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Circular RNAs (circRNAs) play key roles in colorectal cancer (CRC) progression, but little is known about the biological functions of hsa_circRNA_001676 in CRC. Therefore, we explored the potential role of hsa_circRNA_001676 in CRC development. RT-qPCR was performed to determine hsa_circRNA_001676, miR-556-3p and Ras-GTPase-activating SH3 domain-binding-proteins 2 (G3BP2) levels in CRC tissues. Meanwhile, to evaluate the roles of hsa_circRNA_001676, miR-556-3p and G3BP2 on CRC, functional analysis of cell proliferation, migration and stemness were then performed. Our results showed that compared to normal tissues, hsa_circRNA_001676 and G3BP2 level was elevated, but miR-556-3p level was reduced in CRC tissues. Additionally, luciferase reporter results showed that hsa_circRNA_001676 was shown to target miR-556-3p, and G3BP2 was targeted by miR-556-3p. Hsa_circRNA_001676 or G3BP2 overexpression promoted CRC cell proliferation and migration. Conversely, miR-556-3p overexpression suppressed CRC cell proliferation and migration. Moreover, deficiency of hsa_circRNA_001676 or G3BP2 repressed the CRC cell proliferation, migration and stemness. Meanwhile, hsa_circRNA_001676 deficiency obviously reduced tumor growth and stemness in a CRC mouse xenograft model. Furthermore, hsa_circRNA_001676 deficiency notably reduced G3BP2 level, but elevated miR-556-3p level in tumor tissues from tumor-bearing mice. Mechanistically, hsa_circRNA_001676 targeted miR-556-3p to increase G3BP2 level, contributing to the progression of CRC. Collectively, hsa_circRNA_001676 was able to accelerate proliferation, migration and stemness in CRC through regulating miR-556-3p/G3BP2 axis, suggesting that hsa_circRNA_001676 may become a potential therapeutic target in treating CRC. [ABSTRACT FROM AUTHOR]

The articles in this Special Issue focus on the application of deep learning techniques in visualization. The great success of deep learning techniques in computer vision, natural language processing, and speech recognition offers new opportunities for data visualization and analytics. We can leverage these technologies not only to recognize visual representations but also to understand analytical tasks. However, introducing deep learning techniques into visualization tasks faces new challenges and problems to solve. In this issue, we present six articles that illustrate how deep learning techniques interact with visualization. [ABSTRACT FROM AUTHOR]

Planning an ideal tour schedule is a tedious process, where the attractions to visit and the order of visits need to be carefully determined. In this paper, we propose a novel interactive approach for tour planning. We first extract prior tourists' experiences from the crowdsourcing tour data on the Web using frequent substring mining. We then design and implement a planning tool equipped with interactive visualizations, enabling users to learn the extracted experiences and plan their own tours. Our approach is evaluated with two usage scenarios on real-world tour data in two cities. Compared with previous methods, our approach strikes a balance between efficiency and reliability. On the one hand, we support the interactive manipulation of attraction sequence (i.e., multiple attractions at a time), thereby ensuring efficiency. On the other hand, we keep humans in the loop of the tour planning process via interactive visualizations. This paper shows the value of tour data published by tourists on the Web for personalized tour planning. [ABSTRACT FROM AUTHOR]

Word clouds are proliferating on the Internet and have received much attention in visual analytics. Although word clouds can help users understand the major content of a document collection quickly, their ability to visually compare documents is limited. This paper introduces a new method to create semantic-preserving word clouds by leveraging tailored seam carving, a well-established content-aware image resizing operator. The method can optimize a word cloud layout by removing a left-to-right or top-to-bottom seam iteratively and gracefully from the layout. Each seam is a connected path of low energy regions determined by a Gaussian-based energy function. With seam carving, we can pack the word cloud compactly and effectively, while preserving its overall semantic structure. Furthermore, we design a set of interactive visualization techniques for the created word clouds to facilitate visual text analysis and comparison. Case studies are conducted to demonstrate the effectiveness and usefulness of our techniques. [ABSTRACT FROM AUTHOR]

