End to end learning and optimization on graphs
This addresses the challenge of integrating learning and optimization in graph-based applications, such as community detection, for researchers and practitioners in machine learning and optimization, representing an incremental improvement over existing methods.
The paper tackles the problem of combining learning and optimization on graphs, where graphs are partially observed, by proposing a decision-focused learning approach that integrates a differentiable proxy for graph optimization problems as a layer in learned systems. The result shows that their ClusterNet system outperforms both pure end-to-end approaches and standard separate methods.
Real-world applications often combine learning and optimization problems on graphs. For instance, our objective may be to cluster the graph in order to detect meaningful communities (or solve other common graph optimization problems such as facility location, maxcut, and so on). However, graphs or related attributes are often only partially observed, introducing learning problems such as link prediction which must be solved prior to optimization. Standard approaches treat learning and optimization entirely separately, while recent machine learning work aims to predict the optimal solution directly from the inputs. Here, we propose an alternative decision-focused learning approach that integrates a differentiable proxy for common graph optimization problems as a layer in learned systems. The main idea is to learn a representation that maps the original optimization problem onto a simpler proxy problem that can be efficiently differentiated through. Experimental results show that our ClusterNet system outperforms both pure end-to-end approaches (that directly predict the optimal solution) and standard approaches that entirely separate learning and optimization. Code for our system is available at https://github.com/bwilder0/clusternet.