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yWorks releases GraphMLViewer 1.6, the freely available Flash®-based viewer for GraphML files!

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Case Study: Intelligent Data Provision in the Automotive and Rail Industries

VIRTUAL VEHICLEResearch CenterAutomotive and Railresearch & developmentResearch & development in the automotive and rail industries is shaped by renowned international project partners with a multitude of data from various sources and structures. In a research project, VIRTUAL VEHICLE develops a prototypical digital decision-making basis: a graph database for data collection and an application for visualizing and navigating the connected data. How Austrian researchers are rethinking automotive R&D using data visualization and yFilesNew topics like e-mobility and autonomous driving present automotive companies with new challenges, particularly in the area of research and development. For example, the cycles of model development are expected to change. These cycles currently last up to eight years and will become significantly shorter in the future, especially in terms of software and hardware. Source: VIRTUAL VEHICLETo meet these future demands, research & development is rapidly moving in the direction of virtualization and digitization – away from classical experiments on prototypes, and towards numerical methods. This evolution requires new development environments with agile processes and the appropriate methods and tools. For this reason, the Efficient Development division of VIRTUAL VEHICLE Research Center in Graz, Austria - one of the leading research centers for automotive and rail industries - focuses on a functional system view, orchestration of complex product verification, and advanced information and process management. VIRTUAL VEHICLE, one of the leading R&D centers for the automotive and rail industries, is based in Graz, Austria. Future Engineering LabSource: VIRTUAL VEHICLEThe Future Engineering Lab project team is working with renowned project partners from the automotive and rail industries to develop a prototype implementation of future data provision and consistency in engineering, along the product lifecycle. Their approach is to collect the heterogeneous data sources, and then develop aggregating and view-based access to them. By virtually merging the metadata in a graph database, the team frees itself from predefined data structures (as is common in relational database systems). As a result, they can concentrate on the relationships between the data, and integrate additional data sources as needed. The researchers develop a web application whose core is data visualization. An interactive graph makes it easy to navigate through the data and serves as a digital decision-making basis for development engineers. The data is stored in native graph format in a NEO4j database.Source: VIRTUAL VEHICLEVisualizing the dataThe requirements for the visualization solution were: potential future integration into other tools and platforms, intuitive visualization of large linked data sets, the option of expanding and collapsing data points, suitable structuring of the visualization, and a high degree of freedom in realizing the project and designing user interactions. After a lengthy evaluation of commercial as well as freely available visualization tools, the decision was made in favor of yFiles for HTML, by yWorks GmbH. Dr. Bernd Fachbach, project lead“The flexibility in designing our own application, as well as the many existing, relevant implementation examples led to our decision to use yFiles.”Source: Dr. Bernd FachbachThe developed test model serves as a prototype of future data provision and consistency in engineering, along the product lifecycle. The prototype is intended as a future-proof model: a web application that allows exploration and navigation in context-based engineering information. Initial results will be published in summer 2019. VIRTUAL VEHICLE | Kompetenzzentrum – Das Virtuelle Fahrzeug Forschungsgesellschaft mbHVIRTUAL VEHICLE is a leading international research and development center for the automotive and rail industries, located in Graz, Austria. The center focuses on the consistent virtualization of vehicle development. An essential element is the linking of numerical simulations and hardware testing, which ensures a comprehensive hardware-software system design. VIRTUAL VEHICLE’s international partner network consists of:80+ international industrial partners40+ international scientific institutions (including TU Graz, TU Berlin, Fraunhofer)VIRTUAL VEHICLE is the largest COMET-funded research center and is also active in over 30 EU projects. In addition, VIRTUAL VEHICLE offers a broad portfolio of contract research for vehicle development.Download Case StudyFurther Sources:PWC Studie Automotive Eascyhttps://www.pwc.de/de/automobilindustrie/pwc_automotive_eascy-studie.pdf

yWorks releases version 2.2 of yFiles for HTML!

