Degree Ranking Using Local Information
Akrati Saxena, Ralucca Gera, S. R. S. Iyengar

TL;DR
This paper introduces local algorithms to estimate a node's degree rank in dynamic networks using minimal samples, enabling efficient analysis without full network data, applicable to both social and random networks.
Contribution
The paper presents novel local algorithms for degree rank estimation that require only 1% of network samples, applicable to real-world and synthetic networks, including Erdős-Rényi models.
Findings
High accuracy in degree rank estimation with 1% sampling
Estimation accuracy decreases for lower-ranked nodes
Methods are effective on both social and random networks
Abstract
Most real world dynamic networks are evolved very fast with time. It is not feasible to collect the entire network at any given time to study its characteristics. This creates the need to propose local algorithms to study various properties of the network. In the present work, we estimate degree rank of a node without having the entire network. The proposed methods are based on the power law degree distribution characteristic or sampling techniques. The proposed methods are simulated on synthetic networks, as well as on real world social networks. The efficiency of the proposed methods is evaluated using absolute and weighted error functions. Results show that the degree rank of a node can be estimated with high accuracy using only samples of the network size. The accuracy of the estimation decreases from high ranked to low ranked nodes. We further extend the proposed methods for…
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Taxonomy
TopicsComplex Network Analysis Techniques · Opinion Dynamics and Social Influence · Advanced Graph Neural Networks
