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Dagstuhl Seminar 11452

Analysis of Dynamic Social and Technological Networks

( Nov 08 – Nov 11, 2011 )

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In the recent years, we have witnessed an increasing interest in the analysis of complex networks, i.e., networks composed of many interacting entities that show emergent behavior at global level. Usually, the key features of these networks can be captured by means of a statistical characterisation of their properties at local level such as their degree distribution and clustering coefficient. Models can then be built in order to describe the system in its entirety and to study the processes taking place on it, also over time. These models can be used to understand several phenomena dependent on the structure and dynamics of these networked systems, such as the spreading of computer viruses.

One of the main motivations of the explosion of interest in social networks that we are seeing is the availability of large data sets, e.g., the "snapshots" of the Internet structure or the maps of online social networks obtained by crawling extremely popular Web sites like Facebook and Twitter. Moreover, many data collections exercises of data sets related to human interactions by means of Bluetooth radio or GPS receivers have been carried in the recent years. These large data sets provide information that is not limited to a particular instant of time, but cover a very large time interval as well as fine grained space information. These data sets can be used to study the evolution of the network and dynamic processes happening over time such as simulated epidemics. Other large data sets that have attracted considerable interest include biological, commodity and economic networks. The initial research efforts have been focussed on the analysis of the static properties of these networks, including the presence of clusters, hubs, and community structure, More recently, researchers became interested in studying dynamic processes taking place on these networks such as information diffusion in the Internet, disease epidemics and malware propagation.

The goal of this seminar was broad, including both mathematical aspects and practical applications of theoretical models and techniques. The seminar focused on two key classes of networks that are of fundamental importance not only in computer science but also in the everyday life of millions of people, namely technological networks and online social networks.

The main contributions of this seminar were:

  • Presenting a wide range of recent research results on the dynamics of processes and structure of technological and social networks.
  • Exchanging solutions and practices in the different areas of computer science and other disciplines in order to find novel solutions and start fruitful long-term collaborations among the seminar attendants.
  • Exploring the new challenges and opportunities arising from the analysis of data from mobile devices and social network tools, which offer the change to collect very rich data sets of information about the everyday life of people including their movements, their contacts and their social network.
  • Discussing how the social networks extracted from mobile device interactions are time and location dependent, requiring new models and techniques to study them.
  • Examining the application of machine learning and data mining techniques to the analysis of technological and social networks, bringing together researchers and practitioners working on the massive available networking data sets and machine learning experts interested in real-world problems.
  • Studying and considering the computational challenges presented by the scale of these data sets, which impose the design of novel algorithms and the rethinking of existing techniques.
  • Discussing the ethical problems arising from the treatment of privacy-sensitive user data and potential technical and legal solutions to overcome them.

  • Ginestra Bianconi (Northeastern University - Boston, US) [dblp]
  • Vincent D. Blondel (UC Louvain-la-Neuve, BE)
  • Sonja Buchegger (KTH Royal Institute of Technology, SE) [dblp]
  • Krishna P. Gummadi (MPI-SWS - Saarbrücken, DE) [dblp]
  • Des Higham (The University of Strathclyde - Glasgow, GB)
  • Petter Holme (University of Umeå, SE)
  • Byungnam Kahng (Seoul National University, KR)
  • Renaud Lambiotte (Imperial College London, GB) [dblp]
  • Cecilia Mascolo (University of Cambridge, GB) [dblp]
  • Alan Mislove (Northeastern University - Boston, US)
  • Mirco Musolesi (University of Birmingham, GB)
  • Vincenzo Nicosia (University of Catania, IT)
  • Anastasios Noulas (University of Cambridge, GB)
  • Eamonn O'Neill (University of Bath, GB)
  • Alessandro Panconesi (Sapienza University of Rome, IT) [dblp]
  • Daniele Quercia (University of Cambridge, GB) [dblp]
  • Jason Rentfrow (University of Cambridge, GB)
  • Stefan Saroiu (Microsoft Corporation - Redmond, US)
  • Salvatore Scellato (University of Cambridge, GB)
  • Roberta Sinatra (University of Catania, IT)
  • Michael Sirivianos (Telefonica Research - Barcelona, ES) [dblp]
  • Thorsten Strufe (TU Darmstadt, DE) [dblp]
  • John Tang (University of Cambridge, GB)
  • Walter Willinger (AT&T Labs Research - Florham Park, US) [dblp]

  • artificial intelligence/robotics
  • mobile computing
  • networks
  • society
  • HCI
  • modeling/simulation

  • Complex Networks
  • Social Networks
  • Technological Networks
  • Network Analysis
  • Network Data Mining