Dynamic Network Visualization Research Articles

STEP: Sequence of time-aligned edge plots

We present sequence of time-aligned edge plots (STEP): a sequence- and edge-scalable visualization of dynamic networks and, more broadly, graph ensembles. We construct the graph sequence by ordering the individual graphs based on specific criteria, such as time for dynamic networks. To achieve scalability with respect to long sequences, we partition the sequence into equal-sized subsequences. Each subsequence is represented by a horizontal axis placed between two vertical axes. The horizontal axis depicts the order within the subsequence, while the two vertical axes depict the source and destination vertices. Edges within each subsequence are depicted as segmented lines extending from the source vertices on the left to the destination vertices on the right throughout the entire subsequence, and only the segments corresponding to the sequence members where the edges occur are drawn. By partitioning the sequence, STEP provides an overview of the graphs’ structural changes and avoids aspect ratio distortion. We showcase the utility of STEP for two realistic datasets. Additionally, we evaluate our approach by qualitatively comparing it against three state-of-the-art techniques using synthetic graphs with varying complexities. Furthermore, we evaluate the generalizability of STEP by applying it to a graph ensemble dataset from the architecture domain.

On Network Structural and Temporal Encodings: A Space and Time Odyssey.

The dynamic network visualization design space consists of two major dimensions: network structural and temporal representation. As more techniques are developed and published, a clear need for evaluation and experimental comparisons between them emerges. Most studies explore the temporal dimension and diverse interaction techniques supporting the participants, focusing on a single structural representation. Empirical evidence about performance and preference for different visualization approaches is scattered over different studies, experimental settings, and tasks. This paper aims to comprehensively investigate the dynamic network visualization design space in two evaluations. First, a controlled study assessing participants' response times, accuracy, and preferences for different combinations of network structural and temporal representations on typical dynamic network exploration tasks, with and without the support of standard interaction methods. Second, the best-performing combinations from the first study are enhanced based on participants' feedback and evaluated in a heuristic-based qualitative study with visualization experts on a real-world network. Our results highlight node-link with animation and playback controls as the best-performing combination and the most preferred based on ratings. Matrices achieve similar performance to node-link in the first study but have considerably lower scores in our second evaluation. Similarly, juxtaposition exhibits evident scalability issues in more realistic analysis contexts.

Online Dynamic Network Visualization Based on SIPA Layout Algorithm

Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods.

Analysis of Construction Networks and Structural Characteristics of Pearl River Delta and Surrounding Cities Based on Multiple Connections

The Pearl River Delta (PRD) is one of three world-class city clusters in China, which is important for the strategical deployment of the national “Belt and Road”. Based on nighttime lighting data, Baidu index, and train stopping times, this study analyzed the network of spatial patterns and structural evolution of the PRD and surrounding cities via social network analysis and dynamic network visualization, providing new perspectives and ideas for the study of intercity linkages and urban networks. The results provide decision support to the government for urban cluster planning. From 2014 to 2020, the economic network evolved from a uniaxial structure to an “inverted V” structure. The transportation network evolved from a uniaxial structure to a “△” structure. The information network did not show any obvious structural changes during its development, except for a star-shaped radial structure. The PRD city cluster and its surrounding cities exhibited a spatially non-uniform distribution in terms of spatial connections. The total connections between Guangzhou and Foshan and the surrounding cities in terms of economic, transportation, and information functions account for 30%, 28%, and 10% of the total urban connections, respectively. The graph entropy growth rates of the PRD city cluster and surrounding cities in economic, transportation, and information networks from 2014 to 2020 were 39.9%, 115.4%, and 5.1%, respectively. The network structures of economic and transportation networks stabilized eventually. The information network structures are stable.

Identification of runs of homozygosity in Western honey bees (Apis mellifera) using whole-genome sequencing data.

