Networks occur in many as fields of science and every-day life. Oftentimes they are used as a tool to model social, physical or biological interactions or structures. Some more concrete example are computer and sensor networks; social networks such as Facebook or LinkedIn; biological networks such as herd interactions or the workings of various cell-systems; and physical networks, such as self-assembling nano-particles or mechanical structures. Though the domains for these networks are completely differ-ent, their underlying structure is the same. That is why they are processed, stored, visualized and analyzed in a similar way. When these networks are small they can be analyzed easily by domain experts inspecting various repre-sentations of the networks. However, in many cases the networks are much too large to create a clear representation of the whole network. As such, it becomes very hard to obtain useful information or oth-erwise work with the network. To alleviate this problem powerful algorithms and tools are needed that can process larger networks. This is where computer science comes into play, by first designing good measures and structures that help to define and find various useful properties of the networks, and then providing algorithms and data structures to compute these. The goal of the seminar will be to develop a basis for such algorithms and data structures for kinetic geometric networks.