Introduction to network science. Graph theory. Real-world networks.
Node position. Spectral and distance node centrality. Clustering coefficients. Link analysis algorithms.
Link importance. Betweenness and bridgeness link centrality. Embeddedness and topological overlap.
Node similarity. Local and global node similarity. Structural and regular equivalence.
Node fragments. Egonets analysis. Network motifs and graphlets. Convex subgraphs. Node orbit distributions.
Graph partitioning. Graph bisection. Spectral analysis. Hierarchical clustering. Core-periphery structure.
Network clustering. Modularity optimization. Community detection. Blockmodeling.
Network structure. Small-world and scale-free networks. Node mixing.
Network modeling. Erdos-Renyi. Watts-Strogatz. Price, Barabasi-Albert and configuration models.
Network abstraction. Network representations. Structural network comparison. Network sampling. Network layout algorithms. Network visualization.
Network mining. Node classification and ranking. Network inference and link prediction. Machine learning with graphs.
Selected applications of network analysis. Fraud detection. Software engineering. Information science
