A community-imbalanced graph refers to a graph containing multiple communities with large differences in node and edge scales. Graph sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. However, existing graph sampling algorithms may encounter several problems, including the loss of small communities, disconnections between communities, and distortions of community scale distribution, on maintaining the community structures in a community-imbalanced graph. In this work, a new quality indicator is proposed to determine if a graph can be regarded as a community-imbalanced graph. A community-imbalanced graph sampling (CIGS) algorithm is proposed to address the community-imbalanced graph sampling problems. Three new evaluation metrics are proposed to assess the performance of community structure maintenance of graph sampling. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the proposed CIGS.

Force-directed graph layout methods are widely used in node-link diagram. However, applying uniform force computations to all nodes or node pairs limits their local adjustment ability, which is required to generate specific graph layouts. For example, users may want to increase the repulsive force among a group of densely clustered nodes while keeping the repulsive force among the other nodes unchanged, thereby facilitating the perception of connection structures among the group of nodes. In this study, a unified force-directed graph layout framework supporting local adjustments (GLLA) is proposed. GLLA consists of three core components: two unified parametric force expressions, a unified force model, and an improved stochastic gradient descent-based layout solver. The three components enable GLLA to reproduce the graph layout behaviors of mainstream force-directed algorithms and possess the capability to perform local adjustments on graph layouts. A performance experiment demonstrates that GLLA-implemented graph layouts are comparable to those produced by mainstream force-directed algorithms in graph structure preservation and readability while achieving notable improvements in time performance. Four real-world use cases show that GLLA can satisfy various local adjustment needs in diverse graph layout scenarios.

Porous materials (e.g., weathered stone, industrial coatings) exhibit complex optical effects due to their micrograin and pore structures, posing challenges for photorealistic rendering. Explicit geometry models struggle to characterize their micrograin distributions at microscopic scales, while single-scattering microfacet model fails to accurately capture the multiple-scattering effects and causes energy non-conservation artifacts, manifesting as unrealistic luminance decay. We propose an enhanced micrograin BSDF model that accurately accounts for multiple scattering. First, we introduce a visible normal distribution function (VNDF) sampling method via rejection sampling. Building on VNDF sampling, we derive a position-free microsurface formulation incorporating both inter-micrograin and micrograin-to-base interactions. Furthermore, we propose a practical random walk method to simulate microsurface scattering, which accurately solves the derived formulation. Our micrograin BSDF model effectively eliminates the energy loss artifacts inherent in the previous model while significantly reducing noise, providing a physically accurate yet artistically controllable solution for rendering porous materials.

Cybercrime poses a severe threat to the entire Internet ecosystem. Various cyber assets, such as domain name, IP address, and security certificate, are staple infrastructures of cybercrime. A cyber asset graph (CAG) is a collection of closely related cyber assets held by a cybercrime gang to support online criminal activities. Analyzing CAGs provides rich data insights for cybercrime investigation and governance. This paper introduces an open dataset of CAGs comprised of 2.37 million nodes with eight types of cyber assets and 3.28 million edges with eleven types of relations. This paper introduces the dataset construction process, applied areas, and the experience of using the dataset in the ChinaVis Data Challenge 2022. This dataset contains numerous CAGs of cybercrime gangs in the real world, which is the first open dataset of CAGs for cybercrime research. This dataset can also support the development of other application-oriented areas, such as cyber asset management and cyber-physical-social system, and various graph-related research areas, such as graph theory, graph mining, and graph visualization.

Degree centrality (DC) is a widely used metric that measures node importance in data space. A node-link diagram is a commonly used graph visualization to help viewers identify important nodes in visual space. Previous graph perception studies largely concentrated on revealing perception principles in visual space. However, they rarely investigated the intrinsic relations between computed and perceived important nodes by jointly using data and visual spaces, thereby hindering a deep integration of computational and interactive graph analytics. To address this gap, we adopted the visual perception of DC as a representative object to conduct a graph perception study by jointly using data and visual spaces. Two research questions were defined. (RQ1) Can viewers accurately estimate the relative DCs of the given nodes in a node-link diagram through visual perception? (RQ2) What visual factors influence viewers' visual estimation of relative DCs? A controlled user experiment was conducted to answer the questions. Results showed that: (1) The participants failed to estimate the relative DCs accurately, particularly when the DC differences between nodes were not great. (2) Seven visual factors influencing the tasks were summarized, such as the size of the visual receptive region of a node, the link and node densities in the visual receptive region of the node, and the wrapping angle of the node's neighbors. (3) The factors presented certain priorities in complicated situations. These findings provide rich implications for graph analytics, such as utilizing the findings to optimize graph visualizations to achieve the desired consistency between computed and perceived important nodes.