Many patients recovering from surgery in wards are disturbed by environmental noise. However, the effects of environmental noise on postoperative pain are unclear. This study aimed to assess the association between postoperative noise and pain. This prospective study included 182 women who underwent cesarean sections. Postoperative noise was continuously recorded, and pain intensity at rest was assessed using a numerical rating scale (NRS) for 0–6, 6–12, 12–18, and 18–24 h after the patients were returned to the ward. Cumulative pain scores were calculated by summing the NRS scores at each time point and comprised the primary outcome. The maximum pain NRS score and analgesic consumption during the 24 h after surgery were also recorded. Mean environmental noise intensity during the daytime was an independent factor for cumulative pain scores, maximum pain scores, and analgesic use during the first postoperative 24 h (β, 0.37; 95% CI, 0.21–0.53 and β, 0.12; 95% CI, 0.07–0.17; P < 0.001 for both; β, 0.86; 95% CI, 0.25–1.46; P = 0.006). Cumulative and maximum NRS pain scores as well as the incidence of NRS ≥ 4 were significantly higher in patients under mean daytime environmental noise of ≥58, than <58 decibels (dB) (8.0 [6.0–11.3] vs. 6.0 (5.0–7.0); 3.0 [2.0–4.0] vs. 2.0 [2.0–2.0, and 25.6% vs. 11.0%; RR, 2.32; 95% CI, 1.19–4.54, respectively; P < 0.001 for all). Higher-level postoperative noise exposure was associated with more severe postoperative pain and increased analgesic needs, as well as a higher incidence of moderate-to-severe pain in patients recovering from cesarean delivery. Our findings indicate that reducing environmental ward noise might benefit for postoperative pain management. CD, cesarean delivery; PCIA, patient-controlled intravenous analgesia; BMI, body mass index; dB, decibel; NRS, number rating scale. [Display omitted] • We explored whether noise affects pain induced by surgery in patients receiving from cesarean delivery. • We found that higher-level postoperative noise correlated with more severe postoperative pain in patients undergoing cesarean delivery. • Given the affordability and safety of noise reduction measures, proactive steps to reduce noise pollution within patient wards is crucial. [ABSTRACT FROM AUTHOR]

Most laparoscopic surgeries under general anesthesia are performed in noisy environments, although the effect of intraoperative noise reduction on postoperative pain remains uncertain. This study aimed to explore whether postoperative pain could be reduced through the intraoperative use of noise-cancelling headphones. This study was conducted as a prospective parallel-group randomized clinical trial. Operating room and surgery room. Ninety patients who underwent laparoscopic surgery under general anesthesia. In the intervention group, noise-cancelling headphones were used to reduce noise intensity during laparoscopic surgery under general anesthesia. The primary outcome was the maximum movement-evoked pain intensity within 24 h post-surgery, measured using a 10-point numeric rating scale. Secondary outcomes included the maximum resting pain score and total opioid consumption during the 24-h period post-surgery. Mean intraoperative noise and the proportion of intraoperative time with noise intensity ≥70 dB were recorded. The maximum movement-evoked pain score was significantly lower in the intervention group than in the control group (mean score [SD], 2.7 [1.0] and 4.0 [1.0], respectively; P < 0.001). The intervention group required significantly fewer opioids than the control group (mean [SD], 44.2 [12.8] and 51.3 [17.5] mg, respectively; P = 0.032). In the control group, but not the intervention group, all postoperative pain scores were significantly associated with the proportion of intraoperative time with noise intensity ≥70 dB, which was an independent risk factor for postoperative pain. During laparoscopic surgery under general anesthesia, intraoperative noise isolation using noise-cancelling headphones is a safe and effective strategy for relieving postoperative pain and decreasing total opioid analgesic consumption. [Display omitted] • Noise-cancelling headphones during surgery reduced postoperative pain scores. • Noise-cancelling headphones during surgery reduced postoperative opioid consumption. • Reduction of hyperpathia contributes to the beneficial effect of noise-cancelling. [ABSTRACT FROM AUTHOR]

We review the state-of-the-art interaction techniques of visualization authoring tools. The visualization tools tend to help users in the creation, exploration, or presentation of visualizations. Also, they allow users to craft expressive designs or extract data from visualizations. The review presents the interaction techniques integrated into the tools for those mentioned above five high-level goals. We cover each goal's tools and summarize how a sequence in the independent interaction techniques leads to the goal. We also discuss how well researchers had evaluated the usability and intuitiveness of interaction techniques. We aimed to reflect on the strengths and weaknesses of the evaluations. To that end, from the perspective of human cognition, we reviewed the goals, procedures, and findings of evaluations. Principally, human cognition is engaged when they perform tasks in a tool. The interaction techniques bridge the gap between human cognition and the goals they want to achieve from the tool. To sum up, in this review, we present a novel triad 'goals-interaction techniques-cognition' taxonomy. Besides, the review suggests the need for further work to enhance tools and understand users. [ABSTRACT FROM AUTHOR]

