About: Uncovering Hidden Dependency in Weighted Networks via Information Entropy

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Uncovering Hidden Dependency in Weighted Networks via Information Entropy
Creator	Lee, Eun Lee, Mi Lee, Sang Jeong, Hawoong Lee, Deok-Sun »more»
source	ArXiv
abstract	Interactions between elements, which are usually represented by networks, have to delineate potentially unequal relationships in terms of their relative importance or direction. The intrinsic unequal relationships of such kind, however, are opaque or hidden in numerous real systems. For instance, when a node in a network with limited interaction capacity spends its capacity to its neighboring nodes, the allocation of the total amount of interactions to them can be vastly diverse. Even if such potentially heterogeneous interactions epitomized by weighted networks are observable, as a result of the aforementioned ego-centric allocation of interactions, the relative importance or dependency between two interacting nodes can only be implicitly accessible. In this work, we precisely pinpoint such relative dependency by proposing the framework to discover hidden dependent relations extracted from weighted networks. For a given weighted network, we provide a systematic criterion to select the most essential interactions for individual nodes based on the concept of information entropy. The criterion is symbolized by assigning the effective number of neighbors or the effective out-degree to each node, and the resultant directed subnetwork decodes the hidden dependent relations by leaving only the most essential directed interactions. We apply our methodology to two time-stamped empirical network data, namely the international trade relations between nations in the world trade web (WTW) and the network of people in the historical record of Korea, Annals of the Joseon Dynasty (AJD). Based on the data analysis, we discover that the properties of mutual dependency encoded in the two systems are vastly different.
has issue date	2020-08-25(xsd:dateTime)
has license	arxiv
sha1sum (hex)	d1c81a70702b8a8dd6367aa636bfa3acd7ac3780
resource representing a document's title	Uncovering Hidden Dependency in Weighted Networks via Information Entropy
resource representing a document's body	covid:d1c81a70702b8a8dd6367aa636bfa3acd7ac3780#body_text
is schema:about of	named entity 'relative' named entity 'systems' named entity 'weighted network' named entity 'observable' named entity 'positively correlated' »more»

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