What kind of network are you?

Abstract

The amount of research done in the area of realworld networked systems is rapidly growing. Everybody knows what six degrees of separation or small-world phenomenon are. Scientists very easily give labels to the networks they analyse. If it has power law node degree distribution then it has to be scale-free network or if there is high clustering coefficient then it must be small-world network. These simplifications, although convenient, are not always very useful from the perspective of understanding phenomena existing within the network. In this paper we decided to go back to the basics and investigate whether analysis of one single measure is enough to describe a network. We analyse both local and global characteristics in order to discover the "true" nature of a network. Not only using local and/or global measures can lead to different classification of a network but we also show how significantly different interpretation can result from analysing the same data by building network models as directed/undirected and/or weighted/binary graphs.

Toward Digital Twin Oriented Modeling of Complex Networked Systems and Their Dynamics: A Comprehensive Survey

“…8, all the simulated social networks share the same values of the edge number and density within each iteration under the assumed 4% edges (22 edges) to be rewired.The number of edges and the density of networks approach the values of the target network in the 4 th iteration. nodes tend to link to other nodes with the same or similar degree, indicating a stronger assortative mixing pattern [62]. As is shown in Fig.…”

“…For SNSs, which generate simulated or hybrid networks to deal with the unobservable information, their assessment involves network statistics like density, degree distribution, shortest path, assortativity, modularity, clustering coefficient and betweenness [62]. Most SNSs employ network statistics concerned with the global network structures like degree centrality, betweenness centrality, and PageRank coefficient [3] to measure the similarity between the social network simulations and the target network.…”

“…We propose the composite performance indexes to measure the similar-ity of social network simulations and the target network. composite performance indexes measure the distance ||G k − Ĝ|| between the simulated social network G k and the target network Ĝ based on their similarity from both the global and the local perspectives [62]. As shown in equation 9, the composite performance index is calculated as the average distance between the simulated and the real networks in terms of each network measure η i .…”

“…The global perspective, concerned with the high level outcomes of interactions between the number of actors, includes measures such as node degree distribution, shortest path length distribution and the values of modularity and assortativity [62]. The local perspective, connected with interpersonal relations within subgraphs, includes measures such as the clustering coefficient distribution and the triad significance profile [37,38,60,62].…”

“…Density of networks is calculated as a fraction of existing edges to all possible connections between all node pairs [62]. The density of networks increases with more edges added over the iterations.…”