NDLI: Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Content Provider	IEEE Xplore Digital Library
Author	Bailey, A. Ventresca, M. Ombuki-Berman, B.
Copyright Year	1997
Abstract	Complex networks are becoming an integral tool for our understanding of an enormous variety of natural and artificial systems. A number of human-designed network generation procedures have been proposed that reasonably model specific real-life phenomena in structure and dynamics. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications, and the social sciences have recently resulted. A graph model is an algorithm capable of constructing arbitrarily sized networks, whose end structure will exhibit certain statistical and structural properties. The process of deriving an accurate graph model is very time intensive and challenging and may only yield highly accurate models for very specific phenomena. An automated approach based on genetic programming was recently proposed by the authors. However, this initial system suffered from a number of drawbacks, including an under-emphasis on creating hub vertices, the requirement of user intervention to determine objective weights, and the arbitrary approach toward selecting the most representative model from a population of candidate models. In this paper, we propose solutions to these problems and show experimentally that the new system represents a significant improvement and is very capable of reproducing existing common graph models from even a single small initial network.
Starting Page	405
Ending Page	419
Page Count	15
File Size	12543116
File Format	PDF
ISSN	1089778X
Volume Number	18
Issue Number	3
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Biological system modeling Complex networks Sociology Statistics Genetic programming Computational modeling Educational institutions Evolutionary Computation Automatic programming evolutionary computation complex networks
Content Type	Text
Resource Type	Article
Subject	Theoretical Computer Science Computational Theory and Mathematics Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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