Interface icons are often scaled to adapt to different displays in cross-device collaborations. However, adaptive scaling of icons may cause perceptual bias in how icon arrays are visually perceived, which reduces usability and coherent user experience. This article presents an empirical study that evaluates the perceptual bias in the consistency of icon spacing and size caused by adaptive scaling. Then the impact of various visual features of icons (i.e., the border shape, polarity, and composition) on the perceptual bias are investigated. In this study, we found that cross-device scaling of icons causes a perceptual bias in the consistency of icon spacing and size. The shape of the icon border has a significant difference in the perceived spacing of icons, and the bias of the round shape is smaller than that of the square shape. Moreover, changing the icon polarity can affect the perceptual bias of consistency in icon size. These findings are expected to propose scaling recommendations for improving the visual consistency of icon arrays across.

Blockchain-based cryptocurrencies, such as Bitcoin (BTC) and Ethereum (ETH), are newly emerging financial assets. Cryptocurrency exchanges are marketplaces for cryptocurrency circulation while becoming a new venue for money laundering. In this work, we cooperate with a cryptocurrency exchange to investigate new solutions for anti-money laundering in cryptocurrency exchanges. First, we learn the domain knowledge of cryptocurrency transactions and summarize data analytical requirements of transaction supervisors in their daily work of anti-money laundering. Then, we propose a visual analysis approach to support their daily work. The approach consists of a new algorithm that automatically detects suspicious money laundering accounts and a multiviewed user interface that visualizes the algorithm results and relevant transaction data. An abacus-inspired visualization is designed in the interface to depict transaction patterns contained in numerous cryptocurrency transactions, which can help supervisors find money laundering clues and deduce the trading tactic adopted by launderers. Finally, an algorithm performance experiment, a case study, and a field study are conducted with real-world data to demonstrate the effectiveness of our solution.

NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.

The past decade has witnessed the superior power of deep neural networks (DNNs) in applications across various domains. However, training a high-quality DNN remains a non-trivial task due to its massive number of parameters. Visualization has shown great potential in addressing this situation, as evidenced by numerous recent visualization works that aid in DNN training and interpretation. These works commonly employ a strategy of logging training-related data and conducting post-hoc analysis. Based on the results of offline analysis, the model can be further trained or fine-tuned. This strategy, however, does not cope with the increasing complexity of DNNs, because (1) the time-series data collected over the training are usually too large to be stored entirely; (2) the huge I/O overhead significantly impacts the training efficiency; (3) post-hoc analysis does not allow rapid human-interventions (e.g., stop training with improper hyper-parameter settings to save computational resources). To address these challenges, we propose an in-situ visualization and analysis framework for the training of DNNs. Specifically, we employ feature extraction algorithms to reduce the size of training-related data in-situ and use the reduced data for real-time visual analytics. The states of model training are disclosed to model designers in real-time, enabling human interventions on demand to steer the training. Through concrete case studies, we demonstrate how our in-situ framework helps deep learning experts optimize DNNs and improve their analysis efficiency.

Data have become valuable assets for enterprises. Data governance aims to manage and reuse data assets to facilitate enterprise management and product innovations. A data lineage graph (DLG) is an abstracted collection of data assets and their data lineages in data governance. Analyzing DLGs can provide rich data insights for data governance. However, the progress of data governance technologies is hindered by the shortage of available open datasets for DLGs. This paper introduces an open dataset of DLGs, including the DLG model, the dataset construction process, and applied areas. This real-world dataset is sourced from Huawei Cloud Computing Technology Company Limited, which contains 18 DLGs with three types of data assets and two types of relations. To the best of our knowledge, this dataset is the first open dataset of DLGs for data governance. This dataset can also support the development of other application areas, such as graph analytics and visualization.

