Sep 22 2014

Book review: Network Graph Analysis and visualization with Gephi by Ken Cherven

network_gephi

This review was first published at ScraperWiki.

I generally follow the rule that if I haven’t got anything nice to say about something then I shouldn’t say anything at all. Network Graph Analysis and visualization with Gephi by Ken Cherven challenges this principle.

Gephi is a system for producing network visualisations, as such it doesn’t have a great many competitors. Fans of Unix will have used Graphviz for this purpose in the past but Gephi offers greater flexibility in a more user-friendly package. Graph theory and network analysis have been growing in importance over the past few years in part because of developments in the analysis of various complex systems using network science. As a physical scientist I’ve been aware of this trend, and it clearly also holds in the social sciences. Furthermore there is much increased availability of network information from social media such as Twitter and Facebook.

I’ve used Gephi a few times in the past, and to be honest there has been an air of desperate button clicking to my activities. That’s to say I felt Gephi could provide the desired output but I could only achieve it by accident. I have an old-fashioned enthusiasm for books even for learning about modern technology. Hence Network Graph Analysis and visualization with Gephi – the only book I could find with Gephi in the title. There is substantial online material to support Gephi but I hoped that this book would give me a better insight into how Gephi worked and some wider understand of graph theory and network analysis.

On the positive side I now have a good understanding of the superficial side of the interface, a feel for how a more expert user thinks about Gephi, and some tricks to try.

I discovered from Network Graph Analysis that the “Overview” view in Gephi is what you might call “Draft”, a place to prepare visualisations which allows detailed interaction. And the “Preview” view is what you might call “Production”, a place where you make a final, beautiful version of your visualisations.

The workflow for Gephi is to import data and then build a visualisation using one of a wide range of layout algorithms. For example, force-based layouts assume varying forces between nodes for which an arrangement of nodes can be calculated by carrying out a pseudo-physical simulations. These algorithms can take a while to converge, and may get trapped in local minima. The effect of these layout algorithms is to reveal some features of the network. For example, the force layouts can reveal clusters of nodes which might also be discovered by a more conventional statistical clustering algorithm. The concentric layout allows a clearer visualisation of hierarchy in a network.

It’s clear that the plugin ecosystem is important to the more experienced user of Gephi. Plugins provide layout algorithms, data helpers, new import and export functionality, analysis and so forth. You can explore them in the Gephi marketplace.

Cherven recommends a fairly small, apparently well-chosen set of references to online resources and books. The Visual Complexity website looks fabulous. You can read the author’s complete, pre-publication draft of Networks, Crowds and Markets: Reasoning about a highly connected world by David Easley and Jon Kleinberg here. It looks good but it’s nearly 800 pages! I’ve opted for the rather shorter Graph Theory and Complex Networks: An Introduction by Maarten van Steen.

On the less positive side, this is an exceedingly short book. I read it in a couple of 40 minute train journeys. It’s padded with detailed descriptions of how to install Gephi and plugins, including lots of screenshots. The coverage is superficial, so whilst features may be introduced the explanation often tails off into “…and you can explore this feature on your own”.

Network Graph Analysis is disappointing, it does bring a little enlightenment to a new user of Gephi but not very much. A better book would have provided an introduction to network and graph analysis with Gephi the tool to provide practical experience and examples, in the manner that Data Mining does for weka and Natural Language Processing with Python does for the nltk library.

This book may be suitable for someone who is thinking about using Gephi and isn’t very confident about getting started. The best alternative that I’ve found is the online material on GitHub (here).

Sep 21 2014

Book review: Falling Upwards by Richard Holmes

fallingupwardsI read Richard Holmes book The Age of Wonder some time ago, in it he made a brief mention of balloons in the 18th century. It pricked my curiosity, so when I saw his book Falling Upwards, all about balloons, I picked it up.

The chapters of Falling Upwards cover a series of key points in the development of ballooning, typically hydrogen balloons from the last couple of decades of the 18th century to the early years of the 20th century. One of the early stories is a flight from my own home city, Chester. Thomas Baldwin recorded his flight in Airopaidia: Containing the Narrative of a Balloon Excursion from Chester, the eighth of September, 1785. The book does not have the air of a rigorous history of ballooning, it introduces technical aspects but not systematically. It is impressionistic to a degree, and as a result a rather pleasant read. For Holmes the artistic and social impact of balloons are as important as the technical.

In the beginning there was some confusion as to the purposes to which a balloon might be put, early suggestions included an aid to fast messengers who would stay on the ground to provide but use a small balloon to give them “10 league boots”, there were similar suggestions for helping heavy goods vehicles.

