The Center for Complex Networks and Systems Research (CNetS) is part of the School of Informatics and Computing and the Pervasive Technology Institute of Indiana University. The center supports and enhances the research efforts of the complex systems group, which has been active within the School since 2004. CNetS is meant to foster interdisciplinary research in all areas related to complex networks and systems. On this website you can find information on CNetS faculty, research groups, and their activities.

News

Kinsey Reporter App

UPDATE: With legal review completed, we re-launched Kinsey Reporter V.2!

CNetS, in collaboration with The Kinsey Institute, has released Kinsey Reporter, a global mobile survey platform for collecting and sharing anonymous data about sexual and other intimate behaviors. The pilot project allows citizen observers around the world to use free applications now available for Apple and Android mobile platforms to not only report on sexual behavior and experiences, but also to share, explore and visualize the accumulated data.

This new platform will allow us to explore issues that have been challenging to study until now, such as the prevalence of unreported sexual violence in different parts of the world, or the correlation between various sexual practices like condom use, for example, and the cultural, political, religious or health contexts in particular geographical areas.

The Kinsey Institute’s longstanding seminal studies of sexual behaviors created a perfect synergy with research going on at CNetS related to mining big data crowd-sourced from mobile social media. The sensitive domain — sexual relations — added an intriguing challenge in finding a way to share useful data with the community while protecting the privacy and anonymity of the reporting volunteers.

Apps are available for free download at both the Apple iOS and Android app stores — download yours now! (More from IU News Room…)

David Crandall

Congratulations to David Crandall for his NSF CAREER Award! The Faculty Early Career Development (CAREER) Program is a Foundation-wide activity that offers the National Science Foundation’s most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education and the integration of education and research within the context of the mission of their organizations.  David’s project Observing the world through the lenses of social media will lay the foundation for using visual social media as a new source of observational data for a variety of scientific disciplines by investigating the algorithms and technologies needed for mining large collections of photographs and noisy metadata to draw inferences about the physical world. “Every day, millions of people across the world take photos and upload them to social media websites,” David observes. “Their goal is to share photos with friends and others, but collectively they are creating vast repositories of visual information about the world and how it looked across time and space. Aggregated together, these photos could provide new sources of observational data for use in disciplines like biology, earth science, social science or history.” More…

Dynamical Modularity

Read our latest paper titled Canalization and Control in Automata Networks: Body Segmentation in Drosophila melanogaster in PLoS ONE. Authors Manuel Marques-Pita & Luis Rocha ask, How do cells and tissues ‘compute’? Schema redescription is presented as a methodology to characterize canalization in automata networks used to model biochemical regulation and signalling. Canalization becomes synonymous with redundancy present in the logic of automata. This results in straightforward measures to quantify canalization in an automaton (micro-level), which is in turn integrated into a highly scalable framework to characterize the collective dynamics of large-scale automata networks (macro-level). Several new results ensue from this methodology developed as part of the CASCI collective dynamics project: uncovering of dynamical modularity (modules in the dynamics rather than in the structure of networks), identification of minimal conditions and critical nodes to control the convergence to attractors, simulation of dynamical behaviour from incomplete information about initial conditions, and measures of macro-level canalization and robustness to perturbations. The methodology is applicable to any complex network that can be modelled using automata, but this work focuses on biochemical regulation and signalling, with a well-known model of the intra- and inter cellular genetic regulation of body segmentation in Drosophila melanogaster.

The project “Social SLAM: Creating Dynamical Socio-Environmental Models for Mobile Robots”, a collaboration between Luis Rocha, Selma Sabanovic, Matt Francisco, and Alin Cosmanescu, has received an IUCRG grant for 2013-2014 from the Office of the Vice President for Research at Indiana University.

The pharmacokinetics ontology and corpus for text mining developed in collaboration with Li’s lab at IUPUI, part of CASCI Biomedical Literature Mining work, has been reported in BMC Bioinformatics where it has become a Highly Accessed paper:

Wu, Hengyi, S. Karnik, A. Subhadarshini, Z. Wang, S. Philips, X. Han, C. Chiang, L. Liu, M. Boustani, L.M. Rocha, S.K. Quinney, D.A. Flockhart and L. Li [2013]. “An Integrated Pharmacokinetics Ontology and Corpus for Text Mining”. BMC Bioinformatics. 14:35. DOI:10.1186/1471-2105-14-35.

IU Click Collection System

To foster the study of the structure and dynamics of Web traffic networks, we are making available to the research community a large Click Dataset of 13 53.5 billion HTTP requests collected at Indiana University. Between 2006 and 2010, our system generated data at a rate of about 60 million requests per day, or about 30 GB/day of raw data. We hope that this data will help develop a better understanding of user behavior online and create more realistic models of Web traffic. The potential applications of this data include improved designs for networks, sites, and server software; more accurate forecasting of traffic trends; classification of sites based on the patterns of activity they inspire; and improved ranking algorithms for search results.