Malicious webshells currently present tremendous threats to cloud security. Most relevant studies and open webshell datasets consider malicious webshell defense as a binary classification problem, that is, identifying whether a webshell is malicious or benign. However, a fine-grained multi-classification is urgently needed to enable precise responses and active defenses on malicious webshell threats. This paper introduces a malicious webshell family dataset named MWF to facilitate webshell multi-classification researches. This dataset contains 1,359 malicious webshell samples originally obtained from the cloud servers of Alibaba Cloud. Each of them is provided with a family label. The samples of the same family generally present similar characteristics or behaviors. The dataset has a total of 78 families and 22 outliers. Moreover, this paper introduces the human-machine collaboration process that is adopted to remove benign or duplicate samples, address privacy issues, and determine the family of each sample. This paper also compares the distinguished features of the MWF dataset with previous datasets and summarizes the potential applied areas in cloud security and generalized sequence, graph, and tree data analytics and visualization.

Neuronal feature visualization is widely used in Explainable Artificial Intelligence (XAI). It can provide an intuitive visualization to depict the feature extraction of an individual neuron in a Convolutional Neural Network (CNN). However, it is extremely exhaustive for human users to identify highly interpretable visualizations by manually browsing massive neurons contained in a CNN. Inspired by the Shapley Value Method in coalitional game theory, this paper proposes a metric to quantitatively measure the interpretability of a neuronal feature visualization by calculating the similarity between the SHAP (SHapley Additive exPlanation) image and the visualization. This metric can help human users quickly find highly interpretable neuronal feature visualizations for understanding the classification results of a CNN.

We propose a contrastive dimensionality reduction approach (CDR) for interactive visual cluster analysis. Although dimensionality reduction of high-dimensional data is widely used in visual cluster analysis in conjunction with scatterplots, there are several limitations on effective visual cluster analysis. First, it is non-trivial for an embedding to present clear visual cluster separation when keeping neighborhood structures. Second, as cluster analysis is a subjective task, user steering is required. However, it is also non-trivial to enable interactions in dimensionality reduction. To tackle these problems, we introduce contrastive learning into dimensionality reduction for high-quality embedding. We then redefine the gradient of the loss function to the negative pairs to enhance the visual cluster separation of embedding results. Based on the contrastive learning scheme, we employ link-based interactions to steer embeddings. After that, we implement a prototype visual interface that integrates the proposed algorithms and a set of visualizations. Quantitative experiments demonstrate that CDR outperforms existing techniques in terms of preserving correct neighborhood structures and improving visual cluster separation. The ablation experiment demonstrates the effectiveness of gradient redefinition. The user study verifies that CDR outperforms t-SNE and UMAP in the task of cluster identification. We also showcase two use cases on real-world datasets to present the effectiveness of link-based interactions.

A time-frequency diagram is a commonly used visualization for observing the time-frequency distribution of radio signals and analyzing their time-varying patterns of communication states in radio monitoring and management. While it excels when performing short-term signal analyses, it becomes inadaptable for long-term signal analyses because it cannot adequately depict signal time-varying patterns in a large time span on a space-limited screen. This research thus presents an abstract signal time-frequency (ASTF) diagram to address this problem. In the diagram design, a visual abstraction method is proposed to visually encode signal communication state changes in time slices. A time segmentation algorithm is proposed to divide a large time span into time slices. Three new quantified metrics and a loss function are defined to ensure the preservation of important time-varying information in the time segmentation. An algorithm performance experiment and a user study are conducted to evaluate the effectiveness of the diagram for long-term signal analyses.

One main challenge for simplifying node-link diagrams of large-scale social networks lies in that simplified graphs generally contain dense subgroups or cohesive subgraphs. Graph triangles quantify the solid and stable relationships that maintain cohesive subgraphs. Understanding the mechanism of triangles within cohesive subgraphs contributes to illuminating patterns of connections within social networks. However, prior works can hardly handle and visualize triangles in cohesive subgraphs. In this paper, we propose a triangle-based graph simplification approach that can filter and visualize cohesive subgraphs by leveraging a triangle-connectivity called k-truss and a force-directed algorithm. We design and implement TriGraph, a web-based visual interface that provides detailed information for exploring and analyzing social networks. Quantitive comparions with existing methods, two case studies on real-world datasets, and the feedback from domain experts demonstrate the effectiveness of TriGraph.

