(Complex Networks Analysis)
- Introduction to the course, preliminaries,
introducing complex networks: examples and applications
- Random graph model (theoretical and empirical analysis of degree distribution, giant component, clustering coefficient, diameter, ...)
- Small-world phenomena(models, theoretical and empirical analysis)
- Information cascades in complex networks, influence maximization in complex networks, submodular optimization
- Outbreak optimization in complex networks
- Network formation processes - power-law degree distribution - preferential attachment
- Link analysis: HITTS, PageRank and random walk algorithms
- Strengths of weak ties and community structure in complex networks, Girvan-Newman for detecting communities and clusters in complex networks and graph partitioning.
- Spectral algorithms for clustering in complex networks
- Analysis of overlapping clusters/communities in complex networks
- Link prediction in complex networks (leaning-based approaches, index-based approaches, ...)
- Contraction in complex networks, deconvolution in complex networks
- Learning representations in complex networks, random walk-based approaches, node2vec algorithm
- Graph neural networks - Graph convolutional networks - Graph attention networks
- Centrality in complex networks (betweenness, eigenvector, closeness, Katz, .…)