Runs of homozygosity (ROH) are continuous homozygous segments that arise through the transmission of haplotypes that are identical by descent. The length and distribution of ROH segments provide insights into the genetic diversity of populations and can be associated with selection signatures. Here, we analyzed reconstructed whole-genome queen genotypes, from a pool-seq data experiment including 265 Western honeybee colonies from Apis mellifera mellifera and Apis mellifera carnica. Integrating individual ROH patterns and admixture levels in a dynamic population network visualization allowed us to ascertain major differences between the two subspecies. Within A. m. mellifera, we identified well-defined substructures according to the genetic origin of the queens. Despite the current applied conservation efforts, we pinpointed 79 admixed queens. Genomic inbreeding (F ROH) strongly varied within and between the identified subpopulations. Conserved A. m. mellifera from Switzerland had the highest mean F ROH (3.39%), while queens originating from a conservation area in France, which were also highly admixed, showed significantly lower F ROH (0.45%). The majority of A. m. carnica queens were also highly admixed, except 12 purebred queens with a mean F ROH of 2.33%. Within the breed-specific ROH islands, we identified 14 coding genes for A. m. mellifera and five for A. m. carnica, respectively. Local adaption of A. m. mellifera could be suggested by the identification of genes involved in the response to ultraviolet light (Crh-BP, Uvop) and body size (Hex70a, Hex70b), while the A. m. carnica specific genes Cpr3 and Cpr4 are most likely associated with the lighter striping pattern, a morphological phenotype expected in this subspecies. We demonstrated that queen genotypes derived from pooled workers are useful tool to unravel the population dynamics in A. mellifera and provide fundamental information to conserve native honey bees.

Visual analysis method of foundation pit monitoring information based on BIM platform

In order to visually express the safety monitoring information of foundation pits and underground engineering, realize the efficient management of massive dynamic safety monitoring information during the construction of foundation pits and underground engineering, as well as assist safety analysis, a building information model (BIM)-based Foundation pit and underground engineering dynamic safety monitoring information integration and network visualization method is proposed in the article. Firstly, the research builds a three-dimensional BIM model of foundation pit engineering based on the analysis of the element composition of the three-dimensional virtual scene of the foundation pit and underground engineering. Then, apply the method of key field mapping to establish the dynamic coupling between the BIM model and the safety monitoring information. Finally, the research realizes the virtual display of the construction process based on the three-dimensional visualization function of BIM technology. Furthermore, combined with the actual situation, the corresponding system is developed and applied in a certain project, which realized the visual integration of dynamic information of engineering safety monitoring and BIM under the network environment, and provided a support platform for the safety analysis of foundation pits and underground engineering.

Network Patterns of Zhongyuan Urban Agglomeration in China Based on Baidu Migration Data

As a new product of the Internet and big data era, migration data are of great significance for the revealing of the complex dynamic network patterns of urban agglomerations and for studying the relations between cities by using the “space of flows” model. Based on Baidu migration data of one week in 2021, this paper constructs a 30 × 30 rational data matrix for cities in Zhongyuan Urban Agglomeration and depicts the network pattern from static and dynamic perspectives by using social network analysis and dynamic network visualization. The results show that the network of Zhongyuan Urban Agglomeration is characterized by a circular structure with Zhengzhou as the center, a city belt around Zhengzhou as the connection, subcentral cities as the support and peripheral cities as the extension. Zhengzhou is the core city of the entire network, related to which the central and backbone networks divided in this paper account for nearly 40% of the total migration. Shangqiu, Luoyang, Zhoukou and Handan also play an important role in the structure of the migration network as subcentral cities. For a single city, the migration scale generally peaks on weekends and reaches its minimum during Tuesday to Thursday. Regarding the relations between cities, the migration variation can be divided into four types: peaking on Monday, peaking on weekends, bimodal and stable, and there are obvious phenomena of weekly commuting. In general, the links between cities outside Henan Province and other cities in the urban agglomeration are relatively weak, and the constraints of administrative regionalization on intercity migration are presumed to still exist. According to the results, the location advantage for multi-layer development and construction of Zhongyuan Urban Agglomeration should be made use of. In addition, the status as the core city and the radiation range should be strengthened, and the connections between the peripheral cities and the other cities should be improved, so as to promote the integrated and efficient development of the whole urban agglomeration.

Interactive Visual Graph Analytics on the Web

We present a web-based network visual analytics platform called GraphVis that combines interactive visualizations with analytic techniques to reveal important patterns and insights for sense making, reasoning, and decision-making. The platform is designed with simplicity in mind and allows users to visualize and explore networks in seconds with a simple drag-and-drop of a graph file into the web browser. GraphVis is fast and flexible, web-based, requires no installation, while supporting a wide range of graph formats as well as state-of-the-art visualization and analytic techniques. In particular, the multi-level network analysis engine of GraphVis gives rise to a variety of new possibilities for exploring, analyzing, and understanding complex networks interactively in real-time. Finally, we also highlight other key aspects including filtering, querying, ranking, manipulating, exporting, partitioning (community/role discovery), as well as tools for dynamic network analysis and visualization, interactive graph generators (including two new block model approaches), and a variety of multi-level network analysis and statistical techniques.