With the new 2.2 release of yFiles for HTML yWorks again proves to be the leading provider of professional high-quality graph diagramming software libraries. The new release contains new features, unmatched in the industry, that will both delight existing customers and will open the product to a wider audience range.In this post I want to talk about some of the highlights that we included in the new 2.2 release. Since the focus is mostly on new features, for those looking for a more general introduction to the library and its capabilities, we suggest to take a look at the features page, instead.We always make sure that any major new release brings improvements and new features to all areas of the product: There are less visible improvements that developers will benefit from, but also end-user visible features like improvements to automatic layout, and several improvements relating to the visual and interactive API parts. Still the API has remained stable and mostly backwards compatible to older releases. Both, developers new to yFiles and existing applications can benefit from the new release.Improved and New Demos Of course, we also improved and reworked many of the demos and also added some more for you to get inspired from. Here are some of the new demos and feature updates: Improvements for Automatic Layout Of course, the 2.2 release contains some features for our most flexible and most widely used graph layout algorithm for hierarchically structured graphs. But there are also nice improvements to the generic edge routing algorithm and the generic tree layout styles. Hierarchic Layout Improvements For the very wide layouts in flat hierarchies, there is now a perfect option to produce more compact bus-like drawings for the successors and predecessors of specific elements. As can be seen in the example, such an arrangement is especially suitable for star-like substructures (i.e., a node connected to multiple nodes with degree one) which can otherwise consume a lot of space and make the layout less compact. The new layout results in better legibility for the labels and makes better use of the screen real-estate in typical use-cases. Edge Router Improvements The generic edge routing algorithm can be used in those situations where the locations of the nodes are fixed and where there is no layout algorithm that does not perform an integrated edge routing along with the arrangement of the nodes. For the edge routing algorithm, two major new features have been added to the release:Integrated Labeling Integrated edge labeling allows the algorithm to choose edge routes that consider one or more labels and their size of an edge. This ensures perfect legibility of even longer labels. If necessary, the edge will be routed using a different path so that it passes through areas that can hold the label's visualization without producing overlaps.Support for Bus Structures In addition, we added support for bus structures to the generic edge routing algorithm. The routing of such buses is like the results produced by the bus routing algorithm and combines the routes of multiple edges that form a bus. Also, the edge routing algorithm is able to optionally consider user-specified bus locations and provides more options for solving other constraints.More Tree Layout Variants Our versatile tree arrangement algorithm received a number of new arrangement strategies. The first one supports the distribution of nodes into multiple branches (a kind of vertical bus) that are placed next to each other. Of course, any orientation and distribution is supported by the implementation and the strategy can be applied to parts of the diagram, only. The second new strategy is based on the existing grid placement facility and allows to create a symmetric bus-style featuring multiple rows or columns respectively.RUN DEMOView and Interaction There are many smaller and incremental improvements for the view and interaction part in yFiles for HTML 2.2. We both increased performance in many areas of the visualization and added more customization options for the developers to choose from.More Built-in WebGL Rendering OptionsWebGL had been added to yFiles for HTML with the 2.1 release as an optional third rendering technology, next to SVG and HTML5 Canvas. The 2.2 release now provides more WebGL visualization options out of the box. For the visualization of very large networks, the overview visualization, the grid visualization, and the visualization of the default manipulation handles can now be rendered using either SVG, Canvas, or WebGL, bringing together the strengths of the three technologies in a single diagram visualization.Hide DescriptionOpen in yEd LiveDownloadAdvanced CSS Styling and AnimationFor the 2.2 release we made it significantly easier for the developer to change the default color schemes and the animations for the various controls in the diagram: Tooltips and the integrated label text editor can now open and close using sophisticated CSS3 animations with minimal effort. The coloring and styling of