Artificial Intelligence

Lovro Šubelj

Content (Syllabus outline):
Introduction to network analysis. Graphs. 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. Block models.
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. Role discovery. Blockmodeling.
Network modeling. Erdos-Renyi. Watts-Strogatz. Price, Barabasi-Albert and configuration models.
Network abstraction. Structural network comparison. Network layout algorithms. Network visualization.
Network mining. Node classification and ranking by equivalence and position. Link prediction by similarity.
Selected applications of network analysis. Fraud detection. Software engineering. Information science.