Opioids are metabolised by enzymes the activities of which vary with the circadian rhythm. We examined whether opioid infusions administered at different times of the day produce varying degrees of opioid-induced hyperalgesia (OIH) in animal experiments and clinical studies. Male Sprague–Dawley rats received remifentanil infusions (1 μg kg−1·min−1 for 1 h) at Zeitgeber times (ZT) 0, 4, 8, 12, 16, or 20 h. Rhythmicity of mechanical hypersensitivity was assayed after the infusion. Mechanical hypersensitivity, drug concentration, and metabolic enzyme activity of Wistar rats that received sufentanil (10 μg kg−1; four consecutive i.p. injections at 15-min intervals) or remifentanil infusion at ZT0 or ZT8 were assayed. Sixty patients who underwent abdominal laparoscopic surgery under general anaesthesia received remifentanil infusion (0.15 μg kg−1 min−1) and sufentanil injection (0.2 μg kg−1) at induction and skin incision, respectively. Postoperative pressure pain sensitivity, pain Numeric Rating Scale (NRS), drug concentrations, and nonspecific esterase activity were assessed. Sprague–Dawley rats that received remifentanil infusion exhibited a robust rhythmic paw withdrawal threshold (JTK_CYCLE: P =0.001, Q=0.001, Phase=26). Wistar rats infused with remifentanil or sufentanil at ZT8 exhibited greater OIH (P <0.001) than those infused at ZT0, with higher blood concentrations (P <0.001) and lower metabolic enzyme activities (P =0.026 and P =0.028, respectively). Patients in the afternoon group exhibited higher pressure pain sensitivity at forearm (P =0.002), higher NRS (P <0.05), higher drug concentrations (sufentanil: P =0.037, remifentanil: P =0.005), and lower nonspecific esterase activity (P =0.024) than the morning group. Opioid infusions administered at different times of day produced varying degrees of OIH, possibly related to circadian rhythms of metabolic enzyme activities. NCT 05234697. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing trend towards taking advantage of audio--visual representations. Previous research has aimed at improving users' performance and engagement with these representations. The attainment of these benefits primarily depends on the effectiveness of audio--visual relationships used to represent the data. However, the visualization field yet lacks an empirical study that guides the effective relationships. Given the compatibility effect between visual and auditory channels, this research presents the effectiveness of four audio channels (timbre, pitch, loudness, and tempo) with six visual channels (spatial position, color, position, length, angle, and area). In six experiments, one per visual channel, we observed how each audio channel, when used with a visual channel, impacted users' ability to perform the differentiation or similarity task accurately. Each experiment provided the ranking of audio channels along a visual channel. Central to our experiments was the evaluation at two stages, and accordingly, we identified the effectiveness. Our results showed that timbre, with spatial position and color, aided in more accurate target identification than the three other audio channels. With position and length, pitch allowed a more accurate judgment of the magnitude of data than loudness and tempo but was less accurate than the other two channels along angle and area. Overall, our experiments showed that the choice of representation methods and tasks had impacted the effectiveness of audio channels. [ABSTRACT FROM AUTHOR]

