Kleyton da Costa

Kleyton da Costa, Bernardo Modenesi, Ivan F. M. Menezes et al.

Particle Swarm Optimization (PSO) is susceptible to premature convergence when the swarm collapses around the global best, particularly on multimodal landscapes in higher dimensions. We propose Divergence-guided PSO (DPSO), which augments the velocity update with a modulation term that repels particles whose personal bests have converged near the global best. The repulsion is gated by a Gaussian similarity kernel, which we prove is equivalent to an exponentially decaying function of the KL divergence between Gaussian-embedded personal and global bests, connecting the mechanism to the family of $f$-divergences and providing a principled basis for kernel design. Experiments on 36 benchmark functions (15 unimodal, 21 multimodal) across dimensions $D \in \{10, 30, 50\}$, each with 30 independent runs, show that DPSO frequently outperforms standard PSO on multimodal problems, with improvements of 2-8$\times$ on functions such as Pinter, Ackley, and Levy, and up to 5$\times$ reduction in run-to-run variance. On unimodal landscapes the modulation term is counterproductive, confirming that DPSO targets the exploration-exploitation trade-off rather than offering a universal improvement. The method adds one hyperparameter, incurs 15--25\% wall-clock overhead, and does not increase the asymptotic per-iteration complexity of PSO. The project code is available here: https://github.com/Kleyt0n/dpso

Cristian Munoz, Kleyton da Costa, Bernardo Modenesi et al.

The rapid integration of artificial intelligence (AI) into various industries has introduced new challenges in governance and regulation, particularly regarding the understanding of complex AI systems. A critical demand from decision-makers is the ability to explain the results of machine learning models, which is essential for fostering trust and ensuring ethical AI practices. In this paper, we develop six distinct model-agnostic metrics designed to quantify the extent to which model predictions can be explained. These metrics measure different aspects of model explainability, ranging from local importance, global importance, and surrogate predictions, allowing for a comprehensive evaluation of how models generate their outputs. Furthermore, by computing our metrics, we can rank models in terms of explainability criteria such as importance concentration and consistency, prediction fluctuation, and surrogate fidelity and stability, offering a valuable tool for selecting models based not only on accuracy but also on transparency. We demonstrate the practical utility of these metrics on classification and regression tasks, and integrate these metrics into an existing Python package for public use.

Kleyton da Costa

Anomaly detection is a challenging task, particularly in systems with many variables. Anomalies are outliers that statistically differ from the analyzed data and can arise from rare events, malfunctions, or system misuse. This study investigated the ability to detect anomalies in global financial markets through Graph Neural Networks (GNN) considering an uncertainty scenario measured by a nonextensive entropy. The main findings show that the complex structure of highly correlated assets decreases in a crisis, and the number of anomalies is statistically different for nonextensive entropy parameters considering before, during, and after crisis.

Kleyton da Costa, Bernardo Modenesi

Graph Neural Networks (GNNs) benchmarks often report single point estimates, even when performance differences are small relative to variation across random seeds, train/test splits, and datasets. Confidence intervals, paired comparisons, multiple-comparison correction, and rank-based aggregation are standard statistical tools, but they are rarely the default output of graph-learning benchmark suites. We introduce GraphNetz, a benchmarking framework whose default output is a structured statistical report rather than a raw accuracy table. GraphNetz currently includes 63 dataset loaders, four task types, and five canonical GNN architectures, while also supporting custom datasets and models. The framework standardizes multi-seed evaluation and automatically returns per-cell confidence intervals, Holm-corrected paired tests, and Friedman-Nemenyi critical-difference diagrams across tasks. In a cross-category benchmark over ten heterogeneous tasks, apparent rank differences among four canonical node-level encoders fall within a single Nemenyi clique, indicating that none is significantly better than the others at $α= 0.05$. GraphNetz therefore provides researchers with a reproducible computational and statistical pipeline to benchmark new graph-learning methods against standard architectures, over different tasks and a wide set of applications, while reporting principled statistical evidence for benchmarking which accounts for seed uncertainty. This framework is set to serve the graph-learning community with a reproducible and honest model comparison ready to be added to papers.