Connecting network science and information theory

Author(s)

de Arruda, Henrique F. ^[1] ; Silva, Filipi N. ^{[2, 3]} ; Comin, Cesar H. ^[2] ; Amancio, Diego R. ^{[1, 3]} ; Costa, Luciano da F. ^[2]

Affiliation

^[1] Univ Sao Paulo, Inst Math & Comp Sci, Sao Carlos, SP - Brazil

^[2] Univ Sao Paulo, Sao Carlos Inst Phys, Sao Carlos, SP - Brazil

^[3] Indiana Univ, Sch Informat Comp & Engn, Bloomington, IN - USA

• Document type: Journal article
• Source: PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS; v. 515, p. 641-648, FEB 1 2019.
• Web of Science Citations: 0

Abstract

A framework integrating information theory and network science is proposed. By incorporating and integrating concepts such as complexity, coding, topological projections and network dynamics, the proposed network-based framework paves the way not only to extending traditional information science, but also to modeling, characterizing and analyzing a broad class of real-world problems, from language communication to DNA coding. Basically, an original network is supposed to be transmitted, with or without compaction, through a sequence of symbols or time-series obtained by sampling its topology by some network dynamics, such as random walks. We show that the degree of compression is ultimately related to the ability to predict the frequency of symbols based on the topology of the original network and the adopted dynamics. The potential of the proposed approach is illustrated with respect to the efficiency of transmitting several types of topologies by using a variety of random walks. Several interesting results are obtained, including the behavior of the Barabasi Albert model oscillating between high and low performance depending on the considered dynamics, and the distinct performances obtained for two geographical models. (C) 2018 Elsevier B.V. All rights reserved. (AU)

• FAPESP's process: 15/18942-8 - Associating Complex Networks with Effective Feature Spaces
• Grantee: Cesar Henrique Comin
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 16/19069-9 - Using semantical information to classify texts modelled as complex networks
• Grantee: Diego Raphael Amancio
• Support Opportunities: Regular Research Grants
• FAPESP's process: 14/20830-0 - Using complex networks to recognize patterns in written texts
• Grantee: Diego Raphael Amancio
• Support Opportunities: Regular Research Grants
• FAPESP's process: 15/08003-4 - Complex network approach to e-Science and dynamic datasets
• Grantee: Filipi Nascimento Silva
• Support Opportunities: Scholarships in Brazil - Post-Doctoral
• FAPESP's process: 11/50761-2 - Models and methods of e-Science for life and agricultural sciences
• Grantee: Roberto Marcondes Cesar Junior
• Support Opportunities: Research Projects - Thematic Grants