• Clinical and subclinical seizures have quantifiable pre-ictal EEG differences in mesial temporal lobe epilepsy. • Variability of pre-ictal functional connectivity is able to distinguish seizure phenotypes. • Pre-ictal brain network stability within or between epileptic zones is pertinent to the expression of seizure behavior. We explored whether quantifiable differences between clinical seizures (CSs) and subclinical seizures (SCSs) occur in the pre-ictal state. We analyzed pre-ictal stereo-electroencephalography (SEEG) retrospectively across mesial temporal lobe epilepsy patients with recorded CSs and SCSs. Power spectral density and functional connectivity (FC) were quantified within and between the seizure onset zone (SOZ) and the early propagation zone (PZ), respectively. To evaluate the fluctuation of neural connectivity, FC variability was computed. Measures were further verified by a logistic regression model to evaluate their classification potentiality through the area under the receiver-operating-characteristics curve (AUC). Fifty-four pre-ictal SEEG epochs (27 CSs and 27 SCSs) were selected among 14 patients. Within the SOZ, pre-ictal FC variability of CSs was larger than SCSs in 1–45 Hz during 30 seconds before seizure onset. Pre-ictal FC variability between the SOZ and PZ was larger in SCSs than CSs in 55–80 Hz within 1 minute before onset. Using these two variables, the logistic regression model achieved an AUC of 0.79 when classifying CSs and SCSs. Pre-ictal FC variability within/between epileptic zones, not signal power or FC value, distinguished SCSs from CSs. Pre-ictal epileptic network stability possibly marks seizure phenotypes, contributing insights into ictogenesis and potentially helping seizure prediction. [ABSTRACT FROM AUTHOR]

Data charts are widely used in practices to display insights in complex data. Due to ineffective designs, novice readers may require descriptive content (e.g., chart captions) to understand the implying data stories that may not be accessible in some situations. This problem hinders the usage of data charts for the mass, which has raised deep concerns for visualization researchers. Recently, researchers have proposed deep-learning-based methods to automatically provide text context for data charts. However, these methods ignore the visual links between textual content and visual figures. Moreover, some of them are mainly applied in scalable vector graphics and cannot be easily extended to Internet pictures that are in raster format (e.g., PNG or JPEG). To overcome these limitations, we propose a novel deep-learning-based framework to automatically discover visual insights and generate corresponding text descriptions for chart figures. Specifically, we train a saliency detection model to reveal the salient area that presents the most important data insights and employ an image captioning model to generate the corresponding descriptive text. Meanwhile, we propose a novel method to optimize the saliency map to enable viewers to be aware of visual insights easily. Finally, we develop an interactive system that supports users to upload chart figures and then display chart insights as well as the related descriptions. We evaluate our saliency detection model and image captioning model through quantitative and qualitative experiments and conduct a user study to demonstrate the usage of our system. [ABSTRACT FROM AUTHOR]

Anticipation skill is important for elite racquet sports players. Successful anticipation allows them to predict the actions of the opponent better and take early actions in matches. Existing studies of anticipation behaviors, largely based on the analysis of in‐lab behaviors, failed to capture the characteristics of in‐situ anticipation behaviors in real matches. This research proposes a data‐driven approach for research on anticipation behaviors to gain more accurate and reliable insight into anticipation skills. Collaborating with domain experts in table tennis, we develop a complete solution that includes data collection, the development of a model to evaluate anticipation behaviors, and the design of a visual analytics system called Tac‐Anticipator. Our case study reveals the strengths and weaknesses of top table tennis players' anticipation behaviors. In a word, our work enriches the research methods and guidelines for visual analytics of anticipation behaviors. [ABSTRACT FROM AUTHOR]

This study aims to elucidate the intricate interplay between public attention and public emotion toward multiple social issues. A theoretical framework is developed based on three perspectives including endogenous affect hypothesis, affect transfer hypothesis, and affective intelligence theory. Large-scale longitudinal data with 265 million tweets on five social issues are analyzed using a time series analytical approach. Public attention on social issues can influence public emotion on the issue per se. Social issues interact with one another to attract public attention in both cooperative and competitive ways. Instead of a direct transfer from public emotion to public attention, the public emotion toward a social issue moderates the interaction between the issue and other issue(s). [ABSTRACT FROM AUTHOR]

The digital traces of U.S. members of congress on Twitter enable researchers to observe how these public officials interact with one another in a direct and unobtrusive manner. Using data from Twitter and other sources (e.g., roll-call vote data), this study aims to examine how members of congress connect and communicate with one another on Twitter, why they will connect and communicate with one another in such a way, and what effects such connection and communication among members of congress have on their floor vote behavior. The follower-followee and communication networks of members of congress on Twitter demonstrate a high degree of partisan homogeneity. Members of congress prefer to follow or communicate with other members who are similar to them in terms of partisanship, home state, chamber, and public concern. This condition is known as the homophily effect in social network research. However, the magnitude of the homophily effect is mitigated when the effects of endogenous networking mechanisms (i.e., reciprocity and triadic closure) in such networks are controlled. Follower-followee ties can facilitate political discourse among members of congress on Twitter, whereas both follower-followee and communication ties on Twitter increase the likelihood of vote agreement among members of congress. The theoretical, methodological, and practical implications of the findings are addressed. [ABSTRACT FROM AUTHOR]

