Read our latest paper titled Social Dynamics of Science in Nature Scientific Reports. Authors Xiaoling Sun, Jasleen Kaur, Staša Milojević, Alessandro Flammini & Filippo Menczer ask, How do scientific disciplines emerge? No quantitative model to date allows us to validate competing theories on the different roles of endogenous processes, such as social collaborations, and exogenous events, such as scientific discoveries. Here we propose an agent-based model in which the evolution of disciplines is guided mainly by social interactions among agents representing scientists. Disciplines emerge from splitting and merging of social communities in a collaboration network. We find that this social model can account for a number of stylized facts about the relationships between disciplines, scholars, and publications. These results provide strong quantitative support for the key role of social interactions in shaping the dynamics of science. While several “science of science” theories exist, this is the first account for the emergence of disciplines that is validated on the basis of empirical data.
In our paper on Competition among memes in a world with limited attention in Nature Scientific Reports, Lilian Weng and coauthors Sandro Flammini, Alex Vespignani, and Fil Menczer report that we can explain the massive heterogeneity in the popularity and persistence of memes as deriving from a combination of the competition for our limited attention and the structure of the social network, without the need to assume different intrinsic values among ideas. The findings have been mentioned in the popular press, including Information Week, The Atlantic, and the Dutch daily NRC.
On December 16, Mark Meiss presented our paper “Modeling Traffic on the Web Graph” (with Bruno, José, Sandro, and Fil) at the 7th Workshop on Algorithms and Models for the Web Graph (WAW 2010), at Stanford. In this paper we introduce an agent-based model that explains many statistical features of aggregate and individual Web traffic data through realistic elements such as bookmarks, tabbed browsing, and topical interests.
Complex Adaptive Systems and Computational Intelligence
The Complex Adaptive Systems and Computational Intelligence (CASCI) group at Indiana University and the Instituto Gulbenkian de Ciencia works on complex networks & systems and their applications to informatics, biology, health, and social systems. We are particularly interested in the informational properties of natural and artificial systems which enable them to adapt and evolve. This means both understanding how information is fundamental for controlling the behavior and evolutionary capabilities of complex systems, as well as abstracting principles from natural systems to produce adaptive information technology.
Our research projects are on complex networks & systems, computational and systems biology, and computational intelligence; all our publications are available online as are news about our group. Additional information available on Luis Rocha’s Website and our group page at the Instituto Gulbenkian de Ciencia.
See our current roster and information on how to join our group. As a group, we are seriously interconnected with other research groups and networks: The Center for Complex Networks and Systems (CNets), the Indiana University Network Science Institute, the Cognitive Science Program, the FLAD Computational Biology Collaboratorium, the Instituto Gulbenkian de Ciência, and the Champalimaud Neuroscience Program.
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We study the structure and dynamics of Web traffic networks based on data from HTTP requests made by users at Indiana University. Gathering anonymized requests directly from the network rather than relying on server logs and browser instrumentation allows us to examine large volumes of traffic data while minimizing biases associated with other data sources. It also gives us valuable referrer information that we can use to reconstruct the subset of the Web graph actually traversed by users.
Our Web traffic (click) dataset is available!
Our goal is to develop a better understanding of user behavior online and creating more realistic models of Web traffic. The potential applications of this analysis 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.
Among our more intriguing findings are that server traffic (as measured by number of clicks) and site popularity (as measured by distinct users) both follow distributions so broad that they lack any well-defined mean. Actual Web traffic turns out to violate three assumptions of the random surfer model: users don’t start from any page at random, they don’t follow outgoing links with equal probability, and their probability of jumping is dependent on their current location. Search engines appear to be directly responsible for a smaller share of Web traffic than often supposed. These results were presented at WSDM2008 (paper | talk).
Another paper (also here; presented at Hypertext 2009) examined the conventional notion of a Web session as a sequence of requests terminated by an inactivity timeout. Such a definition turns out to yield statistics dependent primarily on the timeout value selected, which we find to be arbitrary. For that reason, we have proposed logical sessions defined by the target and referrer URLs present in a user’s Web requests.
Inspired by these findings, we designed a model of Web surfing able to recreate not only the broad distribution of traffic, but also the basic statistics of logical sessions. Late breaking results were presented at WSDM2009. Our final report in the ABC model was presented at WAW 2010.
|Mark Meiss was supported by the Advanced Network Management Laboratory, one of the Pervasive Technology Labs established at Indiana University with the assistance of the Lilly Endowment.|
|This research was also supported in part by the National Science Foundation under awards 0348940, 0513650, and 0705676.|
|This research was also supported in part from the Institute for Information Infrastructure Protection research program. The I3P is managed by Dartmouth College and supported under Award Number 2003-TK-TX-0003 from the U.S. DHS, Science and Technology Directorate.|
Opinions, findings, conclusions, recommendations or points of view of this group are those of the authors and do not necessarily represent the official position of the U.S. Department of Homeland Security, Science and Technology Directorate, I3P, National Science Foundation, or Indiana University.