioning

confidence: 99%

“…Recently, Zhang and Bu (ZB) [17] proposed a new method for community detection based on the resistance distance. The resistance distance includes more information than the shortest paths, since it also considers every different possible path between any two nodes, weighing them as parallel paths [22,23,24,25]. Although the resistance distance has been widely used for network analysis [26,27,28,29,30,31], its use for community detection has been limited [32,17].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Community detection by resistance distance: automation and benchmark testing

Gancio,

Rubido

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Heterogeneity characterises real-world networks, where nodes show a broad range of different topological features. However, nodes also tend to organise into communities -subsets of nodes that are sparsely inter-connected but are densely intra-connected (more than the network's average connectivity). This means that nodes belonging to the same community are close to each other by some distance measure, such as the resistance distance, which is the effective distance between any pair of nodes considering all possible paths. In this work, we present automation (i.e., unsupervised) and missing accuracy tests for a recently proposed semi-supervised community detection algorithm based on the resistance distance. The accuracy testing involves quantifying our algorithm's performance in terms of recovering known synthetic communities from benchmark networks, where we present results for Girvan-Newman and Lancichinetti-Fortunato-Radicchi networks. Our findings show that our algorithm falls into the class of accurate performers.

show abstract

“…A measure to quantify the topological distance between pairs of nodes is the resistance distance [22,23,24,25]. For nodes i and j, it can be determined by…”

Section: Definitions and Notationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Community detection by resistance distance: automation and benchmark testing

Gancio,

Rubido

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Relevant literatures are available in [16]- [18]. In recent years, many scholars have studied resistive distance in different aspects of complex network, such as community detection [19]- [23], centrality measures [24]- [26], link prediction problem [27] and network robustness [28]- [31], etc. By the way, resistance in the network is intimately connected with the lengths of random walks on the graph.…”

Section: Introductionmentioning

confidence: 99%

Novel Resistive Distance Descriptors on Complex Network

Zhou

Lin

et al. 2022

IEEE Access

View full text Add to dashboard Cite

Large-scale complex network data poses significant challenges for the analytic ideas and tools to monitor and analyze complex networks. As classical structure descriptors, average path length (L), global and local network efficiency (E glob and E loc ), general and loop clustering coefficient (C 3 and D), play significant roles in complex network analysis. In this paper, we make use of resistive distance instead of the shortest path distance to suggest resistive distance descriptors, i.e., L r , Ω glob and Ω loc , Ω 3 and D r . We investigate all the resistive descriptors on classical WS, BA and ER models and find some interesting phenomenons. On one hand, L r and Ω 3 (resp., Ω glob and Ω loc ) can be used to characterize the features of small-word networks. On the other hand, L r , Ω 3 , and Ω glob can be utilized to measure network invulnerability. To access the effectiveness of the resistive distance descriptors, we conduct extensive numerical simulations on synthetic and real networks. In comparison with the baselines, the proposed metrics show competitive performance on classical network models and are more efficient for networks with small and medium size.

show abstract

Community Detection by Resistance Distance: Automation and Benchmark Testing

Gancio

Rubido

2022

Complex Networks &Amp; Their Applications X

View full text Add to dashboard Cite

Finding the resistance distance and eigenvector centrality from the network’s eigenvalues

Cited by 7 publications

References 50 publications

Community detection by resistance distance: automation and benchmark testing

Community detection by resistance distance: automation and benchmark testing

Novel Resistive Distance Descriptors on Complex Network

Community Detection by Resistance Distance: Automation and Benchmark Testing

Contact Info

Product

Resources

About