handles, selection, focus, and highlight indicators, as well as the selection marquee is now as easy as changing a line of CSS code.RUN DEMODeveloper Highlights With every release we improve the productivity of the developers working with our software library. This means not only adjusting to new trends in the web development world, but also improving the way our APIs work and how the huge amount of features our libraries offer can be combined by software developers to create new, fascinating end-user applications.ECMAScript Modules Loading The last release paved the way for ECMAScript module loading , and it has proved to be a blessing for modern web development and the support in web development IDEs has improved dramatically for this technology. In this release we switched the complete documentation to use the more concise syntax throughout the code, samples, tutorials, and demos. All demos and existing code snippets have been rewritten and refactored to use ES modules, and we added tools that help with the automatic migration from older module loading systems to ES module loading and the other way around.import { GraphComponent, GraphEditorInputMode, ShapeNodeStyle, HierarchicLayout, } from 'yfiles' const graphComponent = new GraphComponent('#graphComponent') graphComponent.inputMode = new GraphEditorInputMode() const graph = graphComponent.graph const blueNodeStyle = new ShapeNodeStyle({ fill: 'blue', shape: 'ellipse'}) graph.nodeDefaults.style = blueNodeStyle graphComponent.morphLayout(new HierarchicLayout()) yFiles for HTML as a Node Module With the growing popularity of CLI tools like create-react-app , Angular CLI , Vue CLI , and other webpack and rollup.js based tools, the support for node and npm modules also improved a lot with the various JavaScript and TypeScript IDEs. yFiles for HTML 2.2 can now be bundled as an npm module, making integration into a large amount of build systems as easy as writing npm install --save yfiles. Currently, yFiles for HTML is not available on any public NPM repository and instead needs to be installed once, in a private repository, first.Integrating yFiles for HTML into a build tool-chain becomes almost trivial with the node module support Setting up a new project now barely requires any configuration steps anymore and code completion, build-optimization, and source code transpilers work out of the box with both TypeScript and JavaScript projects.{ "description": "My custom yFiles for HTML App", "private": true, "name": "yfiles-diagramming-app", "version": "1.0.0", "scripts": { "production": "webpack --mode production", "dev": "webpack --mode development", "start": "webpack-dev-server --mode development --open" }, "devDependencies": { "@yworks/optimizer": "^1.0.0", "webpack": "^4.22.0", "webpack-cli": "^3.1.2", "webpack-dev-server": "^3.1.10", "babel-loader": "^8.0.5", "@babel/core": "^7.4.0", "@babel/preset-env": "^7.4.2" }, "dependencies": { "yfiles": "git+https://yguy@repository.internal/npm/yfiles.git", "@babel/polyfill": "^7.4.3" } } Starting with this release we provide a webpack plugin that automatically optimizes, obfuscates, and minifies the yFiles library in production mode. We also worked hard on making the library more resilient against overly-aggressive tree shaking implementations. The chances that a buggy optimization tool-chain now breaks the functionality have been reduced and unused yFiles modules are automatically stripped from the build. We restructured the ES-modules so that now every type is available from a top-level yfiles module, making it much easier to use the available functionality.Hundreds of New Code SamplesWe worked hard to ensure that the most important parts of our API now contain code snippets and example usage code that is accessible right from the API browser and via code completion in the IDEs. More than 600 new code snippets have been added to the documentation, totaling in more than one thousand code snippets.SHOW APIFor the new release more than 1000 code snippets and examples have been added or rewritten We also updated our YouTube Playlist for Developers, of course, to feature the newest versions of APIs and tool-chains.Enhanced Collection APIGraphs are all about data and working with the elements in the data structures has always been key to the API. All collections of elements in the yFiles data structures (lists, sets, maps, collections of nodes, edges, ports, labels, bends, adjacency lists, etc.) now support additional functionality to perform frequent tasks like grouping, filtering, sorting, mapping, reducing, flattening, projecting, combining, etc. with a concise and efficient API:const widestNodes = graphComponent.graph.nodes // group nodes by their width .groupBy(n => n.layout.width, (width, items) => ({ width, items })) // and order the groups by their size .orderByDescending(entry => entry.items.size) // consider the largest two groups only .take(2) // and take all their items .flatMap(entry => entry.items) Stream-lined Graph Algorithm APIyFiles always provided the broadest range of efficient graph analysis algorithm implementations for the web platform. But the API wasn't as easy to use as the layout API and parts of it required advanced algorithm knowledge. The new release contains a completely new facade to the existing algorithms, making configuration, execution, and result interpretation consistent and simple. The new API makes it easy for developers to leverage the power of graph analysis algorithms for their visualization, layout, interaction, and analysis purposes.