From logistics to bioinformatics or web analytics, graphs are versatile abstractions for modelling problems and solving them with specialized efficient algorithms. But with different problems comes the need for different representations. This workshop aims at giving a hands-on tour to the JuliaGraphs libraries, letting the participants build, analyze and visualize graphs for different applications. Introduction Setting up a project, logistics, where to get help, quick tour of the JuliaGraphs ecosystem First steps with SimpleGraphs Building a graph, querying basic information, modifying a graph, constructing a graph iteratively, visualization using GraphPlot, generating standard graphs, getting large graphs from the Graph500.jl and SNAPDatasets.jl datasets. More types, more fun The graph abstraction, why? Two graph variants, with SimpleWeightedGraphs & MetaGraphs. Dot format for visualization of small graphs Applications Discrete optimization (spanning trees, mincut, flows, matching), web & network analysis (pagerank, analysis of the Julia packages), spectral analysis. Target audience: we assume the audience is familiar with the basics of Julia syntax (functions, dispatch) and have already encountered graphs before.