Sampling Based Katz Centrality Estimation for Large-Scale Social Networks

Lin, MingDe; Li, Wenzhong; Nguyen, Cam-Tu; Wang, Xiaoliang; Lu, Sanglu

doi:10.1007/978-3-030-38961-1_50

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Under the current algorithm and implementation, several other measures ( Table 4 –red) would be limited to smaller networks, which makes them a prime target for follow-up studies using either distributed and/or approximation algorithms to deliver computational improvements. Such studies have a rich history in network science and continue to deliver improvements for well-known algorithms, as exemplified by ongoing works on parallel algorithms for degree or betweenness centrality (García and Carriegos, 2019 ), and approximation algorithms for Katz centrality (Lin et al, 2021 ). Our work thus provides the research community with a potential roadmap to improve emerging centrality measures by leveraging existing techniques.…”

Section: Discussionmentioning

confidence: 99%

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

Freund¹,

Giabbanelli²

2022

Front. Big Data

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Node centrality measures are among the most commonly used analytical techniques for networks. They have long helped analysts to identify “important” nodes that hold power in a social context, where damages could have dire consequences for transportation applications, or who should be a focus for prevention in epidemiology. Given the ubiquity of network data, new measures have been proposed, occasionally motivated by emerging applications or by the ability to interpolate existing measures. Before analysts use these measures and interpret results, the fundamental question is: are these measures likely to complete within the time window allotted to the analysis? In this paper, we comprehensively examine how the time necessary to run 18 new measures (introduced from 2005 to 2020) scales as a function of the number of nodes in the network. Our focus is on giving analysts a simple and practical estimate for sparse networks. As the time consumption depends on the properties in the network, we nuance our analysis by considering whether the network is scale-free, small-world, or random. Our results identify that several metrics run in the order of O(nlogn) and could scale to large networks, whereas others can require O(n2) or O(n3) and may become prime targets in future works for approximation algorithms or distributed implementations.

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Section: Discussionmentioning

confidence: 99%

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

Freund¹,

Giabbanelli²

2022

Front. Big Data

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“…A different but related research direction is to question the stability of centrality measures under perturbations [21,22,23]. In the case of PageRank score, and more recently for Katz centrality as well [24], the focus of similar lines of research is based on the different objective of estimating single nodes' scores or approximating the external information missing for reliable within-sample estimation [25,26,27], rather than estimating the relative ranking of nodes within a sample as we do here. Finally, focusing on temporal networks, [28] propose a centrality measure suitable for this case and a method for its estimation using the network dynamics.…”

Section: Related Workmentioning

confidence: 99%

Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures

Ruggeri,

De Bacco

2020

Preprint

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We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological quantities. Our goal is on one hand to extend the analysis of the behavior of TCEC [1], a theoretically-grounded sampling method for eigenvector centrality estimation. On the other hand, to demonstrate more broadly how sampling can impact the estimation of relevant network properties like centrality measures different than the one aimed at optimizing, community structure and node attribute distribution. Finally, we adapt the theoretical framework behind TCEC for the case of PageRank centrality and propose a sampling algorithm aimed at optimizing its estimation. We show that, while the theoretical derivation can be suitably adapted to cover this case, the resulting algorithm suffers of a high computational complexity that requires further approximations compared to the eigenvector centrality case.

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“…It is useful in estimating traffic status and to measure intermediation of nodes in spatial network. Also, it evaluates pedestrian flow in urban network Sampling-based Katz Centrclity (SAKE) (Lin et al 2019) 2019…”

Section: Introductionmentioning

confidence: 99%

Centrality Measures in Finding Influential Nodes for the Big-Data Network

Gopalakrishnan

Sridharan

Swaminathan

2021

Handbook of Smart Materials, Technologies, and Devices

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Network analysis determines node-to-node connectivity and examines each node's features. Various tools are available in the literature to study networks like graph theoretical, statistical, and many. As the graph plays a vital role in representing networks, the centrality measurements play an essential role in network analysis. The centrality measures use various metrics for evaluating how important the node is. The centrality predicts the characteristics and importance of the nodes in the network. In this review, selected 16 centrality measures presented along with their implementations.

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Sampling Based Katz Centrality Estimation for Large-Scale Social Networks

Cited by 5 publications

References 19 publications

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

An Experimental Study on the Scalability of Recent Node Centrality Metrics in Sparse Complex Networks

Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures

Centrality Measures in Finding Influential Nodes for the Big-Data Network

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