A graph is an abstract model that represents relations among entities, for example, the interactions between characters in a novel. A background story endows entities and relations with real-world meanings and describes the semantics and context of the abstract model, for example, the actual story that the novel presents. Considering practical experience and prior research, human viewers who are familiar with the background story of a graph and those who do not know the background story may perceive the same graph differently. However, no previous research has adequately addressed this problem. This research article thus presents an evaluation that investigated the effects of background stories on graph perception. Three hypotheses that focused on the role of visual focus areas, graph structure identification, and mental model formation on graph perception were formulated and guided three controlled experiments that evaluated the hypotheses using real-world graphs with background stories. An analysis of the resulting experimental data, which compared the performance of participants who read and did not read the background stories, obtained a set of instructive findings. First, having knowledge about a graph's background story influences participants' focus areas during interactive graph explorations. Second, such knowledge significantly affects one's ability to identify community structures but not high degree and bridge structures. Third, this knowledge influences graph recognition under blurred visual conditions. These findings can bring new considerations to the design of storytelling visualizations and interactive graph explorations.

In recent years, technologies of indoor crowd positioning and movement data analysis have received widespread attention in the fields of reliability management, indoor navigation, and crowd behavior monitoring. However, only a few indoor crowd movement trajectory datasets are available to the public, thus restricting the development of related research and application. This article contributes a new benchmark dataset of indoor crowd movement trajectories. This dataset records the movements of over 5000 participants at a three-day large academic conference in a two-story indoor venue. The conference comprises varied activities, such as academic seminars, business exhibitions, a hacking contest, interviews, tea breaks, and a banquet. The participants are divided into seven types according to participation permission to the activities. Some of them are involved in anomalous events, such as loss of items, unauthorized accesses, and equipment failures, forming a variety of spatial–temporal movement patterns. In this article, we first introduce the scenario design, entity and behavior modeling, and data generator of the dataset. Then, a detailed ground truth of the dataset is presented. Finally, we describe the process and experience of applying the dataset to the contest of ChinaVis Data Challenge 2019. Evaluation results of the 75 contest entries and the feedback from 359 contestants demonstrate that the dataset has satisfactory completeness, and usability, and can effectively identify the performance of methods, technologies, and systems for indoor trajectory analysis.

Sampling is a widely used graph reduction technique to accelerate graph computations and simplify graph visualizations. By comprehensively analyzing the literature on graph sampling, we assume that existing algorithms cannot effectively preserve minority structures that are rare and small in a graph but are very important in graph analysis. In this work, we initially conduct a pilot user study to investigate representative minority structures that are most appealing to human viewers. We then perform an experimental study to evaluate the performance of existing graph sampling algorithms regarding minority structure preservation. Results confirm our assumption and suggest key points for designing a new graph sampling approach named mino-centric graph sampling (MCGS). In this approach, a triangle-based algorithm and a cut-point-based algorithm are proposed to efficiently identify minority structures. A set of importance assessment criteria are designed to guide the preservation of important minority structures. Three optimization objectives are introduced into a greedy strategy to balance the preservation between minority and majority structures and suppress the generation of new minority structures. A series of experiments and case studies are conducted to evaluate the effectiveness of the proposed MCGS.

Numerous sampling strategies have been proposed to simplify large-scale networks for highly readable visualizations. It is of great challenge to preserve contextual structures formed by nodes and edges with tight relationships in a sampled graph, because they are easily overlooked during the process of sampling due to their irregular distribution and immunity to scale. In this paper, a new graph sampling method is proposed oriented to the preservation of contextual structures. We first utilize a graph representation learning (GRL) model to transform nodes into vectors so that the contextual structures in a network can be effectively extracted and organized. Then, we propose a multi-objective blue noise sampling model to select a subset of nodes in the vectorized space to preserve contextual structures with the retention of relative data and cluster densities in addition to those features of significance, such as bridging nodes and graph connections. We also design a set of visual interfaces enabling users to interactively conduct context-aware sampling, visually compare results with various sampling strategies, and deeply explore large networks. Case studies and quantitative comparisons based on real-world datasets have demonstrated the effectiveness of our method in the abstraction and exploration of large networks.

Maritime transports play a critical role in international trade and commerce. Massive vessels sailing around the world continuously generate vessel trajectory data that contain rich spatial–temporal patterns of vessel navigations. Analyzing and understanding these patterns are valuable for maritime traffic surveillance and management. As essential techniques in complex data analysis and understanding, visualization and visual analysis have been widely used in vessel trajectory data analysis. This paper presents a literature review on the visualization and visual analysis of vessel trajectory data. First, we introduce commonly used vessel trajectory data sets and summarize main operations in vessel trajectory data preprocessing. Then, we provide a taxonomy of visualization and visual analysis of vessel trajectory data based on existing approaches and introduce representative works in details. Finally, we expound on the prospects of the remaining challenges and directions for future research.

