Schedule


Monday Introduction to Network Analysis (Nina)

09:00-10:30

Some examples of network analysis, general approach of network analysis: structural analysis in comparison with appropriate random graph model, model of newly found structures, interdependence of structure and function relationship.
10:45-12:15
Introduction to graph theory (graph, paths, distance, connected component, directed, weighted, bipartite, partition, cut, BFS/DFS), representation of graphs, graph formats, trees, planar graphs, cliques

    Centrality Indices (Sudarshan)

13:15-14:45

General idea, facility location, definition of centrality (together with concept of isomorphism!), eccentricity, closeness/nearness, betweenness,
15:00-16:30
        Human navigation in complex networks



Tuesday: Centrality Indices (Sudarshan)

09:00-10:30
       Status index of Katz, Bonacich's centrality, Pagerank algorithm.     

10:45-12:15
Borgatti-paper. Applications of centrality indices to networks

    General Patterns in and Network Models for Real-World networks
13:15-14:45

        General patterns in real-world networks: small-worlds, scale-free networks, degree distribution, hierarchical networks,
        clustering coefficient, fractal networks, assortativity. How to find out whether a scale-free network is really scale-free.


15:00-16:30

Network models: small-worlds, preferential attachment, hierarchical, random graphs with given degree distributions, bipartite graphs with given degree distributions

Wednesday: Clustering methods (Sudarshan)

09:00-10:30
Idea of clustering, "definitions", clustering quality measures, hierarchical clustering + level selection problem
10:45-12:15
Clustering algorithms: top-down betweenness centrality, bottom-up modularity, overlapping clusters

13:15-14:45
Applications to biological, social, and other networks
15:00-16:30
Clustering of bipartite networks (Nina)

Thursday:
Programs to Analyze Networks - some hands-on experience (Nina, Sudarshan, Agnes)


09:00-12:00

Writing small Python scripts for network analysis

13:15-14:45
        Clustering can be easy, too. :-) MaxMinDistance-Clustering and k-Cores; Network Workbench.

15:00-16:30

        Pajek, Palla's clustering and CFinder, Motif identification and mfinder/mdraw, graph visualization and yEd

Friday: Processes on Networks

09:00-10:30
Reading club session: Robustness and resilience, percolation, epidemcis
10:45-12:15
Presentation and Discussion

13:15-14:45
Synchronization, Albert's paper, Search (Kleinberg paper, Adamic paper)
 15:00-16:30 
Open questions: unified framework, clustering/centrality, dynamic networks, multi-relationship data