Staged Animation Strategies for Online Dynamic Networks.

Dynamic networks-networks that change over time-can be categorized into two types: offline dynamic networks, where all states of the network are known, and online dynamic networks, where only the past states of the network are known. Research on staging animated transitions in dynamic networks has focused more on offline data, where rendering strategies can take into account past and future states of the network. Rendering online dynamic networks is a more challenging problem since it requires a balance between timeliness for monitoring tasks-so that the animations do not lag too far behind the events-and clarity for comprehension tasks-to minimize simultaneous changes that may be difficult to follow. To illustrate the challenges placed by these requirements, we explore three strategies to stage animations for online dynamic networks: time-based, event-based, and a new hybrid approach that we introduce by combining the advantages of the first two. We illustrate the advantages and disadvantages of each strategy in representing low- and high-throughput data and conduct a user study involving monitoring and comprehension of dynamic networks. We also conduct a follow-up, think-aloud study combining monitoring and comprehension with experts in dynamic network visualization. Our findings show that animation staging strategies that emphasize comprehension do better for participant response times and accuracy. However, the notion of "comprehension" is not always clear when it comes to complex changes in highly dynamic networks, requiring some iteration in staging that the hybrid approach affords. Based on our results, we make recommendations for balancing event-based and time-based parameters for our hybrid approach.

Using Low-Dimensional Manifolds to Map Relationships Between Dynamic Brain Networks.

As the field of dynamic brain networks continues to expand, new methods are needed to allow for optimal handling and understanding of this explosion in data. We propose here a novel approach that embeds dynamic brain networks onto a two-dimensional (2D) manifold based on similarities and differences in network organization. Each brain network is represented as a single point on the low dimensional manifold with networks of similar topology being located in close proximity. The rich spatio-temporal information has great potential for visualization, analysis, and interpretation of dynamic brain networks. The fact that each network is represented by a single point makes it possible to switch between the low-dimensional space and the full connectivity of any given brain network. Thus, networks in a specific region of the low-dimensional space can be examined to identify network features, such as the location of brain network hubs or the interconnectivity between brain circuits. In this proof-of-concept manuscript, we show that these low dimensional manifolds contain meaningful information, as they were able to successfully discriminate between cognitive tasks and study populations. This work provides evidence that embedding dynamic brain networks onto low dimensional manifolds has the potential to help us better visualize and understand dynamic brain networks with the hope of gaining a deeper understanding of normal and abnormal brain dynamics.

Analysis and Visualization of Dynamic Networks Using the DyNet App for Cytoscape.

Biological processes are regulated at a cellular level by tightly controlled molecular interaction networks, which are collectively known as the interactome. The interactome is not a static entity, but instead is dynamically reorganized or "rewired" under varying temporal, spatial, and environmental conditions. Most network analysis and visualization tools have, to date, been developed for static representations of molecular interaction data. Here, we describe a protocol that provides a step-by-step guide to DyNet, a Cytoscape 3 application that facilitates the visualization and analysis of dynamic molecular interaction networks. DyNet represents a dynamic network as a set of state graphs that are synchronized in their layout. This synchronization is managed in real time and is automatically updated when a graph is manipulated by a user (e.g., dragging, zooming, moving a node). DyNet also provides several statistical tools enabling users to quickly identify and analyze the most 'rewired' nodes across many network states. © 2018 by John Wiley & Sons, Inc.