The proposed method uses context-preserving, dynamic word clouds to illustrate content evolution. It generates a sequence of word clouds in which related words are grouped together. This sequence is then coupled with a trend chart that summarizes content changes so that users can better explore large collections of documents. [ABSTRACT FROM PUBLISHER]

Information visualization (InfoVis), the study of transforming data, information, and knowledge into interactive visual representations, is very important to users because it provides mental models of information. The boom in big data analytics has triggered broad use of InfoVis in a variety of domains, ranging from finance to sports to politics. In this paper, we present a comprehensive survey and key insights into this fast-rising area. The research on InfoVis is organized into a taxonomy that contains four main categories, namely empirical methodologies, user interactions, visualization frameworks, and applications, which are each described in terms of their major goals, fundamental principles, recent trends, and state-of-the-art approaches. At the conclusion of this survey, we identify existing technical challenges and propose directions for future research. [ABSTRACT FROM AUTHOR]

With the growing popularity of visualizations in various fields, visualization comprehension has gained considerable attention. In this work, we focus on the effect of data size and pattern salience on comprehension of scatterplot, a popular visualization type. We began with a preliminary study in which we interviewed 50 people in terms of comprehension difficulties of 90 different visualizations. The results reveal that data size is one of the top three factors affecting visualization comprehension. Besides, the effect of data size probably depends on the pattern salience within the data. Therefore, we carried out our experiment on the effect of data size and data-related pattern salience on three intermediate-level comprehension tasks, namely finding anomalies, judging correlation, and identifying clusters. The tasks were conducted on the scatterplot due to its familiarity to users and ability to support diverse tasks. Through the experiment, we found a significant interaction effect of data size and pattern salience on the comprehension of the trends in scatterplots. In specific conditions of pattern salience, data size impacts the judgment of anomalies and cluster centers. We discussed the findings in our experiment and further summarized the factors in visualization comprehension. [ABSTRACT FROM AUTHOR]

Table tennis matches consist of many stroke sequences where two players hit the ball interactively and consecutively until one fails to hit the ball. Players usually employ many complicated playing techniques at each stroke in highly antagonistic, variable, and flexible matches. In-depth comparative analyses of players' stroke sequences are necessary to obtain insights into the technical playing patterns of players. Experts commonly use spreadsheets to browse and compare strokes one by one, and this process is tedious and prone to errors. Statistical analyses are limited to well-defined patterns (e.g., value distribution and relation significance) and fail to present complex and peculiar patterns. We collaborated with experts to dig out soft patterns of stroke sequences and proposed a novel interactive visualization system to present and compare the patterns. The main visualization challenge is to display the multivariate stroke sequence and the spatial variation patterns. We designed a glyph-based pattern view to solve the challenge. These comprehensible visualizations and coordinated views in the system allow efficient comparative analysis of stroke sequence patterns and are highly commended by domain experts, who have identified several new and interesting patterns using the system. We demonstrated the effectiveness and usability of the visualization system through case studies with table tennis experts.. [ABSTRACT FROM AUTHOR]

Understanding large, complex networks is important for many critical tasks, including decision making, process optimization, and threat detection. Existing network analysis tools often lack intuitive interfaces to support the exploration of large scale data. We present a visual recommendation system to help guide users during navigation of network data. Collaborative filtering, similarity metrics, and relative importance are used to generate recommendations of potentially significant nodes for users to explore. In addition, graph layout and node visibility are adjusted in real-time to accommodate recommendation display and to reduce visual clutter. Case studies are presented to show how our design can improve network exploration. [ABSTRACT FROM AUTHOR]

The increasing availability of spatiotemporal data provides unprecedented opportunities for understanding the structure of an urban area in terms of people's activity pattern and how they form the latent regions over time. However, existing solutions are limited in their capacity of capturing the evolutionary patterns of dynamic latent regions within urban context. In this work, we introduce an interactive visual analysis approach, EcoLens, that allows analysts to progressively explore and analyze the complex dynamic segmentation patterns of a city using traffic data. We propose an extended nonnegative matrix factorization-based algorithm smoothed over both spatial and temporal dimensions to capture the spatiotemporal dynamics of the city. The algorithm also ensures the orthogonality of its result to facilitate the interpretation of different patterns. A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time. We evaluate the effectiveness of our system via case studies using a real-world dataset and a qualitative interview with the domain expert. [ABSTRACT FROM AUTHOR]