In practice for much of the period covered balloons were used mainly for entertainment – both for pleasure trips but also aerial displays involving acrobatics and fireworks. Balloons were also used for military surveillance.  Holmes provides chapters on their use in the American Civil War by the Union side (and very marginally by the Confederates). And in the Franco-Prussian war they were used to break the Prussian siege of Paris (or at least bend it). The impression gained though is that they were something like novelty items for surveillance. By the time of the American Civil War in the 1860’s it wasn’t routine or obvious that one must use balloon surveillance, it wasn’t a well established technique. This was likely a limitation of both the balloons themselves and the infrastructure required to get them in the air.

Balloons gave little real utility themselves, except in exceptional circumstances, but they made a link to heavier-than-air flight. They took man into the air, and showed the possibilities but for practical purposes generally didn’t deliver – largely due to their unpredictability. To a large extent you have little control of where you will land in a balloon once you have gone up. Note, for example, that balloons were used to break the Prussian siege of Paris in the outbound direction only. A city the size of Paris is too small a target to hit, even for highly motivated fliers.

Nadar (pseudonym of Gaspard-Félix Tournachon), who lived in Paris, was one of the big promoters of just about anything. He fought a copyright battle with his brother over his, adopted, signature. Ballooning was one of his passions, he inspired Jules Verne to starting writing science fiction. His balloon, Le Géant, launched in 1863 was something of a culmination in ballooning – it was enormous – 60 metres high but served little purpose other than to highlight the limitations of the form – as was Nadar’s intent.

From a scientific point of view Falling Upwards covers James Glaisher and Henry Coxwell’s flights in the mid-nineteenth century. I was impressed by Glaisher’s perseverance in taking manual observations at a rate of one every 9 seconds throughout a 90 minute flight. Glaisher had been appointed by the British Association for the Advancement of Science to do his work, he was Superintendent for Meteorology and Magnetism at the Royal Greenwich Observatory. With his pilot Henry Coxwell he made a record-breaking ascent to approximately 8,800 meters in 1862, a flight they were rather lucky to survive. Later in the 19th century other scientists were to start to identify the layers in the atmosphere. Discovering that it is only a thin shell – 5 miles or so thick which is suitable for life.

The final chapter is on the Salomon Andrée’s attempt to reach the North Pole by balloon, as with so many polar stories it ends in cold, lonely, perhaps avoidable death for Andrée and his two colleagues. Their story was discovered when the photos and journals were recovered from White Island in the Artic Circle, some 30 years after they died.

Falling Upwards is a rather conversational history. Once again I’m struck by the long periods for technology to reach fruition. It’s true that from a technology point of view that heavier-than-air flight is very different from ballooning. But it’s difficult to imagine doing the former without the later.

Sep 18 2014

Of Matlab and Python

I’ve been a scientist and data analyst for nearly 25 years. Originally as an academic physicist, then as a research scientist in a large fast moving consumer goods company and now at a small technology company in Liverpool. In common to many scientists of my age I came to programming in the early eighties when a whole variety of home computers briefly flourished. My first formal training in programming was FORTRAN after which I have made my own way.

I came to Matlab in the late nineties, frustrated by the complexities of producing a smooth workflow with FORTRAN involving interaction, analysis and graphical output.

Matlab is widely used in academic circles and a number of industries because it provides a great deal of analytical power in a user-friendly environment. Its notation for handling matrix (array) calculations is slick. Its functionality is extended by a range of toolboxes, and there is a community of scientists sharing new functionality. It shares this feature set with systems such as IDL and PV-WAVE.

However, there are a number of issues with Matlab:

  • as a programming language it has the air of new things being botched onto a creaking frame. Support for unit testing is an afterthought, there is some integration of source control into the Matlab environment but it is with Source Safe. It doesn’t support namespaces. It doesn’t support common data structures such as dictionaries, lists and sets.
  • The toolbox ecosystem is heavily focused on scientific applications, generally in the physical sciences. So there is no support for natural language processing, for example, or building a web application based on the powerful analysis you can do elsewhere in the ecosystem;
  • the licensing is a nightmare. Once you’ve got core Matlab additional toolboxes containing really useful functionality (statistics, database connections, a “compiler”) are all at an additional cost. You can investigate pricing here. In my experience you often find yourself needing a toolbox for just a couple of functions. For an academic things are a bit rosier, universities get lower price licenses and the process by which this is achieved is opaque to end-users. As an industrial user, involved in the licensing process, it is as bad as line management and sticking needles in your eyes in the “not much fun thing to do” stakes;
  • running Matlab with network licenses means that your code may stop running part way through because you’ve made a call to a function to which you can’t currently get the license. It is difficult to describe the level of frustration and rage this brings. Now of course one answer is to buy individual licenses for all, or at least a significant surplus of network licenses. But tell that to the budget holder particularly when you wanted to run the analysis today. The alternative is to find one of the license holders of the required toolbox and discover if they are actually using it or whether they’ve gone off for a three hour meeting leaving Matlab open;
  • deployment to users who do not have Matlab is painful. They need to download a more than 500MB runtime, of exactly the right version and the likelihood is they will be installing it just for your code;

I started programming in Python at much the same time as I started on Matlab. At the time I scarcely used it for analysis but even then when I wanted to parse the HTML table of contents for Physical Review E, Python was the obvious choice. I have written scrapers in Matlab but it involved interfering with the Java underpinnings of the language.

