Social interactions are conduits for various processes spreading through a population, from rumors and opinions to behaviors and diseases. In the context of the spread of a disease or undesirable behavior, it is important to identify blockers: individuals that are most effective in stopping or slowing down the spread of a process through the population. This problem has so far resisted systematic algorithmic solutions. In an effort to formulate practical solutions, in this paper we ask: Are there structural network measures that are indicative of the best blockers in dynamic social networks? Our contribution is two-fold. First, we extend standard structural network measures to dynamic networks. Second, we compare the blocking ability of individuals in the order of ranking by the new dynamic measures. We found that overall, simple ranking according to a node’s static degree, or the dynamic version of a node’s degree, performed consistently well. Surprisingly the dynamic clustering coefficient seems to be a good indicator, while its static version performs worse than the random ranking. This provides simple practical and locally computable algorithms for identifying key blockers in a network.

The animation of network visualizations poses technical and theoretical challenges. Rather stable patterns are required before the mental map enables a user to make inferences over time. In order to enhance stability, we developed an extension of stressminimization with developments over time. This dynamic layouter is no longer based on linear interpolation between independent static visualizations, but change over time is used as a parameter in the optimization. Because of our focus on structural change versus stability the attention is shifted from the relational graph to the latent eigenvectors of matrices. The approach is illustrated with animations for the journal citation environments of Social Networks, the (co-)author networks in the carrying community of this journal, and the topical development using relations among its title words. Our results are also compared with animations based on PajekToSVGAnim and SoNIA.

There are several types of processes which can be modeled explicitly by recording the interactions between a set of actors over time. In such applications, a common objective is, given a series of observations, to predict exactly when certain interactions will occur in the future. We propose a representation for this type of temporal data and a generic, streaming, adaptive algorithm to predict the pattern of interactions at any arbitrary point in the future. We test our algorithm on predicting patterns in e-mail logs, correlations between stock closing prices, and social grouping in herds of Plains zebras. Our algorithm averages over 85 % accuracy in predicting a set of interactions at any unseen timestep. To the best of our knowledge, this is the first algorithm that predicts interactions at the finest possible time grain.

In this paper we propose three methods of measuring betweenness of individuals in networks which are best modeled as graphs with explicit time ordering on their edges. The betweenness centrality index is one of the basic measure in the analysis of social networks, but most of the work done for measuring the betweenness index of individuals is based on the aggregate representation of the network. Many network problems are based on fundamental relationship involving time. We incorporate the time factor in the aggregate graph representation of social networks to create dynamic networks. We define and measure the betweenness in this dynamic framework. We compare the three betweenness with the standard betweenness measure for the same network. We show that by incorporating the exact times of interactions among individuals in a network, we can better study the betweenness of individuals in the In this paper, we extend the study of betweenness centrality of individuals in social networks to networks which are explicitly dynamic. The idea of representing societies as networks of interacting individuals dates back to

We analyze knowledge production in Computer Science by means of coauthorship networks. For this, we consider 30 graduate programs of different regions of the world, being 8 programs in Brazil, 16 in North America (3 in Canada and 13 in the United States), and 6 in Europe (2 in France, 1 in Switzerland and 3 in the United Kingdom). We use a dataset that consists of 176,537 authors and 352,766 publication entries distributed among 2,176 publication venues. The results obtained for different metrics of collaboration social networks indicate the process of knowledge production has changed differently for each region. Research is increasingly done in teams across different fields of Computer Science. The size of the giant component indicates the existence of isolated collaboration groups in the European network, contrasting to the degree of connectivity found in the Brazilian and North-American counterparts. We also analyzed the temporal evolution of the social networks representing the three regions. The number of authors per paper experienced an increase in a time span of 12 years. We observe that the number of collaborations between authors grows faster than the number of authors, benefiting from the existing network structure. The temporal evolution shows differences between well-established fields, such as Databases and Computer Architecture, and emerging fields, like Bioinformatics and Geoinformatics. The patterns of collaboration analyzed in this paper contribute to an overall understanding of Computer Science research in different geographical regions that could not be achieved without the use of complex networks and a large publication database.

Since research trends can change dynamically, researchers have to keep up with these new trends and undertake new research topics. Therefore, research communities for new research domains are important. In this paper, we propose a method to discover research communities. The key features of our method are a network model of papers and a word assignment technique for the communities obtained. We show our system based on the proposed method and discuss our system through case studies and experiments. 1

The VIVO project is creating an open, Semantic Web-based network of institutional ontology-driven databases to enable national discovery, networking, and collaboration via information sharing about researchers and their activities. The project has been funded by NIH to implement VIVO at the University of Florida, Cornell University, and Indiana University Bloomington together with four other partner institutions. Working with the Semantic Web/Linked Open Data community, the project will pilot the development of common ontologies, integration with institutional information sources and authentication, and national discovery and exploration of networks of researchers. Building on technology developed over the last five years at Cornell University, VIVO supports the flexible description and interrelation of people, organizations, activities, projects,

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This position paper builds on Ann Carlson’s summary of the results from the Atlanta workshop that has been distributed as Discussion Questions for GSF Workshop06-12.pdf. It starts by introducing a technologically very feasible ‘dream tool ’ for science policy makers and many other stakeholders interested in more effective knowledge management and utilization. It lays out the rationale and sketches the design of a socio-technical cyberinfrastructure that supports the storage, integration, collective annotation, analysis, modeling, and visual communication/exploration of terabytes and soon petabytes of relevant data. Maps of science are introduced as a means to interlink, make sense, and communicate complex datasets. Models of science are discussed as a way to gain a deeper understanding of the inner workings of science. The paper concludes by suggesting next steps. Please note that my main expertise is in scholarly knowledge management and the mapping and modeling of science. The approaches suggested/examples used here have been tested in/drawn from this application domain. The opinions, findings, conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation or any

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