Network Analysis Based on Important Node Selection and Community Detection

Attila Mester,Andrei Pop,Laura Dioşan,Horea Greblă,Bogdan-Eduard-Mădălin Mursa,Camelia Chira

doi:10.3390/math9182294

Abstract

The stability and robustness of a complex network can be significantly improved by determining important nodes and by analyzing their tendency to group into clusters. Several centrality measures for evaluating the importance of a node in a complex network exist in the literature, each one focusing on a different perspective. Community detection algorithms can be used to determine clusters of nodes based on the network structure. This paper shows by empirical means that node importance can be evaluated by a dual perspective—by combining the traditional centrality measures regarding the whole network as one unit, and by analyzing the node clusters yielded by community detection. Not only do these approaches offer overlapping results but also complementary information regarding the top important nodes. To confirm this mechanism, we performed experiments for synthetic and real-world networks and the results indicate the interesting relation between important nodes on community and network level.

Highlights

Mathematics 2021, 9, 2294. https://The highly dense literature and studies on complex networks [1,2,3,4] demonstrate the importance of this field, which can be explained by its versatile usage, ability to model a wide variety of real systems and applicability in a series of domains such as travel, commerce, biology, technology, sociology, chemistry, economics and many other fields.Regarding a whole network, its dynamism and properties are encoded in its nodes and their wiring diagram, i.e., the links
We investigate the important nodes in different networks by aligning the results of centrality measures with community detection methods—analyzing cases where network-level important nodes detected by different centrality measures are hubs of communities, and vice versa
Since we lack the ground truth partitioning of the nodes in the datasets, we focus on strictly topological evaluation in our experiments—calculating the most intuitive metrics regarding the accuracy of community detection: modularity, coverage and performance

Summary

Introduction

Mathematics 2021, 9, 2294. https://The highly dense literature and studies on complex networks [1,2,3,4] demonstrate the importance of this field, which can be explained by its versatile usage, ability to model a wide variety of real systems and applicability in a series of domains such as travel, commerce, biology, technology, sociology, chemistry, economics and many other fields.Regarding a whole network, its dynamism and properties are encoded in its nodes and their wiring diagram, i.e., the links. The highly dense literature and studies on complex networks [1,2,3,4] demonstrate the importance of this field, which can be explained by its versatile usage, ability to model a wide variety of real systems and applicability in a series of domains such as travel, commerce, biology, technology, sociology, chemistry, economics and many other fields. The identification of important nodes in networks is a challenging but essential task with many high-impact applications in the real world. Identifying the important nodes will increase the endurance and robustness of the network. Another example can be extracted from the series of large blackouts that happened in different countries over the last decades. The one from 30th July affected over 400 million people, being the largest power outage considering all the people affected, surpassing the blackout from

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Results

Conclusion