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.

Differential Item Functioning (DIF) is a powerful tool for developing fairer tests and mitigating bias in applicant selection tests. DIF aims to detect items in a test that favor or harm groups of people based on aspects such as gender, age, and race, which should be irrelevant to the assessment. Likewise, in machine learning, selecting a model from a pool of candidates is essential to identify the one that minimizes or eliminates discriminatory effects in its decision-making process. As far as we know, research into knowledge discovery through supervised machine learning has predominantly focused on including fair-ness notions at the lowest level of the pre-processing, pattern extraction, and post-processing phases. This fact evidences a need for studies on the impact of model selection on the development of impartial models. Herein, we present a novel approach to fairness-aware model selection to fill the mentioned gap. Our proposal introduces ABC, the first group fairness metric based on DIF concepts. We experimentally evaluated our approach against two model selection strategies by employing ten datasets, six classification algorithms, one performance measure, four group fairness measures, and one statistical significance test. According to the results, our proposal stands out for achieving a trade-off between improving the sense of justice and good classifier performance. Consequently, ABC is a promising metric for selecting fairer models with high predictive power.

The quality of a location-based social network (LBSN) is mainly related to the granularity of information on the users' location. When LBSN is built using stay points, it presents much more information since GPS logs convey more users' mobility information. However, the main challenge in building LBSN using stay points is to define local-vertices. This problem is known as local-entity resolution. This local-vertices could represent venues with semantic information like parks, restaurants, among others. The most common way to resolve local-entity is by applying clustering algorithms to group nearby stay points into local-vertices. However, in this case, only geographic information is used, which makes it very difficult to separate geographically close venues into distinct local-vertices. This paper addresses this gap and presents a novel approach that uses the coarsening stage of a multilevel optimization scheme to build LBSNs by using stay points. The experimental evaluation carried out indicates that our approach has advantages compared to usual clustering methods to represent real-world features.