Graph Viz 101: a blog post series on the visual exploration of graphs

We are launching a series of posts to teach the basics of graph visualization, written by Sébastien Heymann in collaboration with Bénédicte Le Grand of Université de Paris 1. At Linkurious we are working on better software to help people visualize graphs easily. Of course writing software is a powerful way to improve our ability to tackle complexity but it’s no substitute for human intelligence. With that in mind we are starting a series of posts that will teach you how to create, read, and interpret graphs visually. Taking their roots in the Königsberg Bridge Problem, graphs are meant to be seen.

Illustration of a graph in Linkurious

We have noticed in the recent years a tremendous adoption of graphs outside the scientific community, notably thanks to open source products like Neo4j and Gephi. Graphs offer a powerful tool to think, to design and to create new products adapted to our complex world.

However like any new tool we need to learn how to use it properly, and mastering the art of graph visualization and interpretation takes time. I am still horrified when I hear that graph visualization is “just a toy” or a “nice-to-have”. Quite the opposite, when visualization is correctly and wisely used, it becomes a very efficient medium between you and your data, between your point of view and the ones of your team, between your insights and your audience. Think about it as a surface that you can shape in many ways: layouts, colors, sizes, shapes of nodes and relationships, etc.

Such freedom is necessary to experiment, but it also comes inevitably with bad visualizations and fanciful interpretations. They are rarely deliberate thought, and during my other activities (as Gephi community manager and PhD student) I’ve seen many mistakes due to a lack of basic knowledge or skills. Beware also of histograms and “simpler” graphics: misleading charts are everywhere. I am personally attached to democratize network thinking, so at Linkurious we want to help raise the bar.

But there is a problem: where to start learning graph visualization? Whereas an extensive literature has been produced so far, one must compile various sources after hours of Web search, and try to extract what is important and what is not, what is meaningful and what is esoteric technique. I’ve read dozens of scientific articles and a few books, I’ve closely looked at what people do with Gephi, but I’ve not found a complete yet simple introduction to the visual exploration of graphs (aka networks), even if one can find good introductions to Social Network Analysis. So I’ve created a live tutorial two years ago for the ICWSM conference, and recently worked on a book chapter. It was two great experiences, but the audiences remain limited.

So today I’m excited to announce that we will start Graph Viz 101, a series of 10 blog posts about the theories related to visual graph exploration, in a concise, accessible style. It will help you understand our approach with Linkurious, but more importantly it will give you an in-depth perspective of the classical methods and challenges in the field. It will eventually help you formulate your needs, distinguish good vs bad visualizations, and avoid some common mistakes. Here is the plan (which may be modified later):

  1. Why Exploratory Network Analysis?
  2. Perceptual support of visualization
  3. Emergence of knowledge through visualization
  4. Visual representation of graphs
  5. A visual language of node-link diagrams
  6. The non-linear data processing chain
  7. Interaction and data mining algorithms
  8. Challenges: time-varying graphs and large graphs
  9. The global approach
  10. The local approach (where Linkurious stands)

Should you read our Graph Viz 101? The answer is YES, because I am sure that you will discover something new whatever your skills, unless (maybe) if you are already publishing research articles in this field. You will not find this content anywhere else thought: graph visualization is at the crossing of many fields of research, and I have literally spend months curating existing papers to assemble what I consider to be the most important approaches in these fields (e.g. infovis, psychology, design, data mining, and even a bit of epistemology). This presentation is original as it results from my experience and point of view, so I hope you will enjoy it and discuss it using the blog comments. I would love to see complementary discussions that help go deeper.

As a small gift before we start, don’t miss out the Subtleties / of / Color, 3 well-written blog posts by Robert Simmon that introduces color theory for data visualization.

If you don’t want to miss the Graph Viz 101 series, I recommend you subscribe to the email alerts below (you can unsubscribe any time), or follow us on your favorite social network: Twitter, LinkedIn, Google+, Facebook. Help us spread it to see everyone making better and useful graph visualizations!

Sébastien

We’ll be talking about graph visualization at GraphConnect in San Francisco

Linkurious will be present for the GraphConnect 2013 San Francisco conference, taking place on October 3-4. Jean will be talking about how to search, explore and visualize Neo4j data with Linkurious.

graphconnect-sf

Graphs are hard to understand even for those who work with them. Whether you’re developing an application or looking for answers in your data, this can be a problem. With Linkurious you can now explore in a simple way the data you store in Neo4j. The presentation will focus on how everyone can use it to solve common problems like correcting errors, identifying patterns or finding and communicating insights.

We are particularly proud to be part of GraphConnect. It is the only conference focusing on the rapidly growing world of graph databases and the applications that are making sense of connected data. The presentations cover various use cases for graphs and are a great way to learn more about this developing topic. This edition will feature presentations by Emil Eifrem, CEO, Jim Webber, Chief Scientist, and Ian Robinson, Director of Customer Success at Neo Technology. Additional talks at the conference will come from other graph thought leaders, enthusiasts, and customers. The complete list of speakers can be found here.

You can register here for the conference.

If you are interested in graph visualization, we hope to see you in San Francisco in October. Just give us a shout-out on Twitter or via email, we’d be happy to meet during the conference or in the following days!

 

Officially launching Linkurious, a graph visualization solution for Neo4j

A few months after launching our beta, we are proud to announce that we are releasing the first version of Linkurious. We hope it will help you manage and explore your graph data easily. It reflects the feedback we received from our beta users and first customers, the graph community and our vision of democratizing graph data. You can get a copy here.