// configure the shortest path algorithm const algorithm = new SingleSourceShortestPaths({ // single source source: startNode, // add edge cost mapping which returns the actual length of the edge costs: edge => edge.style.renderer .getPathGeometry(edge, edge.style) .getPath() .getLength() }) // run the algorithm: // calculate paths from startNode to all nodes in the graph const result = algorithm.run(graph) // for each end node for (const targetNode of targetNodes) { // set its distance to the startNode as label text graph.setLabelText(targetNode.labels.get(0), `${result.getPathTo(targetNode).distance}`) // and mark the edge path from start to the end node for (const edge of result.getPathTo(targetNode).edges) { graph.setStyle(edge, highlightPathStyle) } } The new API makes it easy for developers to leverage the power of graph analysis algorithms If you haven't done so, go ahead and evaluate yFiles for HTML, today! Happy diagramming!EVALUATE NOW

yFiles and Neo4j: Custom Visualization Solutions

Neo4j is great for storing and processing large amounts of connected data. For example, in web analytics, where click-paths and custom events get logged, it is easy to get that data into Neo4j. Once the data is in the graph database, complex queries may be executed that help both site administrators and site owners get a good understanding of how their users and visitors use the website or ecommerce store.Sure, the results are used to improve the predictions of recommendation engines. Gut with that knowledge, shop architects can also then optimize the structure of their sites and further optimize customer experiences.However, one aspect that is often overlooked is how the visualization of graphs helps in understanding the query results and the automatic decisions of recommendation engines, as well as in planning for optimizations of customer journeys.The Neo4j browser is an invaluable tool for experimenting with Cypher queries and refactoring the schema of your database contents. The developer tool also comes with a visualization component that displays query results in a graphical manner.The simple, low-level interface is great for developers, but less technically inclined end-users prefer less-generic, highly-customized solutions that suit the exact requirements of their specific use-cases.There are several aspects that need to be considered to get the most out of data visualization and the best end-user experience:Rich VisualizationBeing able to visualize connected items and their properties in diagrams helps a lot in understanding the data and processes. For the best user-experience rich element visualizations should be used. Many generic solutions provide a master/detail view of the graph, where mainly the structure of the visualization is presented in one part of the screen. The details behind each of the elements are seen in a separate, mostly textual detail window beside the main visualization. This quickly becomes difficult to use once the diagram is explored in more detail. Users have to manually select each of the elements one by one to find more information about the element. Exporting such a diagram for reports becomes almost impossible, because the detail information is missing from the visualization or is only available for a single element. With rich item visualizations, more information can be put into the diagram and included in the visualization for every element on the screen or later in the report. For example, in a customer journey diagram, not only the name or ID of the page could be shown in the visualization, but also numeric data like “time spent on page,” “drop-off-rate,” “average page value” and “number of visitors.” These values can be added to the visualization of each element. They don’t need to be represented as naked numbers either, but data can be implemented via color-coding, gauges, level-meters, varying sizes and so on. Of course, auxiliary textual data is displayed just as well as one or more badges that indicate a certain state of the page or event (“high-performer,” “needs-attention,” “data-anomaly”). These techniques result in drastic improvements in the user experience. Just like in Neo4j, where different node labels depict a different type of entity in the database, the visualization should make it easy for the user to tell the difference between the different kinds of elements. Not all elements need to follow the same visualization scheme. Instead, the visualization should make it easy for the user to recognize which types of elements are visualized.Sometimes there is a problem with having