Piotr Bródka

Nicola Garau, Niccolò Bisagno, Piotr Bródka et al.

Human Pose Estimation (HPE) aims at retrieving the 3D position of human joints from images or videos. We show that current 3D HPE methods suffer a lack of viewpoint equivariance, namely they tend to fail or perform poorly when dealing with viewpoints unseen at training time. Deep learning methods often rely on either scale-invariant, translation-invariant, or rotation-invariant operations, such as max-pooling. However, the adoption of such procedures does not necessarily improve viewpoint generalization, rather leading to more data-dependent methods. To tackle this issue, we propose a novel capsule autoencoder network with fast Variational Bayes capsule routing, named DECA. By modeling each joint as a capsule entity, combined with the routing algorithm, our approach can preserve the joints' hierarchical and geometrical structure in the feature space, independently from the viewpoint. By achieving viewpoint equivariance, we drastically reduce the network data dependency at training time, resulting in an improved ability to generalize for unseen viewpoints. In the experimental validation, we outperform other methods on depth images from both seen and unseen viewpoints, both top-view, and front-view. In the RGB domain, the same network gives state-of-the-art results on the challenging viewpoint transfer task, also establishing a new framework for top-view HPE. The code can be found at https://github.com/mmlab-cv/DECA.

Piotr Bródka, Michał Czuba, Bogumił Kamiński et al.

The increasing availability of relational data has contributed to a growing reliance on network-based representations of complex systems. Over time, these models have evolved to capture more nuanced properties, such as the heterogeneity of relationships, leading to the concept of multilayer networks. However, the analysis and evaluation of methods for these structures is often hindered by the limited availability of large-scale empirical data. As a result, graph generators are commonly used as a workaround, albeit at the cost of introducing systematic biases. In this paper, we address the inverse-generator problem by inferring the configuration parameters of a multilayer network generator, mABCD, from a real-world system. Our goal is to identify parameter settings that enable the generator to produce synthetic networks that act as digital twins of the original structure. We propose a method for estimating matching configurations and for quantifying the associated error. Our results demonstrate that this task is non-trivial, as strong interdependencies between configuration parameters weaken independent estimation and instead favour a joint-prediction approach.

Mateusz Stolarski, Adam Piróg, Piotr Bródka

The identification of key nodes in complex networks is an important topic in many network science areas. It is vital to a variety of real-world applications, including viral marketing, epidemic spreading and influence maximization. In recent years, machine learning algorithms have proven to outperform the conventional, centrality-based methods in accuracy and consistency, but this approach still requires further refinement. What information about the influencers can be extracted from the network? How can we precisely obtain the labels required for training? Can these models generalize well? In this paper, we answer these questions by presenting an enhanced machine learning-based framework for the influence spread problem. We focus on identifying key nodes for the Independent Cascade model, which is a popular reference method. Our main contribution is an improved process of obtaining the labels required for training by introducing 'Smart Bins' and proving their advantage over known methods. Next, we show that our methodology allows ML models to not only predict the influence of a given node, but to also determine other characteristics of the spreading process-which is another novelty to the relevant literature. Finally, we extensively test our framework and its ability to generalize beyond complex networks of different types and sizes, gaining important insight into the properties of these methods.

Karol Dykiert, Mateusz Stolarski, Michał Czuba et al.

Accurate pipe roughness estimation in large-scale water distribution networks is often hindered by the high cost of traditional field methods. This study investigates whether network partitioning, by utilizing hydraulic and graph-derived attributes, can enhance the calibration of these parameters. Using a high-fidelity model of a real network as a benchmark, we evaluate density-based clustering, and topology-driven grouping strategies. Optimization experiments demonstrate that attribute-based grouping yields stable, repeatable results comparable to manual calibration for hydraulically significant pipes. While hydraulic attributes generate more distinct cluster structures, the inclusion of graph-based data improves calibration robustness by stabilizing the optimization process. Notably, density-based clustering achieves similar accuracy to k-means while reducing computational effort in specific configurations. Although the method does not eliminate all sources of uncertainty, results suggest that topology-informed grouping provides a systematic, reproducible, and computationally efficient alternative to manual heuristics, highlighting the critical role of network structure in reliable parameter estimation.

Łukasz Kraiński, Michał Czuba, Piotr Bródka et al.

One of the most persistent challenges in network science is the development of various synthetic graph models to support subsequent analyses. Among the most notable frameworks addressing this issue is the Artificial Benchmark for Community Detection (ABCD) model, a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of ABCD and introduce its variant, mABCD, thereby addressing the gap in models capable of generating multilayer networks. The uniqueness of the proposed approach lies in its flexibility at both levels of modelling: the internal structure of individual layers and the inter-layer dependencies, which together make the network a coherent structure rather than a collection of loosely coupled graphs. In addition to the conceptual description of the framework, we provide a comprehensive analysis of its efficient Julia implementation. Finally, we illustrate the applicability of mABCD to one of the most prominent problems in the area of complex systems: spreading phenomena analysis.

Michał Czuba, Mateusz Stolarski, Adam Piróg et al.

Identifying super-spreaders can be framed as a subtask of the influence maximisation problem. It seeks to pinpoint agents within a network that, if selected as single diffusion seeds, disseminate information most effectively. Multilayer networks, a specific class of heterogeneous graphs, can capture diverse types of interactions (e.g., physical-virtual or professional-social), and thus offer a more accurate representation of complex relational structures. In this work, we introduce a novel approach to identifying super-spreaders in such networks by leveraging graph neural networks. To this end, we construct a dataset by simulating information diffusion across hundreds of networks - to the best of our knowledge, the first of its kind tailored specifically to multilayer networks. We further formulate the task as a variation of the ranking prediction problem based on a four-dimensional vector that quantifies each agent's spreading potential: (i) the number of activations; (ii) the duration of the diffusion process; (iii) the peak number of activations; and (iv) the simulation step at which this peak occurs. Our model, TopSpreadersNetwork, comprises a relationship-agnostic encoder and a custom aggregation layer. This design enables generalisation to previously unseen data and adapts to varying graph sizes. In an extensive evaluation, we compare our model against classic centrality-based heuristics and competitive deep learning methods. The results, obtained across a broad spectrum of real-world and synthetic multilayer networks, demonstrate that TopSpreadersNetwork achieves superior performance in identifying high-impact nodes, while also offering improved interpretability through its structured output.

Marcin Lewicki, Tomasz Kajdanowicz, Piotr Bródka et al.

The main purpose of this article was to create a model and simulate the profitability conditions of an interactive presentation system (IPS) with the recommender system (RS) used in the kiosk. 90 million simulations have been run in Python with SymPy to address the problem of discount recommendation offered to the clients according to their usage of the IPS.

Jarosław Jankowski, Stanisław Saganowski, Piotr Bródka

Today, in the digital age, the mobile devices are more and more used to aid people in the struggle to improve or maintain their health. In this paper, the mobile eHealth solution for remote patient monitoring during clinical trials is presented, together with the outcomes of quantitative and qualitative performance evaluation. The evaluation is a third step to improve the quality of the application after earlier Good Clinical Practice certification and validation with the participation of 10 patients and three general practitioners. This time, the focus was on the usability which was evaluated by the seventeen participants divided into three age groups (18-28, 29-50, and 50+). The results, from recorded sessions and the eye tracking, show that there is no difference in performance between the first group and the second group, while for the third group the performance was worse, however, it was still good enough to complete task within reasonable time.