By encoding semantic relations into relative positions, word clouds have shown the capability to deliver richer messages than purely visualizing word frequencies. Existing studies mainly focus on layout algorithms that cluster related words, preserve temporal coherence, and optimize spatial shapes. However, they cannot fully convey multiple relations among words and their evolvement through relative positions and static representations. In this paper, we explore animated word clouds that take advantage of storytelling strategies to present interactions between words and show the dynamic process of content changes, thus communicating the underlying stories. We initially create several exemplars of animated word clouds with designers through a structured iterative design process. These exemplars lead to a preliminary design space that distills essential narrative elements with design choices. Based on the design space, we develop a prototype tool, DancingWords, which provides story-oriented interactions and automatic layouts for users to generate animated word clouds. We evaluate the expressiveness and usefulness of our system through several example animated stories and a usability study with general users. The results show that DancingWords allows users to produce appealing storytelling videos easily and quickly for communication. [ABSTRACT FROM AUTHOR]

Short-form videos are an increasingly prevalent medium for storytelling in journalism and marketing, of which information can be greatly enhanced by animated data visualizations. However, there is no prior research that systematically investigates how to augment such short videos with data visualizations in an effective way. We conducted a design workshop with experienced video, animation designers and visualization experts to discuss principles and practices for augmenting short-form videos with data visualizations. After the workshop, we summarized the participants' design considerations and proposed 20 design guidelines. We further collected design purposes of the participants and associated these purposes with the guidelines. Finally, we conducted a crowd-sourcing study and a task-based evaluation to validate the effectiveness and usability of the guidelines. Results indicate that our guidelines can significantly improve the videos accompanied with data visualizations and help novices easily obtain desired knowledge when augmenting videos. [ABSTRACT FROM AUTHOR]

Temporal ranking ensembles indicate time-evolving multivariate rankings. Such data can be commonly found in our daily life, for example, different rankings of universities (QS, ARWU, THE, and USNews) over year and those of NBA players over season. Effective analysis and tracking of rankings allow users to gain insights into the overall ranking change over time and seek the explanation for the change. This paper introduces a novel visual analytics approach for characterizing and visualizing the uncertainty, dynamics, and differences of ranking ensemble data. A novel visual design is proposed to characterize the evolution pattern, distribution, and uncertainty of a large number of temporal ranking ensembles. The evolutionary ranking ensembles are progressively explored, tracked, and compared by means of an intuitive visualization system. Two case studies and a task-driven user study conducted on real datasets demonstrate the effectiveness and feasibility of the implemented system. [ABSTRACT FROM AUTHOR]

Abstract: Streaming data can be found in many different scenarios, in which data are generated and arriving continuously. Sampling approaches have been proven as an effective means to cope with the sheer volume of the streaming data. However, sampling methods also introduce uncertainty, which can affect the reliability of subsequent analysis and visualization. In this paper, we propose a novel model called PDm and visualization named uncertainty tree to present uncertainty that arises from sampling streaming data. PDm is first introduced to characterize uncertainty of streaming data, and an optimization method is then proposed to minimize uncertainty. Uncertainty tree is further developed to enhance data understanding by visualizing uncertainty and revealing temporal patterns of streaming data. Lastly, a quantitative evaluation and real-world examples have been conducted to demonstrate the effectiveness and efficacy of the proposed techniques.Graphical abstract: [ABSTRACT FROM AUTHOR]

Interpersonal ties, such as strong ties and weak ties, describe the information carried by an edge in social network. Tracking the dynamic changes of interpersonal ties can thus enhance our understanding of the evolution of a complex network. Nevertheless, existing studies in dynamic network visualization mostly focus on the temporal changes of nodes or structures of the network without an adequate support of analysis and exploration of the temporal changes of interpersonal ties. In this paper, we introduce a new visual analytics method that enables interactive analysis and exploration of the dynamic changes of interpersonal ties. The method integrates four well-linked visualizations, including a scatterplot, a pixelbar chart, a layered graph, and a node-link diagram, to allow for multi-perspective analysis of the evolution of interpersonal ties. The scatterplot created by multi-dimensional scaling can help reveal the clusters of ties and detect abnormal ties, while other visualizations allow users to explore the clusters of ties interactively from different perspectives. Two case studies have been conducted to demonstrate the effectiveness of our approach. Graphical abstract: [ABSTRACT FROM AUTHOR]