many details shown inside the visualization: If the number of elements in the diagram or on the screen gets large – if the user needs to zoom out of the diagram to understand the bigger picture – that additional information can become distracting and make the graph impossible to decipher. In this case, sophisticated visualization solutions can use a technique called “level of detail rendering.” Depending on the number of elements on the screen, or the zoom level, element visualizations of varying detail can be employed. Once the user zooms into the diagram, more information becomes visible. If the visualization permits, switching between those levels can be animated, resulting in smooth transitions between the various levels of detail. Arranging Elements MeaningfullyAnother aspect to diagram visualization, that’s just as important as how to render the elements on the screen, is where to place those elements. For many applications, the location of the elements in the diagram can encode additional information for the user right into the visualization. Simple solutions only try to reduce the distance between elements that are interconnected by relations. More advanced placement algorithms can encode additional information into the layout of the diagram. That information could be importance or a total ordering of elements. More important elements could be rendered on top of the drawing. Or, if there is a flow of data or goods, a common main direction of the connections in the diagram helps a lot in understanding the flow through the data.But there are more subtle things than flow and order. Alignment and different connection routing styles help just the same – not all relationships must be visualized by an arrow pointing from one element to another. Especially for hierarchically nested structures and relationships, using container visualizations or partitions in the background often are the better choice and lead to less cluttered, more enjoyable diagrams. Sometimes there isn’t a single aspect or layout that suits the application domain, and sometimes the diagrams’ contents change over time. For these scenarios, it’s important to have versatile layout algorithms, as well as smooth transitions between the various visualizations. The latter helps the users keep their mental maps of the diagram data. Watching the animations is a pleasure, too.Interaction and AnimationFinally, interactivity is king when it comes to complex visualizations. If you can give the user the option to interact with the visualization, they will not only enjoy navigating and exploring the database; they will be able to do this a lot more efficiently, too.This is not just about being able to pan the drawing area. Even for the simple use-case of exploration, things like mouse-hovers, highlights, tool-tips and context menus, contextual filters, guided view port animations, etc. can improve the user-experience dramatically. A Perfect Match: Neo4j and yFilesNeo4j is the right tool to store and query your data, and the built-in browser is great for developers. For end-user facing applications, more elaborate visualization solutions should be used. For connected data, the graph visualization library yFiles is a perfect match, since it provides all the possibilities described above.Here’s how yFiles is employed in the above scenarios:With yFiles, you don’t need to just render the raw data in the database. With software-defined mappings you can show more useful abstractions of the data stored in Neo4j.Create perfect item visualizations, specific to the data that will be displayed on the screen. Level of detail rendering and custom reactive displays of the data result in easy-to-understand-and-follow diagrams and visualizations.With the right layout, the structure of the data and their dependencies and relationships can be highlighted. Clever edge-routing and labeling algorithms ensure that no information is hidden behind other items. This removes the need for the user to manually untangle the diagram. The yFiles library comes with the most complete set of diverse automatic layout algorithms for you to play with and choose from.Interactions and built-in animations help the user understand and navigate the diagram more easily and increase the user-experience.You can add user interactions and the ability to edit both the structure and the properties of the diagram. Let interactions trigger updates in the Neo4j database or in external systems.Embed the visualizations into new or existing end-user applications as a white-label solution. Create standalone applications or integrate the functionality into larger dashboards and tools. It is important to understand that yFiles is much more than an off-the-shelf application with only limited customization options. yFiles is a powerful tool for developers to create applications that meet even the most advanced requirements. Explore the demos and download yFiles today and enjoy the endless possibilities you get when combining it with the power of graph databases like Neo4j. Happy diagramming!

