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. Objective: 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.

Temporal graphs are commonly used to represent complex systems and track the evolution of their constituents over time. Visualizing these graphs is crucial as it allows one to quickly identify anomalies, trends, patterns, and other properties that facilitate better decision-making. In this context, selecting an appropriate temporal resolution is essential for constructing and visually analyzing the layout. The choice of resolution is particularly important, especially when dealing with temporally sparse graphs. In such cases, changing the temporal resolution by grouping events (i.e., edges) from consecutive timestamps - a technique known as timeslicing - can aid in the analysis and reveal patterns that might not be discernible otherwise. However, selecting an appropriate temporal resolution is a challenging task. In this paper, we propose ZigzagNetVis, a methodology that suggests temporal resolutions potentially relevant for analyzing a given graph, i.e., resolutions that lead to substantial topological changes in the graph structure. ZigzagNetVis achieves this by leveraging zigzag persistent homology, a well-established technique from Topological Data Analysis (TDA). To improve visual graph analysis, ZigzagNetVis incorporates the colored barcode, a novel timeline-based visualization inspired by persistence barcodes commonly used in TDA. We also contribute with a web-based system prototype that implements suggestion methodology and visualization tools. Finally, we demonstrate the usefulness and effectiveness of ZigzagNetVis through a usage scenario, a user study with 27 participants, and a detailed quantitative evaluation.

Given a patient’s series of exams conducted over time, how can we identify cases with similar abnormalities or symptoms? Hospitals and medical facilities continuously monitor patients through periodic exams, a crucial practice for assessing their current condition and potential progression, thereby supporting decision-making. However, similarity-based searches often consider several exams of a patient, most times overlooking the temporal aspect, which is crucial for patient monitoring. In this paper, we present: (1) a novel similarity search framework that identifies similar cases based on symptoms while considering the temporal evolution of the patients’ conditions; and (2) a novel similarity function, called GCWei function, which is built upon the traditional Levenshtein similarity and improves the quality of the search by penalizing the similarity between non-related sets of symptoms. To identify relations, GCWei relies on well-established graph community detection procedures using all patients’ historical data. By combining (1) and (2), we obtain a search approach called GCWei-based search, which efficiently retrieves similar cases with similar developments and thus gives the specialist a broader view of the patient’s condition based on past cases of other patients. To demonstrate the value of our approach, we evaluate it both quantitatively and qualitatively using the recent and publicly available MIMIC-IV database.

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, especially in developing countries. Electronic Health Records systems have been designed to keep the patients' history and reduce the time spent analyzing the patient's data. However, better tools to support decision-making are still needed. In this article, we propose ClinicalPath, a visualization tool for users to track a patient's clinical path through a series of tests and data, which can aid in treatments and diagnoses. Our proposal is focused on patient's data analysis, presenting the test results and clinical history longitudinally. Both the visualization design and the system functionality were developed in close collaboration with experts in the medical domain to ensure a right fit of the technical solutions and the real needs of the professionals. We validated the proposed visualization based on case studies and user assessments through tasks based on the physician's daily activities. Our results show that our proposed system improves the physicians' experience in decision-making tasks, made with more confidence and better usage of the physicians' time, allowing them to take other needed care for the patients.

Temporal (or time-evolving) networks are commonly used to model complex systems and the evolution of their components throughout time. Although these networks can be analyzed by different means, visual analytics stands out as an effective way for a pre-analysis before doing quantitative/statistical analyses to identify patterns, anomalies, and other behaviors in the data, thus leading to new insights and better decision-making. However, the large number of nodes, edges, and/or timestamps in many real-world networks may lead to polluted layouts that make the analysis inefficient or even infeasible. In this paper, we propose LargeNetVis, a web-based visual analytics system designed to assist in analyzing small and large temporal networks. It successfully achieves this goal by leveraging three taxonomies focused on network communities to guide the visual exploration process. The system is composed of four interactive visual components: the first (Taxonomy Matrix) presents a summary of the network characteristics, the second (Global View) gives an overview of the network evolution, the third (a node-link diagram) enables community- and node-level structural analysis, and the fourth (a Temporal Activity Map - TAM) shows the community- and node-level activity under a temporal perspective. We demonstrate the usefulness and effectiveness of LargeNetVis through two usage scenarios and a user study with 14 participants.

To reduce the number of pending cases and conflicting rulings in the Brazilian Judiciary, the National Congress amended the Constitution, allowing the Brazilian Supreme Court (STF) to create binding precedents (BPs), i.e., a set of understandings that both Executive and lower Judiciary branches must follow. The STF's justices frequently cite the 58 existing BPs in their decisions, and it is of primary relevance that judicial experts could identify and analyze such citations. To assist in this problem, we propose LegalVis, a web-based visual analytics system designed to support the analysis of legal documents that cite or could potentially cite a BP. We model the problem of identifying potential citations (i.e., non-explicit) as a classification problem. However, a simple score is not enough to explain the results; that is why we use an interpretability machine learning method to explain the reason behind each identified citation. For a compelling visual exploration of documents and BPs, LegalVis comprises three interactive visual components: the first presents an overview of the data showing temporal patterns, the second allows filtering and grouping relevant documents by topic, and the last one shows a document's text aiming to interpret the model's output by pointing out which paragraphs are likely to mention the BP, even if not explicitly specified. We evaluated our identification model and obtained an accuracy of 96%; we also made a quantitative and qualitative analysis of the results. The usefulness and effectiveness of LegalVis were evaluated through two usage scenarios and feedback from six domain experts.

Visual analysis of temporal networks comprises an effective way to understand the network dynamics, facilitating the identification of patterns, anomalies, and other network properties, thus resulting in fast decision making. The amount of data in real-world networks, however, may result in a layout with high visual clutter due to edge overlapping. This is particularly relevant in the so-called streaming networks, in which edges are continuously arriving (online) and in non-stationary distribution. All three network dimensions, namely node, edge, and time, can be manipulated to reduce such clutter and improve readability. This paper presents an online and nonuniform timeslicing method, thus considering the underlying network structure and addressing streaming network analyses. We conducted experiments using two real-world networks to compare our method against uniform and nonuniform timeslicing strategies. The results show that our method automatically selects timeslices that effectively reduce visual clutter in periods with bursts of events. As a consequence, decision making based on the identification of global temporal patterns becomes faster and more reliable.

The study of complex networks, especially temporal networks, increased over the last years. Understanding patterns, trends, and anomalies in these networks, as well as simulating and analyzing dynamic processes (e.g., infection spread dynamics in social networks), are not trivial tasks. Information Visualization techniques offer significant potential to assist the user in these analyses. This paper presents an extended version of Dynamic Network Visualization (DyNetVis), a freely available and open-source interactive software to perform visual analysis of temporal networks. It provides four visualization techniques, structural, temporal, matrix, and community layouts, and a number of state-of-the-art methods to interact with each of these layouts. DyNetVis also implements dynamic processes, including standard epidemic models. It is a computational tool to study and explore networks in diverse domains.