Introdução: social networks, communication networks, the web, the internet, biological networks • conceitos-chave da teoria dos grafos: networks and graphs, degree, average degree and degree distribution, adjacency matrix, weighted networks, bipartite networks, paths and distances, connectedness, clustering coefficient • random networks: the Erdos-Renyi model, number of links, degree distribution of random networks, small worlds, clustering coefficient in random networks • a scale free property: power laws and scale-free networks, hubs, the meaning of scale-free, the role of the degree exponent, generating networks with arbitrary degree distribution • o Barabasi-Albert model: growth and preferential attachment, degree dynamics, degree distribution, measuring preferential attachment, non-linear preferential attachment, the origins of preferential attachment, diameter and clustering coefficient • degree correlations: assortativity and disassortativity, measuring degree correlations, structural cutoffs, correlations in real networks, the impact of degree correlations • communities: basics of communities, hierarchical clustering, modularity, overlapping communities, characterizing communities, testing communities • (se o tempo permitir) Graph representations learning: node embeddings, Graph Neural Networks, Deep Generative Models for networks.