On partially labeled data, semi-supervised learning methods have been studied profoundly by expressing the relations between the data entities within weighted graph representations . The inpainting task, on the other hand, is usually defined on the domains accompanying a signal content, which has been addressed with graph signal representations and operations . Most of these studies focus on one type of relationship between the pair of data points during the construction of the graph structure. However, the connections between the entities may possess different types of relationships, which can be represented better by multiple graph structures. The objective of this project is to extend semi-supervised clustering and inpainting tasks on multi-layer graph settings, where each graph layer signifies a particular type of relation between vertices. This yields the same number of vertices in each graph layer, yet the topology (i.e., weight matrix) is different due to the difference between the focus of each layer.
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Requirements: Python, basics of Graph signal processing: graphs signal filtering and spectral clustering (covered in EE-558 Network Tour of Data Science).