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

Experiments on comparing graph clusterins

A promising approach to compare graph clusterings is based on using measurements for calculating the distance. Existing measures either use the structure of clusterings or quality--based aspects. Each approach suffers from critical drawbacks. We introduce a new approach combining both aspects and leading to better results for comparing graph clusterings. An experimental evaluation of existing and new measures shows that the significant drawbacks of existing techniques are not only theoretical in nature and proves that the results of our new measures are more coherent with intuition

A new paradigm for complex network visualization

We propose a new layout paradigm for drawing a nested decomposition of a large network. The visualization supports the recognition of abstract features of the decomposition, while drawing all elements. In order to support the visual analysis that focuses on the dependencies of the individual parts of the decomposition, we use an annulus as the general underlying shape. This method has been evaluated using real world data and offers surprising readability

Analysis of the autonomous system network and of overlay networks using visualization

Taking the physical Internet at the Autonomous System (AS) level as an instance of a complex network, and Gnutella as a popular peer-to-peer application running on top of it, we investigated the correlation of overlay networks with their underlying topology using visualization. We find that while overlay networks create arbitrary topologies, they differ from randomly generated networks, and there is a correlation with the underlying network. In addition, we successfully validated the applicability of our visualization technique for AS topologies by comparing Routeviews data sets with DIMES data sets, and by analyzing the temporal evolution in the Routeviews data sets

On Modularity - NP-Completeness and Beyond

Modularity is a recently introduced quality measure for graph clusterings. It has immediately received considerable attention in several disciplines, and in particular in the complex systems literature, although its properties are not well understood. We here present first results on the computational and analytical properties of modularity. The complexity status of modularity maximization is resolved showing that the corresponding decision version is NP-complete in the strong sense. We also give a formulation as an Integer Linear Program (ILP) to facilitate exact optimization, and provide results on the approximation factor of the commonly used greedy algorithm. Completing our investigation, we characterize clusterings with maximum Modularity for several graph families