Linkurious helps Neo4j users explore their data to find valuable insights

The complex connections within graph data are hard to understand and communicate. Linkurious offers a solution to make it easy to explore and interact with graph data. In minutes it gives you the possibility to search and visualize the data stored in a Neo4j database. Now you can quickly find the answer to questions like “who is this person connected to?” or “is there something abnormal in the pattern of this transaction?”. Whether you need to find answers, understand relationships, track errors or  look for patterns, Linkurious is the easiest way to explore your graph data.

For those for are not using Neo4j, we strongly encourage you to check it out : it’s a great way to store and access graph data. It has an open-source edition that you can install in a matter of minutes.

Here is how Linkurious can help you leverage your graph data :

  • a simple search experience : with a search bar and the possibility to explore  connections by simply clicking, it is very easy to dive right into the data;
  • for the more complicated searches, you can use Cypher;
  • you can add, update and remove nodes or relationships;
  • filter out the noise : the search results show you the part of the graph that is relevant to you, in addition you can filter the data to focus on what’s important

You can try all this via our online demo. We believe our technology will make it easy for everyone to extract insights from graph databases”. To learn more about this you can attend a webinar called “6 degrees of Kevin Bacon: How to use Linkurious to explore and visualize graphs” next week. It will show how, just like the small world of Hollywood movies, the graph data you store is a mine of information waiting to be used.

Start exploring your data

Linkurious is easy to install and works out of the box. A single license costs 249€ (or 199€ if you take one before October the 11th) and comes with 6 months worth of updates. Get Linkurious if you want to learn more about your graph data.

Bridging the gap between graph data and business users

We strongly believe that the businesses that succeed are the ones that are capable of using hard data to answer the questions they face. Whether you are considering the intentions of your consumer, the network of your competitor or the exchange of information within your organization, our world revolves around networks. They are a great way to think about the underlying relationships in data.

We can’t wait to see how you are going to use your graph data.

Six degrees of Kevin Bacon : a webinar on graph visualization (and movies)

Watch the video record on-line at watch.neo4j.org.

play-webinar-1

In the 1960’s a scientist called Milgram unveiled the underlying social networks in human societies with a few simple letters. Today it seems networks are everywhere but without tools and techniques to explore them, they remain mysterious. In a coming webinar, we’ll demonstrate how to use Linkurious to find information in the “Hollywood graph” and why Kevin Bacon is the center of the known Universe.

Six degrees of Kevin Bacon

Milgram’s experiments are linked to the small-world phenomenon.  Through a series of experiences, Milgram and other researchers proved that human societies have a short average path between different people. How short? There is a theory called Six degrees of separation according to which everyone and everything is six or fewer steps away, by way of introduction, from any other person in the world. It means that through friends of friends you are connected to everyone on the planet, from Justin Bieber to the most remote villages in the Amazonia. On virtual social networks, it’s quite true : on Facebook the average number of acquaintances between two users is 4,74. So it’s not hard to imagine that in little networks like the world of Hollywood, everyone knows everyone. Movie buffs have actually invented a game around that called Six degrees of Kevin Bacon. The rules are simple : you have to find the connections between an actor and Kevin Bacon via the people they both have played with. The person who finds the shortest path wins!

How to explore graphs

Graph exploration is not just a trivia game though. Financial transaction, logistics systems or telecommunication networks are all real-world example of graphs. Finding abnormal transactions and uncovering the people they involve, dispatching trucks in an efficient way or improving the reliability of a phone network are all problems that involve understanding graphs. But when you don’t have the tools, networks can become mazes.

On Thursday September 05 @ 08:00 PDT / 17:00 CEST, we’ll present a webinar on the exploration of graphs. Make sure to register for the webinar if you have a Neo4j database and want to better understand the data you store or if you’re looking for new ways to explore the underlying networks in your data.

We’ll use the graph of Hollywood actors (and actresses) to show how to use Linkurious to explore Neo4j databases and find valuable insights. Here are some of the topics we’ll discuss :

– how to see the connections of a single node;

– how to explore a graph starting from a local view;

– how to filter and refine your canvas to make it clearer;

– how to run advanced queries;

Here is a short video that gives you a preview of what we’ll be talking about :

Linkurious – Play the Kevin Bacon game from Linkurious on Vimeo.

If there are questions you want to ask or problems you’d like us to talk about, we welcome your feedback. Please share it in the comments section or via the contact page. We’ll either tackle them during the webinar or we’ll get back to you.

 

Talking about graph visualization in London (27/02)

We will be in London on the 27th to talk about graph visualization.

We will explain why visualization is key to understanding graphs and present some of the options people can use to do so. Of course, we will also talk about Linkurious and why we think it solve some of the common problems with visualization solutions.

Registration is free, you just have to signup here. The event starts at 18:30.

If you are interested by graph and living in London, we would love to see you and talk. Come join us!

We are looking for interesting graph or network datasets : please help us!

We are building a tool to help people understand and visualize graph data. This means that we are always on the lookout for new datasets we can bring the power of graph visualization to. In the coming months we will be participating in conferences, meeting people and writing stuff on this blog. We want to use these opportunities to share interesting stories about the world we live in and we need you for that.

There is something you are curious about? You think there are graph data out there that would be fun to visualize? You want to learn and explore the connections between things? Visualize a particular network? Please share your ideas with us.

We are looking for highly connected data that can be modeled as nodes and links. The easier it is to import it in Neo4j the better.

Currently we are using themoviedb.org database in our demo. There are a few other interesting datasets including some that are already prepared for Neo4j (here).  Stanford has a nice collection of large datasets too. If you are curious, you can learn about it here.

You can help us with your suggestions about the data and the stories graph visualization can be used for. If you have ideas you want to share with us you can contact us on twitter (@Linkurious), via mail (contact {at} linkurio.us) or just leave a comment below.

We are looking forward hearing from you.