Static and Dynamic Aspects of Scientific Collaboration Networks

Collaboration networks arise when we map the connections between scientists which are formed through joint publications. These networks thus display the social structure of academia, and also allow conclusions about the structure of scientific knowledge. Using the computer science publication database DBLP, we compile relations between authors and publications as graphs and proceed with examining and quantifying collaborative relations with graph-based methods. We review standard properties of the network and rank authors and publications by centrality. Additionally, we detect communities with modularity-based clustering and compare the resulting clusters to a ground-truth based on conferences and thus topical similarity. In a second part, we are the first to combine DBLP network data with data from the Dagstuhl Seminars: We investigate whether seminars of this kind, as social and academic events designed to connect researchers, leave a visible track in the structure of the collaboration network. Our results suggest that such single events are not influential enough to change the network structure significantly. However, the network structure seems to influence a participant's decision to accept or decline an invitation.Comment: ASONAM 2012: IEEE/ACM International Conference on Advances in Social Networks Analysis and Minin

Animating the development of Social Networks over time using a dynamic extension of multidimensional scaling

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 stress-minimization 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

Developing a Conceptual Framework for Modeling Deviant Cyber Flash Mob: A Socio-Computational Approach Leveraging Hypergraph Constructs

In a Flash Mob (FM) a group of people get together in the physical world perform an unpredicted act and disperse quickly. Cyber Flash Mob (CFM) is the cyber manifestation of flash mob coordinated primarily using social media. Deviant Cyber Flash Mob (or, DCFM) is a special case of CFM, which is categorized as the new face of transnational crime organizations (TCOs). The DCFM phenomenon can be considered as a form of a cyber-collective action that is defined as an action aiming to improve group’s conditions (such as, status or power). In this paper, we conduct a conceptual analysis of the DCFMs and model the factors that lead to success or failure with groundings in collective action and collective identity formation theories. Mathematical constructs of hypergraph are leveraged to represent the complex relations observed in the DCFM social networks. The model’s efficacy is demonstrated through a test scenario

Centrality in Politics: How Networks Confer Power

A traditional view of power in politics is that it comes from the possession of important resources. The relative possession of resources is thought to provide actors such as people, organizations, and states with means of coercion or influence over others. This traditional view is highly limiting, since power also comes from ties (patterns of association) that link together actors in networks. These ties, whether material (like trade flows) or social (like friendship), determine an actor’s ability to have access to, make connections between, or quickly spread resources to, other actors. An actor’s relative position in a network formed by these ties thus provides another important source of influence over others. In this article, we introduce three classes of network centrality positions (degree, betweenness, and closeness), explain the advantages of each, and demonstrate that network notions of power that derive from centrality can significantly inform the study of politics