A graph contains plentiful structures. Some minority structures are important, such as high degree nodes and bridges. Detecting these minority structures is beneficial to accelerate computational graph analysis and improve the comprehension of graph visualization. Regarding four typical minority structures, this paper proposes two algorithms to detect these structures fast and efficiently. A set of experiments demonstrate the effectiveness of the proposed algorithms.

Cryptocurrencies represented by Bitcoin have fully demonstrated their advantages and great potential in payment and monetary systems during the last decade. The mining pool, which is considered the source of Bitcoin, is the cornerstone of market stability. The surveillance of the mining pool can help regulators effectively assess the overall health of Bitcoin and issues. However, the anonymity of mining-pool miners and the difficulty of analyzing large numbers of transactions limit in-depth analysis. It is also a challenge to achieve intuitive and comprehensive monitoring of multi-source heterogeneous data. In this study, we present SuPoolVisor, an interactive visual analytics system that supports surveillance of the mining pool and de-anonymization by visual reasoning. SuPoolVisor is divided into pool level and address level. At the pool level, we use a sorted stream graph to illustrate the evolution of computing power of pools over time, and glyphs are designed in two other views to demonstrate the influence scope of the mining pool and the migration of pool members. At the address level, we use a force-directed graph and a massive sequence view to present the dynamic address network in the mining pool. Particularly, these two views, together with the Radviz view, support an iterative visual reasoning process for de-anonymization of pool members and provide interactions for cross-view analysis and identity marking. Effectiveness and usability of SuPoolVisor are demonstrated using three cases, in which we cooperate closely with experts in this field.

Analysts commonly investigate the data distributions derived from statistical aggregations of data that are represented by charts, such as histograms and binned scatterplots, to visualize and analyze a large-scale dataset. Aggregate queries are implicitly executed through such a process. Datasets are constantly extremely large; thus, the response time should be accelerated by calculating predefined data cubes. However, the queries are limited to the predefined binning schema of preprocessed data cubes. Such limitation hinders analysts' flexible adjustment of visual specifications to investigate the implicit patterns in the data effectively. Particularly, RSATree enables arbitrary queries and flexible binning strategies by leveraging three schemes, namely, an R-tree-based space partitioning scheme to catch the data distribution, a locality-sensitive hashing technique to achieve locality-preserving random access to data items, and a summed area table scheme to support interactive query of aggregated values with a linear computational complexity. This study presents and implements a web-based visual query system that supports visual specification, query, and exploration of large-scale tabular data with user-adjustable granularities. We demonstrate the efficiency and utility of our approach by performing various experiments on real-world datasets and analyzing time and space complexity.

Scatterplots are frequently scaled to fit display areas in multi-view and multi-device data analysis environments. A common method used for scaling is to enlarge or shrink the entire scatterplot together with the inside points synchronously and proportionally. This process is called geometric scaling. However, geometric scaling of scatterplots may cause a perceptual bias, that is, the perceived and physical values of visual features may be dissociated with respect to geometric scaling. For example, if a scatterplot is projected from a laptop to a large projector screen, then observers may feel that the scatterplot shown on the projector has fewer points than that viewed on the laptop. This paper presents an evaluation study on the perceptual bias of visual features in scatterplots caused by geometric scaling. The study focuses on three fundamental visual features (i.e., numerosity, correlation, and cluster separation) and three hypotheses that are formulated on the basis of our experience. We carefully design three controlled experiments by using well-prepared synthetic data and recruit participants to complete the experiments on the basis of their subjective experience. With a detailed analysis of the experimental results, we obtain a set of instructive findings. First, geometric scaling causes a bias that has a linear relationship with the scale ratio. Second, no significant difference exists between the biases measured from normally and uniformly distributed scatterplots. Third, changing the point radius can correct the bias to a certain extent. These findings can be used to inspire the design decisions of scatterplots in various scenarios.

