Context: Complex networks represent systems with non-trivial connections and are widely used in fields such as social media, biology, and transportation. Temporal networks extend this by capturing the evolution of connections over time, providing insights into event sequences and information diffusion. Analyzing these networks requires specialized tools, and Python offers a variety of libraries tailored for this purpose.

Objectives: This study evaluates Python libraries designed for temporal network analysis based on multiple criteria. The aim is to assess the strengths and limitations of these tools, guide users in selecting appropriate libraries, and identify gaps for future development.

Methods: A comparative analysis was conducted on selected Python libraries using predefined evaluation criteria. The assessment considered factors such as available documentation, supported metrics, visualization capabilities, supported format, uniqueness, community support, and popularity. Data were gathered from official documentation, community forums, scientific papers, and usage statistics.

Results: Findings indicate that the TGX, Teneto, and PathpyG stand out, excelling in three of five criteria. Networkx-t shows balanced performance with no significant drawbacks, making it a reliable general-purpose choice. However, several tools have limitations in specific areas, such as a lack of comprehensive documentation or advanced visualization features.

Conclusion: This review provides an overview of existing Python tools for temporal network analysis, offering insights into their capabilities and shortcomings. The results assist researchers and practitioners in selecting suitable libraries while highlighting areas for improvement and potential future developments in the field.

Analyzing unlabeled trajectory data remains challenging due to high dimensionality, complex interactions between movement variables, and the lack of semantic annotations. In this paper, we present TaxVA, an interactive visual analytics system for exploring and interpreting spatio-temporal trajectories through a taxonomy-driven workflow. TaxVA organizes movement variables into semantically meaningful categories and integrates taxonomy-guided outlier analysis, zone-based behavioral partitioning, and feature-importance modeling within a coordinated visual interface. The system enables users to iteratively explore movement data by selecting analytical dimensions, inspecting behavioral zones, comparing representative trajectories, and linking statistical differences to spatial patterns. Through an interactive demonstration on real-world trajectory data, we show how TaxVA supports multi-perspective analysis, allowing the same trajectories to be interpreted in terms of both kinematic properties (e.g., speed variability) and geometric characteristics (e.g., turning behavior). The demo highlights how the integration of feature importance, temporal heatmaps, and spatial visualization facilitates intuitive and interpretable exploration of complex mobility data without requiring predefined labels.

Forestry presents complex, irregular environments that are underrepresented in the visualization literature, despite the increasing availability of large-scale LiDAR and multispectral datasets. Extracting meaningful information from this data requires analyzing subtle structural and physiological variations among individual trees, a task that remains challenging due to the irregular, multi-layered nature of forests. In this paper, we present a visualization-driven approach for the interactive exploration and analysis of 3D forestry point clouds at the tree level, enriched with Normalized Difference Vegetation Index (NDVI) metrics. Our web-based system, entitled ForestVis, enables users to navigate dense forest environments, query individual tree attributes, and analyze vegetation metrics within their spatial context. The system design is informed by a semi-structured interview with forestry domain experts, from which we derive a set of analytical tasks emphasizing tree-centric abstractions, interactive exploration, and scalability to large datasets. Through illustrative use cases, we show how the system supports applications such as precision forestry, biodiversity monitoring, and detection of tree stress. More broadly, this work highlights the potential of interactive visualization providing novel tools and frameworks for ecological research and sustainable forest management. We evaluate ForestVis through two expert studies: a qualitative study of usability and analytical reasoning, and a quantitative study reporting objective task measures together with benchmarks of rendering, streaming, and interaction performance across two hardware configurations. Our evaluation suggested that our tool is effective in helping domain experts explore specific forest tasks, in large forest point cloud datasets, while maintaining computational performance. Our findings suggest that different point cloud rendering styles are suited to different tasks, whether analyzing individual trees or groups of trees, and interactive filtering helped experts relate vegetation indices to the point-cloud instance segmentation and to tree height.

The need for large, high-quality annotated datasets continues to represent a primary limitation in training Object Detection (OD) models. To mitigate this challenge, we present VILOD, a Visual Interactive Labeling tool that integrates Active Learning (AL) with a suite of interactive visualizations to create an effective Human-in-the-Loop (HITL) workflow for OD annotation and training. VILOD is designed to make the AL process more transparent and steerable, empowering expert users to implement diverse, strategically guided labeling strategies that extend beyond algorithmic query strategies. Through comparative case studies, we evaluate three visually guided labeling strategies against a conventional automated AL baseline. The results show that a balanced, human-guided strategy—leveraging VILOD's visual cues to synthesize information about data structure and model uncertainty—not only outperforms the automated baseline but also achieves the highest overall model performance. These findings emphasize the potential of visually guided, interactive annotation to enhance both the efficiency and effectiveness of dataset creation for OD.

Analyzing Multivariate Networks (MVN) requires considering both graph structure and node attributes. However, most dimensionality reduction techniques operate on a single data modality, limiting their ability to comprehensively represent such networks. In this paper, we propose two workflows for MVN visualization that integrate structural and attribute information into a unified embedding process. The first workflow linearly combines structure- and attribute-based distances, while the second employs Graph Neural Network embeddings as an intermediate representation before projecting the data onto a 2D embedding. To evaluate neighborhood preservation across both data modalities, we introduce Harmonic Trustworthiness, a metric that balances structural and attribute-based preservation. Results on real-world datasets indicate improved representation and neighborhood preservation.

