Advanced Topics in Network Science 2017/18

Course overview and logistics

Course overview, logistics and syllabus. Coursework description, instructions and other details.

From graph theory to network science

From classical graph theory to social network analysis and modern network science. Graphology and networkology.

Large-scale structure and models

Random graphs and real networks. Degrees of separation in small-world networks, power-law distributions of scale-free networks and mixing in networks.

Mesoscopic structure and fragments

Network community and core-periphery structure. Graph partitioning, blockmodeling and community detection. Network motifs, graphlets and node orbits.

Node position and similarity

Measures of node position and centrality, measures of link importance and bridging, and link analysis algorithms. Node similarity and equivalence.

Network formation and evolution

Generative models of network evolution and link copying models. Network optimization models and random geometric graphs.

Network inference and prediction

Network inference and link prediction methods. Network-based clustering, classification and regression. Network influence maximization and outbreak detection.

Network sampling and comparison

Fractal networks and self-similarity, network sampling, backbones and skeletons. Network comparison by fragments and statistical comparison of network metrics.

Network dynamics and processes

Decentralized search and network navigation. Percolation theory and network robustness. Spreading and diffusion on networks. Game theory and networks.

Alternative types of networks

Attributed, valued and signed networks. Multi-relational, multilayer and higher-order networks. Temporal and spatial networks.

Empirical analysis of networks

Network representations, data structures, fundamental algorithms, programming libraries and software. Node layout and network visualization.

Applications of network analysis

Detecting automobile insurance fraudsters. Mining software dependency networks. Comparing bibliographic databases, clustering and modeling scientific publications, and analyzing scientific coauthorships.

Network analysis challenges

Tentative list of short weekly challenges on network concepts and techniques.

\(^‡\)Video lecture    \(^§\)Tentative talk