Laplacian-based semi-Supervised learning in multilayer hypergraphs by coordinate descent

• الحاوية / القاعدة: EURO Journal on Computational Optimization
• المؤلفون الرئيسيون: Sara Venturini, Andrea Cristofari, Francesco Rinaldi, Francesco Tudisco
• التنسيق: مقال
• اللغة: الإنجليزية
• منشور في: Elsevier 2023-01-01
• الموضوعات: Semi-supervised learning; Coordinate methods; Multilayer hypergraphs
• الوصول للمادة أونلاين: http://www.sciencedirect.com/science/article/pii/S2192440623000230
• تدمد: 2192-4406

Graph Semi-Supervised learning is an important data analysis tool, where given a graph and a set of labeled nodes, the aim is to infer the labels to the remaining unlabeled nodes. In this paper, we start by considering an optimization-based formulation of the problem for an undirected graph, and then we extend this formulation to multilayer hypergraphs. We solve the problem using different coordinate descent approaches and compare the results with the ones obtained by the classic gradient descent method. Experiments on synthetic and real-world datasets show the potential of using coordinate descent methods with suitable selection rules.