Graph Neural Networks (GNNs) have demonstrated remarkable results in various real-world applications, including drug discovery, object detection, social media analysis, recommender systems, and text classification. In contrast to their vast potential, training them on large-scale graphs presents significant computational challenges due to the resources required for their storage and processing. Graph Condensation has emerged as a promising solution to reduce these demands by learning a synthetic compact graph that preserves the essential information of the original one while maintaining the GNN's predictive performance. Despite their efficacy, current graph condensation approaches frequently rely on a computationally intensive bi-level optimization. Moreover, they fail to maintain a mapping between synthetic and original nodes, limiting the interpretability of the model's decisions. In this sense, a wide range of decomposition techniques have been applied to learn linear or multi-linear functions from graph data, offering a more transparent and less resource-intensive alternative. However, their applicability to graph condensation remains unexplored. This paper addresses this gap and proposes a novel method called Multi-view Graph Condensation via Tensor Decomposition (GCTD) to investigate to what extent such techniques can synthesize an informative smaller graph and achieve comparable downstream task performance. Extensive experiments on six real-world datasets demonstrate that GCTD effectively reduces graph size while preserving GNN performance, achieving up to a 4.0% improvement in accuracy on three out of six datasets and competitive performance on large graphs compared to existing approaches. Our code is available at https://github.com/nicolasrsantos/gctd.

Graph Neural Networks (GNNs) have recently received extensive attention due to their applicability in a wide range of tasks, including drug discovery, text classification, traffic forecasting, hardware design, and recommendation. However, GNNs face significant challenges regarding scalability and the ability to handle large-scale graphs. Several strategies have been proposed to address these challenges, with multilevel optimization being a prominent approach. This technique involves hierarchically generating compact graphs through a coarsening step, applying a target algorithm (e.g., community detection) to the coarsest graph, and then projecting the initial solution back to the original input to derive the final solution. In this work, we introduce a method for graph-based text classification using GNNs. Our approach involves generating ten smaller graphs from an input bipartite graph using the coarsening step within the multilevel optimization and applying a GNN to learn node representations at various levels of granularity. Moreover, we propose a novel semi-supervised coarsening algorithm called Greedy Sorted Matching using Class and Split Information for Bipartite Graphs (GMCb). GMCb leverages class and train-test split information to select document nodes to merge during the graph coarsening step. We perform three types of reductions by either coarsening only one of the partitions of the graph or both simultaneously. Our method is evaluated on eight diverse datasets using three different GNN architectures. We assess each model's performance, memory usage, and training time to understand the impacts of graph reduction. Our experiments demonstrate that contracting the document nodes can improve performance while reducing memory consumption and training time.