MR4Cancer: a web server prioritizing master regulators for cancer

During cancer stage transition, a master regulator (MR) refers to the key gene controlling cancer initiation and progression by orchestrating the associated target genes (termed as its regulon). Due to their inherent importance, MRs can serve as critical biomarkers for cancer diagnosis and prognosis, and therapeutic targets. However, it is challenging to infer key MRs that might explain gene expression profile changes between two groups due to lack of context-specific regulons, whose expression level can collectively reflect the activity of likely MRs. There is also a need to design an easy-to-use tool of MR identification for research community. First, we generated cancer-specific regulons for 26 cancer types by analyzing high-throughput omics data from TCGA, and extracted noncancer-specific regulons from public databases. We subsequently developed a web server MR4Cancer, integrating the regulons with statistical inference to identify and prioritize MRs driving a phenotypic divergence of interest. Based on the input gene list (e.g. differentially expressed genes) or expression profile with two groups, MR4Cancer outputs ranked MRs by enrichment testing against the predefined regulons. Gene Ontology and canonical pathway analyses are also conducted to elucidate the function of likely MRs. Moreover, MR4Cancer provides dynamic network visualization for MR-target relations, and users can interactively interrogate the network to produce new hypotheses and high-quality figures for publication. Finally, the presented case studies highlighted the performance of MR4Cancer. We expect this user-friendly and powerful web tool will provide researchers novel insights into tumorigenesis and therapeutic intervention. http://cis.hku.hk/MR4Cancer. Supplementary data are available at Bioinformatics online.

Interactive Visual Graph Mining and Learning

This article presents a platform for interactive graph mining and relational machine learning called GraphVis. The platform combines interactive visual representations with state-of-the-art graph mining and relational machine learning techniques to aid in revealing important insights quickly as well as learning an appropriate and highly predictive model for a particular task (e.g., classification, link prediction, discovering the roles of nodes, and finding influential nodes). Visual representations and interaction techniques and tools are developed for simple, fast, and intuitive real-time interactive exploration, mining, and modeling of graph data. In particular, we propose techniques for interactive relational learning (e.g., node/link classification), interactive link prediction and weighting, role discovery and community detection, higher-order network analysis (via graphlets, network motifs), among others. GraphVis also allows for the refinement and tuning of graph mining and relational learning methods for specific application domains and constraints via an end-to-end interactive visual analytic pipeline that learns, infers, and provides rapid interactive visualization with immediate feedback at each change/prediction in real-time. Other key aspects include interactive filtering, querying, ranking, manipulating, exporting, as well as tools for dynamic network analysis and visualization, interactive graph generators (including new block model approaches), and a variety of multi-level network analysis techniques.

IDREM: Interactive visualization of dynamic regulatory networks.

The Dynamic Regulatory Events Miner (DREM) software reconstructs dynamic regulatory networks by integrating static protein-DNA interaction data with time series gene expression data. In recent years, several additional types of high-throughput time series data have been profiled when studying biological processes including time series miRNA expression, proteomics, epigenomics and single cell RNA-Seq. Combining all available time series and static datasets in a unified model remains an important challenge and goal. To address this challenge we have developed a new version of DREM termed interactive DREM (iDREM). iDREM provides support for all data types mentioned above and combines them with existing interaction data to reconstruct networks that can lead to novel hypotheses on the function and timing of regulators. Users can interactively visualize and query the resulting model. We showcase the functionality of the new tool by applying it to microglia developmental data from multiple labs.

EOD Edge Sampling for Visualizing Dynamic Network via Massive Sequence View

Dynamic network visualization is crucial to understand network evolving behavior. Massive sequence view (MSV) is a classic technique for visualizing dynamic networks and provides users with a fine-grained presentation of time-varying communication trend from both node pair and global network levels. However, MSV is vulnerable to visual clutter caused by overlapping edges, failing to show clear patterns or trends. This paper presents an edge sampling method, using the edge overlapping degree (EOD) concept, to reduce visual clutter in MSV while preserving the time-varying features of network communication. Referring to accept–reject sampling, we use kernel density estimation to characterize the time-varying features between node pairs and generate EOD probability density functions to accomplish sampling in a bottom-up manner. To enhance the sampling effect, we also consider the edge length factor and streaming processing. The case studies on two dynamic network data sets demonstrate that our method can significantly improve the overall readability of MSV and clearly reveal the temporal features of both node pairs and global network. A quantitative evaluation comparing with two other sampling methods using three real-world data sets indicates that our method can well balance visual clutter reduction and temporal feature preservation.

DyNetViewer: a Cytoscape app for dynamic network construction, analysis and visualization.