‘Game of Thrones’ Relationship Graph

Every year, the Graph Drawing conference organizes a contest which sets the task to visualize predetermined graph data. This year, the data consists of a graph that presents the relationships between 84 characters from the popular TV series ‘Game of Thrones’. Since our yFiles diagramming library is designed to arrange and visualize graph structures, we decided to participate in the contest. The image above shows a preview of our contribution. Read on if you want to find out how we created it or skip to the final result.Getting to know the dataFirst, we used yFiles to quickly parse the provided GraphML file into a graph in which each node represents a character and each edge between two characters indicates a specific relationship.Hide DescriptionOpen in yEd LiveDownloadApplying different yFiles layout algorithms to the graph provided insight into the overall structure of the connections. However, the data contains more information which is not depicted in these standard views:The full name of each character, its affiliation to a house, family, or group, and whether they are alive or deceased.The type of relationship; this can be a killing, an allegiance, or a family tie. In the latter case, the type further differentiates between spouses, parents, siblings, and lovers.In addition some parts of the graph are tightly connected which surely makes it difficult to follow the paths of the connections.Introducing structureSo how do we restructure the data such that a more structured arrangement can be achieved?Since the story of the TV series is all about different families and groups which are competing for power, we decided to divide the characters by their affiliation to the different houses/families.We introduced new nodes that represent the houses and connected every member of a house to the respective house node. Allegiances and family relationships between members of two different houses are redirected to the house nodes to keep them from spanning across the whole graph. Furthermore, we sized the nodes according to the number of allegiance edges to which they were incident initially. This makes more important characters easily recognizable.Graph layoutThe next step was to actually find a good way to arrange the data. We implemented a custom layout algorithm on top ofthe available algorithms from the yFiles diagramming library.To emphasize the affiliation of the characters to the houses, we made the algorithm place the house nodes in a circle and arrange the members of a house in the according sector.The families get placed on concentric circles around the center of the inner circle. Children are placed further outside than their parents while we tried to set spouses and lovers on the same circle. Characters that are associated to just one house, like servants, dragons or direwolves, are also assigned to the respective house.There are two characters (High Sparrow, Night King) located outside the sectors because they do not belong to any house and are only connected through a kill.There are three types of edges: family ties, allegiances, and killings. We used different edge routing algorithms for those edges so they became easily distinguishable. Edges that describe family relations or allegiances are straight lines when they appear inside a house sector. Inside the inner circle they get routed in arcs whose thicknesses represent the number of edges between two houses.Finally, the kill edges were quite a challenge. They connect characters from all parts of the diagram and there are many of them. After some experiments, we decided that they must run outside the inner circle. They were implemented as two straight segments and an arc. This way they can easily be distinguished from the other types of connections. Of course, we tried to minimize the number of crossings to make it easier to follow the edges.Styling the diagramCreating a clear arrangement of the characters and routing the connections between them was the first important step. But a diagram where nodes are only simple, colored shapes and edges are black lines with an arc is not really an eye-catcher. Using yFiles with its many styling options we were sure it would be easy and fun to change that situation. The nodes on the inner circle carry the emblem of the house or group they represent. They mark the sector as well as the end points for the inner edges.Characters are visualized using a picture of a person or animal along with their name. The border shows the color ofthe house to which the character belongs by birth.Since there are three different kinds of edges, we decided to give them different colors. Edges that represent family relationships are drawn in the color of the family’s house. In case those edges connect to two house nodes, theychange color in the middle. Allegiance edges are drawn using dark blue, dotted lines, independent of where the edge is located.As previously mentioned, kill edges are routed in different style. To separate them even more from the other edges, they have a dark red color. Their direction, i.e. who killed who, is indicated by port icons. The source is marked with a bloody knife while at the target a skull is shown.The final resultBelow you find the final diagram created by our algorithm which combines all described features.So now that we have a concise layout of the data and also a nice visualization of the diagram elements, what can we learn from the visualization? We think that the diagram provides a lot of information at a glance. Important characters can easily be recognized by their size and by the amount of relations they have. The large, most important houses immediately stand out.Even though someone who is very familiar with ‘Game of Thrones’ will probably not learn something totally new, our diagram makes it very easy and efficient to get an overview over the involved groups and characters.How do you like the results? Let us know! Just leave us a message in the social media or using our contact information.