Traditional radio monitoring and management largely depend on radio spectrum data analysis, which requires considerable domain experience and heavy cognition effort and frequently results in incorrect signal judgment and incomprehensive situation awareness. Faced with increasingly complicated electromagnetic environments, radio supervisors urgently need additional data sources and advanced analytical technologies to enhance their situation awareness ability. This paper introduces a visual analytics approach for electromagnetic situation awareness. Guided by a detailed scenario and requirement analysis, we first propose a signal clustering method to process radio signal data and a situation assessment model to obtain qualitative and quantitative descriptions of the electromagnetic situations. We then design a two-module interface with a set of visualization views and interactions to help radio supervisors perceive and understand the electromagnetic situations by a joint analysis of radio signal data and radio spectrum data. Evaluations on real-world data sets and an interview with actual users demonstrate the effectiveness of our prototype system. Finally, we discuss the limitations of the proposed approach and provide future work directions.

Massive Sequence View(MSV) is an important timeline-based technique for dynamic network visualization. However, it often suffers from severe visual clutter when limited screen space holds excessive network edges. Inspired by the use of graph sampling in static graph analysis, we propose to utilize graph sampling to reduce visual clutter in MSV. An edge sampling method based on accept-reject random sampling is designed for visualizing dynamic network via MSV. The method is able to improve the overall readability of MSV while preserving time varying network behaviors. It is also a preliminary attempt to apply graph-sampling technique into dynamic network analysis.

People face data-rich manufacturing environments in Industry 4.0. As an important technology for explaining and understanding complex data, visual analytics has been increasingly introduced into industrial data analysis scenarios. With the durability test of automotive starters as background, this study proposes a visual analysis approach for understanding large-scale and long-term durability test data. Guided by detailed scenario and requirement analyses, we first propose a migration-adapted clustering algorithm that utilizes a segmentation strategy and a group of matching-updating operations to achieve an efficient and accurate clustering analysis of the data for starting mode identification and abnormal test detection. We then design and implement a visual analysis system that provides a set of user-friendly visual designs and lightweight interactions to help people gain data insights into the test process overview, test data patterns, and durability performance dynamics. Finally, we conduct a quantitative algorithm evaluation, case study, and user interview by using real-world starter durability test datasets. The results demonstrate the effectiveness of the approach and its possible inspiration for the durability test data analysis of other similar industrial products.

Fuzzy clustering assigns a probability of membership for a datum to a cluster, which veritably reflects real-world clustering scenarios but significantly increases the complexity of understanding fuzzy clusters. Many studies have demonstrated that visualization techniques for multi-dimensional data are beneficial to understand fuzzy clusters. However, no empirical evidence exists on the effectiveness and efficiency of these visualization techniques in solving analytical tasks featured by fuzzy clusters. In this paper, we conduct a controlled experiment to evaluate the ability of fuzzy clusters analysis to use four multi-dimensional visualization techniques, namely, parallel coordinate plot, scatterplot matrix, principal component analysis, and Radviz. First, we define the analytical tasks and their representative questions specific to fuzzy clusters analysis. Then, we design objective questionnaires to compare the accuracy, time, and satisfaction in using the four techniques to solve the questions. We also design subjective questionnaires to collect the experience of the volunteers with the four techniques in terms of ease of use, informativeness, and helpfulness. With a complete experiment process and a detailed result analysis, we test against four hypotheses that are formulated on the basis of our experience, and provide instructive guidance for analysts in selecting appropriate and efficient visualization techniques to analyze fuzzy clusters.

A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.

In team-based workplaces, reviewing and reflecting on the content from a previously held meeting can lead to better planning and preparation. However, ineffective meeting summaries can impair this process, especially when participants have difficulty remembering what was said and what its context was. To assist with this process, we introduce MeetingVis, a visual narrative-based approach to meeting summarization. MeetingVis is composed of two primary components: (1) a data pipeline that processes the spoken audio from a group discussion, and (2) a visual-based interface that efficiently displays the summarized content. To design MeetingVis, we create a taxonomy of relevant meeting data points, identifying salient elements to promote recall and reflection. These are mapped to an augmented storyline visualization, which combines the display of participant activities, topic evolutions, and task assignments. For evaluation, we conduct a qualitative user study with five groups. Feedback from the study indicates that MeetingVis effectively triggers the recall of subtle details from prior meetings: all study participants were able to remember new details, points, and tasks compared to an unaided, memory-only baseline. This visual-based approaches can also potentially enhance the productivity of both individuals and the whole team.

