Research in diverse fields, from social networks to bioinformatics, demonstrates that graph mining is a powerful approach to derive new knowledge from large networks. The extraction of interesting subgraph structures or graph patterns is a hot topic in data mining and bioinformatics. However, the exploration of local graph patterns that change over time has been a largely unexplored area in the graph mining field. The study of techniques to extract evolving graph patterns from dynamic networks is the scope of this proposal. In extension, we will study how rules can be retrieved that describe how certain events, such as graph perturbations, lead to graph pattern changes. Both challenges are studied from the perspective of general graph patterns, but also towards the more specific concept of network path multiplicity, which entails the fact that multiple paths exist to connect a given pair of nodes. The goals of this project are first addressed from a theoretical perspective. Furthermore, techniques are studied using both synthetic and real experimental data. The concept of evolving graph patterns is relevant for a large series of application domains. However, we will particularly validate our approaches with bioinformatics applications, for which the extraction of this new pattern type is highly interesting. Furthermore, large and rich biomolecular datasets are available, and extensive domain knowledge that enables pattern interpretation is present within the applying groups