Analyzing the user behaviors of multiplayer online games can help understand the sociality and characteristics of players in the virtual world. The primary task is to characterize the game life and its evolution within the game. We propose a novel network-based representation, EvolutionLine Graph, which illustrates the evolving behavior of massive game players as a sequence of time-oriented transitions among various status. We design and implement a novel visual analytics system, GameLifeVis, that supports the visualization, exploration, and analysis of multi-level user behaviors in an integrated visual interface. We exemplify the efficiency of our approach with case studies on a multi-faceted dataset collected within a popular online game (15 million players) in 18 months. Graphical abstract: [ABSTRACT FROM AUTHOR]

Graph sampling, simplying the networks while preserving primary graph characteristics, provides a convenient means for exploring large network. During the last few years a variety of graph sampling algorithms have been proposed, and the evaluation and comparison of the algorithms has witnessed a growing interest. Although different tests have been conducted, an important aspect of graph sampling, namely, uncertainty in graph sampling, has been ignored so far. Additionally, existing studies mainly rely on simple statistical analysis and a few relatively small datasets. They may not be applicable to other more complicated graphs with much larger numbers of nodes and edges. Furthermore, while graph clustering is becoming increasingly important, it is still unknown how different sampling algorithms and their associated uncertainty can impact the subsequent graph analysis, such as graph clustering. In this work, we propose an efficient visual analytics framework for measuring the uncertainty from different graph sampling methods and quantifying the influence of the uncertainty in general graph analysis procedures. A spreadsheet-style visualization with rich user interactions is presented to facilitate visual comparison and analysis of multiple graph sampling algorithms. Our framework helps users gain a better understanding of the graph sampling methods in producing uncertainty information. The framework also makes it possible for users to quickly evaluate graph sampling algorithms and select the most appropriate one for their applications. [ABSTRACT FROM AUTHOR]

Purpose Massively multiplayer online role-playing games (MMORPGs) create quasi-real social systems in which players can interact with one another, and quasi-real economic systems where players can consume and trade in-game items with virtual currency. The in-game currency price, an important indicator of a virtual economy, is highly contingent on players’ behavioral interaction in MMORPGs. The purpose of this paper is to adopt a network perspective to examine how topological characteristics of social networks in an MMORPG, namely, network externalities, density, and closure, would exert impacts on the in-game currency price.Design/methodology/approach Players’ behavioral data were collected from a popular MMORPG in China on a weekly basis for 52 weeks. With a time series analytical approach, the empirical model for the price function of in-game currency was estimated with vector autoregression.Findings The results show that the number of core avatars and network density are positively associated with in-game currency price, while network closure has a negative effect on in-game currency price. However, in-game currency price is found to have no significant relationship with the trade volume of the currency.Originality/value This study fills in an important research gap by investigating factors influencing the in-game currency price of MMORPGs from a network perspective, which contributes to the existing literature of network effects and advances our understanding about how players’ interaction will influence the dynamics of a virtual economy. The findings could offer useful insights for online game companies to better understand their players’ social interaction and consumption behavior. [ABSTRACT FROM AUTHOR]

Previous viewpoint selection methods in volume visualization are generally based on some deterministic measures of viewpoint quality. However, they may not express the familiarity and aesthetic sense of users for features of interest. In this paper, we propose an image-based viewpoint selection model to learn how visualization experts choose representative viewpoints for volumes with similar features. For a given volume, we first collect images with similar features, and these images reflect the viewpoint preferences of the experts when visualizing these features. Each collected image tallies votes to the viewpoints with the best matching based on an image similarity measure, which evaluates the spatial shape and appearance similarity between the collected image and the rendered image from the viewpoint. The optimal viewpoint is the one with the most votes from the collected images, that is, the viewpoint chosen by most visualization experts for similar features. We performed experiments on various volumes available in volume visualization, and made comparisons with traditional viewpoint selection methods. The results demonstrate that our model can select more canonical viewpoints, which are consistent with human perception. [ABSTRACT FROM AUTHOR]