Automatic Metro Map Generation

Metro maps are a very important tool when it comes to illustrating routes of a public transport system. The history of metro maps or, more generally, transport maps, goes back to the famous 'Tube Map' created by Harry Beck in 1931 which shows the London Underground. Nowadays they exist for almost every larger urban area, making it easy for passengers to quickly gain an overview over the possible routes and reachable stations. Simply put, metro maps consist of stations or stops which are connected by lines. So we thought that they are a perfect match for the visualization using our yFiles diagramming library. Our goal was to automatically generate maps from data retrieved online and to polish the results using styles similar to the ones found in official maps. As the computation of a new metro map layout is quite CPU intensive, the goal was not to create maps in real time.The automatic computation of metro maps is very CPU intensive.Getting dataFirst, we needed to get the transport line data. We solved this by using OpenStreetMap and overpass-turbo which uses the Overpass-API to query data from OpenStreetMap. It allowed us to quickly and automatically query data for public transport systems of any desired area, parse it, and create a yFiles diagram from it. What we got is basically a diagram that shows the transport system in a geographically precise way:Hide DescriptionOpen in yEd LiveDownloadModeling and solving the problemOur maps should satisfy a few general visualization rules, shared by most of the official maps, too. The lines should only be either horizontal, vertical, or diagonal at 45 degrees. Such a line layout is referred to as octilinear. Metro maps must preserve the main geographic character, like the relative location of stations, but they do not need not to be geographically precise. Generating metro maps following these rules is a rather well-researched topic. Our solution follows the approach from Martin Nöllenburg ('Automated Drawings of Metro Maps', 2005). Shortly put, the approach models the problem using a mixed-integer linear program (MIP). The solution of the problem yields the coordinates of the stations.Metro maps preserve the main geographic character, but do not need to be geographically precise.In our implementation we mainly created constraints as proposed by Nöllenburg and applied an MIP solver (via API) to get a solution to the problem.We tried both lpsolve and the CPLEX Optimizer. The latter provided us with good results a lot quicker than the former. The solution of the MIP program is then interpreted and transferred to the yFiles diagram created in the first step, yielding a metro map layout. To improve the runtime and results we applied some additional heuristics, too.Visualizing the resultsNow we already had a metro map drawing, but we also wanted to make it look great. Here, our powerful yFiles visualization API came into play and made it fun to style the metro map diagram. For the lines we use the colors from the OpenStreetMap data, if available. Station nodes are automatically drawn smaller or larger, depending on the number of adjacent metro lines.The last challenge was the labeling of the metro stations (modeled as node labels). This is quite a difficult problem by itself, because in larger maps, there is often little space available for sometimes long label texts. With the help of the generic labeling algorithm (available out-of-the-box from the layout library) this challenge became manageable. To make results more similar to real-world maps, we tried to place labels as uniformly as possible and automatically used word-wrapping if there is little space available for a label.Labeling of the metro stations was a challenge, but easy with yFiles. Looking at our final results, we think that the maps can already keep up with some of the official metro maps! And clearly, the results of our automatic approach could be the base for a designer to further fine-tune the maps in some aspects. But have a look yourself: below you will find some of our results. Click the images to see the complete gallery of scrollable and zoomable high-quality vector graphics metro maps for more than a dozen cities!What do you think? Is this something we should include as an algorithm in yFiles? Let us know! Just leave us a message in the social media or using our contact form. The complete algorithm we implemented here is not currently part of the yFiles library. The labeling and visualization part is included in yFiles, but the linear programming part currently depends on third party software. If you are interested in using the algorithm for your own diagrams, please contact our support team for options.