CINET: A cyberinfrastructure for network science

Networks are an effective abstraction for representing real systems. Consequently, network science is increasingly used in academia and industry to solve problems in many fields. Computations that determine structure properties and dynamical behaviors of networks are useful because they give insights into the characteristics of real systems. We introduce a newly built and deployed cyberinfrastructure for network science (CINET) that performs such computations, with the following features: (i) it offers realistic networks from the literature and various random and deterministic network generators; (ii) it provides many algorithmic modules and measures to study and characterize networks; (iii) it is designed for efficient execution of complex algorithms on distributed high performance computers so that they scale to large networks; and (iv) it is hosted with web interfaces so that those without direct access to high performance computing resources and those who are not computing experts can still reap the system benefits. It is a combination of application design and cyberinfrastructure that makes these features possible. To our knowledge, these capabilities collectively make CINET novel. We describe the system and illustrative use cases, with a focus on the CINET user.

[1]  Edward A. Lee,et al.  CONCURRENCY AND COMPUTATION: PRACTICE AND EXPERIENCE Concurrency Computat.: Pract. Exper. 2000; 00:1–7 Prepared using cpeauth.cls [Version: 2002/09/19 v2.02] Taverna: Lessons in creating , 2022 .

[2]  Joseph M. Hellerstein,et al.  Distributed GraphLab: A Framework for Machine Learning in the Cloud , 2012, Proc. VLDB Endow..

[3]  Thomas W. Valente,et al.  Social Networks and Health , 2010 .

[4]  Edward A. Lee,et al.  Scientific workflow management and the Kepler system , 2006, Concurr. Comput. Pract. Exp..

[5]  Network Workbench Tool , 2014, Encyclopedia of Social Network Analysis and Mining.

[6]  Edward A. Fox,et al.  SimDL: a model ontology driven digital library for simulation systems , 2011, JCDL '11.

[7]  Na Li,et al.  oreChem ChemXSeer: a semantic digital library for chemistry , 2010, JCDL '10.

[8]  Edward A. Fox,et al.  Theoretical Foundations for Digital Libraries: The 5S (Societies, Scenarios, Spaces, Structures, Streams) Approach , 2012, Theoretical Foundations for Digital Libraries.

[9]  Rami Puzis,et al.  A Decision Support System for Placement of Intrusion Detection and Prevention Devices in Large-Scale Networks , 2011, TOMC.

[10]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[11]  Vladimir Batagelj,et al.  Pajek - Program for Large Network Analysis , 1999 .

[12]  Jan Medlock,et al.  Backward bifurcations and multiple equilibria in epidemic models with structured immunity. , 2008, Journal of theoretical biology.

[13]  E. David,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World , 2010 .

[14]  Edward A. Fox,et al.  An Extensible Digital Library Service to Support Network Science , 2013, ICCS.

[15]  Mark S. Granovetter Threshold Models of Collective Behavior , 1978, American Journal of Sociology.

[16]  Madhav V. Marathe,et al.  PATRIC: a parallel algorithm for counting triangles in massive networks , 2013, CIKM.

[17]  Angelo Cangelosi,et al.  Computersimulation:anewscientific Approachtothestudyoflanguageevolution Anewapproachtothestudyoflanguage Evolution:computersimulation , 2001 .

[18]  Aric Hagberg,et al.  Exploring Network Structure, Dynamics, and Function using NetworkX , 2008, Proceedings of the Python in Science Conference.

[19]  Jin Zhao,et al.  Math information retrieval: user requirements and prototype implementation , 2008, JCDL '08.

[20]  Thomas W. Valente,et al.  Social Networks and Health: Models, Methods, and Applications , 2010 .

[21]  E. Todeva Networks , 2007 .

[22]  T. Schelling Micromotives and Macrobehavior , 1978 .

[23]  Madhav V. Marathe,et al.  Generation and analysis of large synthetic social contact networks , 2009, Proceedings of the 2009 Winter Simulation Conference (WSC).

[24]  S. Fortunato,et al.  Statistical physics of social dynamics , 2007, 0710.3256.

[25]  Ross Wilkinson,et al.  Discovering Australia's research data , 2010, JCDL '10.

[26]  Réka Albert,et al.  A network model for plant–pollinator community assembly , 2010, Proceedings of the National Academy of Sciences.

[27]  Edward A. Fox,et al.  Streams, structures, spaces, scenarios, societies (5s): A formal model for digital libraries , 2004, TOIS.

[28]  Gilles Clermont,et al.  A Dynamical Model of Human Immune Response to Influenza a Virus Infection , 2006 .

[29]  M. Macy,et al.  Complex Contagions and the Weakness of Long Ties1 , 2007, American Journal of Sociology.

[30]  Matthew Roughan,et al.  The Internet Topology Zoo , 2011, IEEE Journal on Selected Areas in Communications.

[31]  Kunle Olukotun,et al.  Green-Marl: a DSL for easy and efficient graph analysis , 2012, ASPLOS XVII.

[32]  Warren Smith,et al.  Design of the FutureGrid experiment management framework , 2010, 2010 Gateway Computing Environments Workshop (GCE).

[33]  Mathieu Bastian,et al.  Gephi: An Open Source Software for Exploring and Manipulating Networks , 2009, ICWSM.

[34]  Milind Tambe,et al.  Empirical Evaluation of Computational Emotional Contagion Models , 2011, IVA.

[35]  Samarth Swarup,et al.  A general-purpose graph dynamical system modeling framework , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[36]  Yamir Moreno,et al.  The Dynamics of Protest Recruitment through an Online Network , 2011, Scientific reports.

[37]  S. Kasif,et al.  Whole-genome annotation by using evidence integration in functional-linkage networks. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[38]  Pamela Oliver,et al.  Working Paper and Technical Report Series the Opposing Forces Diffusion Model: the Initiation and Repression of Collective Violence , 2022 .

[39]  Madhav V. Marathe,et al.  Distributed-memory parallel algorithms for generating massive scale-free networks using preferential attachment model , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[40]  Matthew S. Mayernik,et al.  Drowning in data: digital library architecture to support scientific use of embedded sensor networks , 2007, JCDL '07.

[41]  Matthew S. Shields,et al.  Triana: a graphical Web service composition and execution toolkit Web Services , 2004 .

[42]  Yolanda Gil,et al.  Pegasus and the Pulsar Search: From Metadata to Execution on the Grid , 2003, PPAM.

[43]  Chris Arney,et al.  Networks, Crowds, and Markets: Reasoning about a Highly Connected World (Easley, D. and Kleinberg, J.; 2010) [Book Review] , 2013, IEEE Technology and Society Magazine.

[44]  Cecelia DeLuca,et al.  Earth system curator: metadata infrastructure for climate modeling , 2008, Earth Sci. Informatics.

[45]  Samarth Swarup,et al.  Inhibiting the Diffusion of Contagions in Bi-Threshold Systems: Analytical and Experimental Results , 2011, AAAI Fall Symposium: Complex Adaptive Systems.