Fuzzy clustering analysis is an effective method to describe the uncertainty relationship between data objects and clusters. However, fuzzy clustering results will become complex and high-dimensional membership degree matrixes when they contain a large number of data points and multiple clusters. In this paper, we propose a Radviz-based interactive visualization to help users understand fuzzy clustering results. Firstly, we utilize the projection mechanism of Radviz to map the membership degree matrixes onto planar and radial pictures, in which data points with low membership uncertainty are located near Radviz circumference, while the others are scattered in the center of Radviz circle. To provide an informative interactive visualization, we then improve traditional Radviz visualization in many aspects, including implementing an optimal and uneven placement of dimension anchors by using the Prim algorithm, designing visual codings of data points and dimension arcs to express statistical information, combining chord diagram to depict the sharing relationship between clusters, and offering a set of interactions to support deeper exploration. Finally, we use a case study to illustrate the effectiveness and usefulness of our visualization.

The percentage of individuals frequently using their smartphones in work and life is increasing steadily. The interactions between individuals and their smartphones can produce large amounts of usage data, which contain rich information about smartphone owners usage habits and their daily life. In this paper, a visual analytic tool is proposed to discover and understand individual behavior patterns in smartphone usage data. Four cooperated visualization views and many interactions are provided in this tool to visually explore the temporal features of various interactive events between smartphones and their users, the hierarchical associations among event types, and the detailed distributions of massive event sequences. In the case studies, plenty of interesting patterns are discovered by analyzing the data of two smartphone users with different usage styles.

Subspace-based analysis has increasingly become the preferred method for clustering high-dimensional data. A visually interactive exploration of subspaces and clusters is a cyclic process. Every meaningful discovery will motivate users to re-search subspaces that can provide improved clustering results and reveal the relationships among clusters that can hardly coexist in the original subspaces. However, the combination of dimensions from the original subspaces is not always effective in finding the expected subspaces. In this study, we present an approach that enables users to reconstruct new dimensions from the data projections of subspaces to preserve interesting cluster information. The reconstructed dimensions are included into an analytical workflow with the original dimensions to help users construct target-oriented subspaces which clearly display informative cluster structures. We also provide a visualization tool that assists users in the exploration of subspace clusters by utilizing dimension reconstruction. Several case studies on synthetic and real-world data sets have been performed to prove the effectiveness of our approach. Lastly, further evaluation of the approach has been conducted via expert reviews.

In this article, we present a novel radial visualization of IDS alerts, named IDSPlanet, which helps administrators identify false positives, analyze attack patterns, and understand evolving network conditions. Inspired by celestial bodies, IDSPlanet is composed of Chrono Rings, Alert Continents, and Interactive Core. These components correspond with temporal features of alert types, patterns of behavior in affected hosts, and correlations amongst alert types, attackers and targets. The visualization provides an informative picture for the status of the network. In addition, IDSPlanet offers different interactions and monitoring modes, which allow users to interact with high-interest individuals in detail as well as to explore overall pattern.

Radviz is a radial visualization technique which maps data from multiple dimensional space onto a planar picture. The dimensions placed on the circumference of a circle, called Dimension Anchors (DAs), can be reordered to reveal different patterns in the dataset. Extending the number of dimensions can enhance the flexibility in the placement of the DAs to explore more meaningful visualizations. In this paper, we describe a method which rationally extends a dimension to multiple new dimensions in Radviz. This method first calculates the probability distribution histogram of a dimension. The mean shift algorithm is applied to get centers of probability density to segment the histogram, and then the dimension can be extended according to the number of segments of the histogram. We also suggest using the Dunn's index to find the optimal placement of DAs, so the better effect of visual clustering could be achieved after the dimension expansion in Radviz. Finally, we demonstrate the usability of our approach on visually analysing the iris data and two other datasets.

Network security depends heavily on automated Intrusion Detection Systems (IDS) to sense malicious activities. Unfortunately, IDS often generates both too much raw information and a large number of false positive alerts. Information visualization research has been performed to help users discover and analyze information through visual exploration efficiently. Even with the aid of visualization, identifying the attack patterns and recognizing the false positives from a great number of alerts are still challenges. In this paper, we present a novel visualization framework for IDS alerts that can monitor the network and perceive the overall view of the security situation using radial graph in real-time. The framework utilizes five categories of entropy functions to quantitatively analyze the irregular behavioral patterns, and synthesizes interactions, filtering and drill-down to detect the potential intrusions. In conclusion, we describe how this framework was used to analyze the mini-challenges of the 2011 and 2012 VAST challenge.