Algorithmik sozialer Netzwerke

• Applicant: Professor Dr. Ulrik Brandes
• Subject Area: Theoretical Computer Science
• Term: from 2010 to 2017
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 159165670

• Final Report Year: 2019

Final Report Abstract

Social Network Algorithmics is a methodological project aimed at bridging the gap between social theory and network-analytic methods. Major categories in the analysis of networks are indicators of micro- and macro-structural features such as triad census, degree distribution, or core-periphery structure, indices of centrality such as degree, closeness, or betwenness centrality, assignments of roles such as structural or regular equivalence, and partitions into relatively cohesive subsets such as modularity clustering. In applied settings, substantive arguments usually motivate why an analytic category may be relevant but stop short of identifying appropriate methods in that category. Since results crucially depend on the choice of method, network analyses are often considered to be problematic. With a focus on social networks and social theory, this project developed an analytic pipeline of smaller-scale elements by which the process of analysis and method selection can be structured. Organized around the central notion of network positions, i.e., the observed or derived relationships actors have with all others, the positional approach explicates assumptions and therefore unveils opportunities for theorizing and empirical testing. As a byproduct, it enables more general mathematical statements and identifies algorithmic and statistical challenges. Although the project has concluded, it sparked a long-term research agenda.

Publications

• Studying Social Networks – A Guide to Empirical Research. Campus, Frankfurt/New York, 2012
  Marina Hennig, Ulrik Brandes, Jürgen Pfeffer, and Ines Mergel
  
• Was messen Zentralitätsindizes? In Marina Hennig and Christian Stegbauer, editors, Die Integration von Theorie und Methode in der Netzwerkforschung, pages 33–52. Springer VS, 2012
  Ulrik Brandes, Sven Kosub, and Bobo Nick
  
• Link prediction with social vector clocks. In Proceedings of the 19th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD 2013), pages 784–792, 2013
  Conrad Lee, Bobo Nick, Ulrik Brandes, and Pádraig Cunningham
  
• Relative importance of effects in stochastic actor-oriented models. Network Science, 1(3):278–304, 2013
  Natalie Indlekofer and Ulrik Brandes
  
• Simmelian backbones: Amplifying hidden homophily in facebook networks. In Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Network Analysis and Mining (ASONAM 2013), pages 525–532. IEEE, 2013
  Bobo Nick, Conrad Lee, Pádraig Cunningham, and Ulrik Brandes
  
• What is network science? Network Science, 1(1):1–15, 2013
  Ulrik Brandes, Garry Robins, Ann McCranie, and Stanley Wasserman
  
• Smallest graphs with distinct singleton centers. Network Science, 2(3):416–418, 2014
  Ulrik Brandes and Jan Hildenbrand
  
• Graph based relational features for collective classification. In Proceedings of the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015), volume 9078 of Lecture Notes in Computer Science, pages 447–458. Springer-Verlag, 2015
  Immanuel Bayer, Uwe Nagel, and Steffen Rendle
  
• Untangling the hairballs of multi-centered, small-world online social media networks. Journal of Graph Algorithms and Applications, 19(2):595–618, 2015
  Arlind Nocaj, Mark Ortmann, and Ulrik Brandes
  
• Investigating link inference in partially observable networks: Friendship ties and interaction. IEEE Transactions on Computational Social Systems, 3(3):113–119, 2016
  Mehwish Nasim, Raphaël Charbey, Christophe Prieur, and Ulrik Brandes
  
• Maintaining the duality of closeness and betweenness centrality. Social Networks, 44:153–159, 2016
  Ulrik Brandes, Stephen P. Borgatti, and Linton C. Freeman
  
• Network positions. Methodological Innovations, 9:1–19, 2016
  Ulrik Brandes
  
• Re-conceptualizing centrality in social networks. European Journal of Applied Mathematics, 27(6):971–985, 2016
  David Schoch and Ulrik Brandes
  
• Correlations among centrality indices and a class of uniquely ranked graphs. Social Networks, 50:46–54, 2017
  David Schoch, Thomas W. Valente, and Ulrik Brandes