Movement data is prevalent across various applications and scientific fields, often characterized by its massive scale and complexity. Exploratory Data Analysis (EDA) plays a crucial role in summarizing and describing such data, enabling researchers to generate insights and support scientific hypotheses. Despite its importance, traditional EDA practices face limitations when applied to high-dimensional, unlabeled movement data. The complexity and multi-faceted nature of this type of data require more advanced methods that go beyond the capabilities of current EDA techniques. This study addresses the gap in current EDA practices by proposing a novel approach that leverages movement variable taxonomies and outlier detection. We hypothesize that organizing movement features into a taxonomy, and applying anomaly detection to combinations of taxonomic nodes, can reveal meaningful patterns and lead to more interpretable descriptions of the data. To test this hypothesis, we introduce TUMD, a new method that integrates movement taxonomies with outlier detection to enhance data analysis and interpretation. TUMD was evaluated across four diverse datasets of moving objects using fixed parameter values. Its effectiveness was assessed through two passes: the first pass categorized the majority of movement patterns as Kinematic, Geometric, or Hybrid for all datasets, while the second pass refined these behaviors into more specific categories such as Speed, Acceleration, or Indentation. TUMD met the effectiveness criteria in three datasets, demonstrating its ability to describe and refine movement behaviors. The results confirmed our hypothesis, showing that the combination of movement taxonomies and anomaly detection successfully uncovers meaningful and interpretable patterns within high-dimensional, unlabeled movement data.

This paper aims to analyze the Ph.D. thesis process and outcomes through the perspectives of three key actors—supervisee, supervisor, and academic institution —by drawing upon the personal experiences ofthe authors. Reflections are based on the authors past experience as supervisees and firsthand involvement in supervision, program management, and committee participation, with the key findings highlighting the intricate dynamics of these roles in shaping Ph.D. education. The aspects discussed in this work are motivation, previous experience, the individual study plan, guidance, and contributions. The motivations for pursuing a Ph.D. were diverse, encompassing personal and professional goals, passion for research, career advancement, and societal contributions. Previous experience from all actors is recognized as a critical factor influencing the success of the Ph.D. journey, where considerations regarding academic background, research interests, and cultural factors may influence the time and the outcome. The individual study plan is recognized by the authors as a vital tool for modeling a Ph.D.student’s research and professional development trajectory. Guidance throughout the Ph.D. process is discussed in terms of frequent and consistent supervision, providing regular support and feedback, which gradually shifts towards fostering research independence. This approach emphasizes the importance of mentorship and effective communication among peers at every stage. Finally, contributions are discussed as a final phase of the Ph.D. journey, where students are expected to demonstrate their expertise and impact through publications, awards, patents, and other forms of recognition. Supervisors and institutions also play a crucial role in supporting and showcasing such contributions. This reflection paper shows the relations between these factors and the collective responsibility shared by the actors in producing a successful Ph.D. thesis and an independent researcher. We argue our reflections are avaluable resource for those involved in Ph.D. programs, offering insights into the various dimensions ofthe Ph.D. journey and highlighting the importance of collaboration and support among these critical actors.

RoboCup Soccer Simulation 2D (SS2D) research is hampered by the complexity of existing C++-based codes like Helios, Cyrus, and Gliders, which also suffer from limited integration with modern machine learning frameworks. This development paper introduces a transformative solution: a gRPC-based, language-agnostic framework that seamlessly integrates with the high-performance Helios base code. This approach not only facilitates the use of diverse programming languages including C#, JavaScript, and Python—but also maintains the computational efficiency critical for real-time decision-making in SS2D. By breaking down language barriers, our framework significantly enhances collaborative potential and flexibility, empowering researchers to innovate without the overhead of mastering or developing extensive base codes. We invite the global research community to leverage and contribute to the Cross Language Soccer (CLS) framework, which is openly available under the MIT License, to drive forward the capabilities of multi-agent systems in soccer simulations (https://github.com/CLSFramework,https://github.com/CLSFramework/cross-language-soccer-framework/wiki).

This study presents an interactive visualization tool that facilitates fairness-aware urban planning. The system introduces afairness scale to assess the accessibility of potential new developments, using color-coded scatter plots to visualize disparities.An intuitive interaction design minimizes complexity while enhancing usability, enabling users to analyze urban infrastructureand services. Developed with web technologies, the tool leverages OpenStreetMap data to ensure adaptability across differentcities. Future optimizations include advanced analytical capabilities and broader dataset integrations to improve decision-making in urban development.

Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the common characteristics shared by important ports by analyzing the network of connections formed by vessel movement among them. To accomplish this task, we adopt a bottom-up network construction approach that combines three years’ worth of AIS (Automatic Identification System) data from around the world, constructing a Ports Network that represents the connections between different ports. Through this representation, we utilize machine learning to assess the relative significance of various port features. Our model examined such features and revealed that geographical characteristics and the port’s depth are indicators of a port’s importance to the Ports Network. Accordingly, this study employs a data-driven approach and utilizes machine learning to provide a comprehensive understanding of the factors contributing to the extent of ports. Our work aims to inform decision-making processes related to port development, resource allocation, and infrastructure planning within the industry.