Finite population sampling has found numerous applications in the past century. The validity of sampling inference of real populations derives from the known probability sampling design under which the sample is selected, “irrespectively of the unknown properties of the target population studied” (Neyman, 1934). This is the key theoretical justification for its universal applicability. Valued graph is a more powerful representation, which allows one to incorporate the connections among the population units in addition to the units on their own. The underlying structure is a graph given as a finite collection of nodes (units) and edges (connections). Attaching measures to the nodes and/or edges yields a valued graph. Many technological, socio-economic, biological phenomena exhibit a graph structure that may be the central interest of study, or the edges may provide effectively access to those nodes that are the primary targets. Either way, graph sampling is a statistical approach to study real graphs. Just like finite population sampling, it is universally applicable based on exploring the variation over all possible subgraphs (i.e. sample graphs), which can be taken from the given population graph, according to a specified method of sampling. Graph sampling thus encompasses finite population sampling, because any latter situation can be represented as a special case of the former. All the so-called “unconventional” finite population sampling techniques, such as indirect, network, adaptive cluster or line-intercept sampling, can be more effectively studied as special cases of graph sampling. Whereas snowball sampling and targeted random walk sampling are probabilistic versions of breadth- or depth-first non-exhaustive search algorithms in graphs. The course provides an introduction to the central concepts of graph sampling, the most common sampling methods, and the construction of graph sampling strategy. An emphasis is the extension from the traditional sampling strategy (finite population sampling, Horvitz-Thompson estimator) to a much more general strategy consisting of bipartite incidence graph sampling (BIGS) and incidence weighting estimator (IWE). The application of the BIGS-IWE strategy will be illustrated for all the aforementioned unconventional situations of finite population sampling, as well as the more complicated graph sampling situations such as snowball sampling and targeted random walk sampling.