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Volumn 82, Issue 6, 2010, Pages

Exploring the randomness of directed acyclic networks

Author keywords

[No Author keywords available]

Indexed keywords

ACYCLIC NETWORKS; CELL LINEAGE; CITATION NETWORKS; COMPONENT DISTRIBUTIONS; CONFIGURATION MODEL; DEGREE SEQUENCE; DIRECTED ACYCLIC GRAPHS; ELEGANS; FEED-FORWARD; FINITE SIZE; FOOD WEBS; NULL MODEL; REAL SYSTEMS; TIME-DEPENDENT PROCESS; TOPOLOGICAL INVARIANTS;

EID: 78651439508     PISSN: 15393755     EISSN: 15502376     Source Type: Journal    
DOI: 10.1103/PhysRevE.82.066115     Document Type: Article
Times cited : (13)

References (33)
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    • ij =1 position in A and m and n are the numbers obtained in the randomization method. For the sake of simplicity we avoid the use of this double notation saying that node position is invariant in the matrix.
    • i j = 1 position in A and m and n are the numbers obtained in the randomization method. For the sake of simplicity we avoid the use of this double notation saying that node position is invariant in the matrix.
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    • note
    • A measure quantifying how random or how deterministic is a structure in relation to the space allowed by the topological invariants was required. The degree-degree joint entropy of a graph holds this property. Other valuable measures, such as assortativity or mutual information, have been pointed out. Assortativity measures degree-degree correlations and degree-degree mutual information quantifies the predictability of neighbors' degrees from the sole knowledge of the degree of a given node in relation to the available degree richness of the system. The former case strictly looks for linear relationships, and it is supposed to be a more appropriate measure for normally distributed data. Furthermore, both approaches naturally require certain degree-degree variance within the graph. For instance, a large feed-forward single chain of nodes has a strong degree-degree determinism that none of these two measurements would capture. The reason is that most of the degree-degree pairs would be (2,2) for undirected and (1,1) for any directed degree analyses. In this sense, degree-degree joint entropy provides a suitable measure of the relation or determinism of degree-degree relations with neither parametric assumptions nor degree-degree variance requisites. Accordingly, we used the concept of degree-degree relations instead of degree-degree correlations.
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* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.