The molecular interactions in a cell are varying with time and surrounded environmental cues. The construction and analysis of dynamic molecular networks can elucidate dynamic cellular mechanisms of different biological functions and provide a chance to understand complex diseases at the systems level. Here, we develop DyNetViewer, a Cytoscape application that provides a range of functionalities for the construction, analysis and visualization of dynamic protein-protein interaction networks. The current version of DyNetViewer consists of four different dynamic network construction methods, twelve topological variation analysis methods and four clustering algorithms. Moreover, visualization of different topological variation of nodes and clusters over time enables users to quickly identify the most variations across many network states. DyNetViewer is freely available with tutorials at the Cytoscape (3.4+) App Store (http://apps.cytoscape.org/apps/dynetviewer). limin@mail.csu.edu.cn. Supplementary data are available at Bioinformatics online.

TieVis: visual analytics of evolution of interpersonal ties

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.

TMNVis: Visual analysis of evolution in temporal multivariate network at multiple granularities

Temporal (Dynamic) multivariate networks consist of objects and relationships with a variety of attributes, and the networks change over time. Exploring such kind of networks in visualization is of great significance and full of challenges as its time-varying and multivariate nature. Most of the existing dynamic network visualization techniques focus on the topological structure evolution lacking of exploration on the multivariate data (multiple attributes) thoroughly, and do not cover comprehensive analyses on multiple granularities. In this paper, we propose TMNVis, an interactive visualization system to explore the evolution of temporal multivariate network. Firstly we list a series of tasks on three granularities: global level, subgroup level and individual level. Secondly three main views, which rely mainly on timeline-based method while animation subsidiary, are designed to resolve the analysis tasks. Thirdly we design a series of flexible interactions and develop a prototype system. At last we verify the effectiveness and usefulness of TMNVis using a real-world academic collaboration data.

DyNet: visualization and analysis of dynamic molecular interaction networks.

Summary: The ability to experimentally determine molecular interactions on an almost proteome-wide scale under different conditions is enabling researchers to move from static to dynamic network analysis, uncovering new insights into how interaction networks are physically rewired in response to different stimuli and in disease. Dynamic interaction data presents a special challenge in network biology. Here, we present DyNet, a Cytoscape application that provides a range of functionalities for the visualization, real-time synchronization and analysis of large multi-state dynamic molecular interaction networks enabling users to quickly identify and analyze the most ‘rewired’ nodes across many network states.Availability and Implementation: DyNet is available at the Cytoscape (3.2+) App Store (http://apps.cytoscape.org/apps/dynet).Contact: david.lynn@sahmri.com.Supplementary Information: Supplementary data are available at Bioinformatics online.

Characterizing mutation–expression network relationships in multiple cancers

BackgroundData made available through large cancer consortia like The Cancer Genome Atlas make for a rich source of information to be studied across and between cancers. In recent years, network approaches have been applied to such data in uncovering the complex interrelationships between mutational and expression profiles, but lack direct testing for expression changes via mutation. In this pan-cancer study we analyze mutation and gene expression information in an integrative manner by considering the networks generated by testing for differences in expression in direct association with specific mutations. We relate our findings among the 19 cancers examined to identify commonalities and differences as well as their characteristics. ResultsUsing somatic mutation and gene expression information across 19 cancers, we generated mutation–expression networks per cancer. On evaluation we found that our generated networks were significantly enriched for known cancer-related genes, such as skin cutaneous melanoma (p<0.01 using Network of Cancer Genes 4.0). Our framework identified that while different cancers contained commonly mutated genes, there was little concordance between associated gene expression changes among cancers. Comparison between cancers showed a greater overlap of network nodes for cancers with higher overall non-silent mutation load, compared to those with a lower overall non-silent mutation load. ConclusionsThis study offers a framework that explores network information through co-analysis of somatic mutations and gene expression profiles. Our pan-cancer application of this approach suggests that while mutations are frequently common among cancer types, the impact they have on the surrounding networks via gene expression changes varies. Despite this finding, there are some cancers for which mutation-associated network behaviour appears to be similar: suggesting a potential framework for uncovering related cancers for which similar therapeutic strategies may be applicable. Our framework for understanding relationships among cancers has been integrated into an interactive R Shiny application, PAn Cancer Mutation Expression Networks (PACMEN), containing dynamic and static network visualization of the mutation–expression networks. PACMEN also features tools for further examination of network topology characteristics among cancers.