Python has matured since my early use. It now has a really great system of libraries which can be installed pretty much trivially, they extend far beyond those offered by Matlab. And in my view they are of very good quality. Innovation like IPython notebooks take the Matlab interactive style of analysis and extend it to be natively web-based. If you want a great example of this, take a look at the examples provided by Matthew Russell for his book, Mining the Social Web.

Python is a modern language undergoing slow, considered improvement. That’s to say it doesn’t carry a legacy stretching back decades and changes are small, and directed towards providing a more consistent language. Its used by many software developers who provide a source of help, support and an impetus for an decent infrastructure.

Ubuntu users will find Python pre-installed. For Windows users, such as myself, there are a number of distributions which bundle up a whole bunch of libraries useful for scientists and sometimes an IDE. I like python(x,y). New libraries can generally be installed almost trivially using the pip package management system. I actually use Python in Ubuntu and Windows almost equally often. There are a small number of libraries which are a bit more tricky to install in Windows – experienced users turn to Christoph Gohlke’s fantastic collection of precompiled binaries.

In summary, Matlab brought much to data analysis for scientists but its time is past. An analysis environment built around Python brings wider functionality, a better coding infrastructure and freedom from licensing hell.

Sep 10 2014

Inordinately fond of beetles… reloaded!

sciencemuseum_logo

This post was first published at ScraperWiki.

Some time ago, in the era before I joined ScraperWiki I had a play with the Science Museums object catalogue. You can see my previous blog post here. It was at a time when I was relatively inexperienced with the Python programming language and had no access to Tableau, the visualisation software. It’s a piece of work I like to talk about when meeting customers since it’s interesting and I don’t need to worry about commercial confidentiality.

The title comes from a quote by J.B.S. Haldane, who was asked what his studies in biology had told him about the Creator. His response was that, if He existed then he was “inordinately fond of beetles”.

The Science Museum catalogue comprises three CSV files containing information on objects, media and events. I’m going to focus on the object catalogue since it’s the biggest one by a large margin – 255,000 objects in a 137MB file. Each object has an ID number which often encodes the year in which the object was added to the collection; a title, some description, it often has an “item name” which is a description of the type of object, there is sometimes information on the date made, the maker, measurements and whether it represents part or all of an object. Finally, the objects are labelled according to which collection they come from and which broad group in that collection, the catalogue contains objects from the Science Museum, Nation Railway Museum and National Media Museum collections.

The problem with most of these fields is that they don’t appear to come from a controlled vocabulary.

Dusting off my 3 year old code I was pleased to discover that the SQL I had written to upload the CSV files into a database worked almost first time, bar a little character encoding. The Python code I’d used to clean the data, do some geocoding, analysis and visualisation was not in such a happy state. Or rather, having looked at it I was not in such a happy state. I appeared to have paid no attention to PEP-8, the Python style guide, no source control, no testing and I was clearly confused as to how to save a dictionary (I pickled it).

In the first iteration I eyeballed the data as a table and identified a whole bunch of stuff I thought I needed to tidy up. This time around I loaded everything into Tableau and visualised everything I could – typically as bar charts. This revealed that my previous clean up efforts were probably not necessary since the things I was tidying impacted a relatively small number of items. I needed to repeat the geocoding I had done. I used geocoding to clean up the place of manufacture field, which was encoded inconsistently. Using the Google API via a Python library I could normalise the place names and get their locations as latitude – longitude pairs to plot on a map. I also made sure I had a link back to the original place name description.

The first time around I was excited to discover the Many Eyes implementation of bubble charts, this time I now realise bubble charts are not so useful. As you can see below in these charts showing the number of items in each subgroup. In a sorted bar chart it is very obvious which subgroup is most common and the relative sizes of the subgroup. I’ve coloured the bars by the major collection to which they belong. Red is the Science Museum, Green is the National Rail Museum and Orange is the National Media Museum.

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Less discerning members of ScraperWiki still liked the bubble charts.

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We can see what’s in all these collections from the item name field. This is where we discover that the Science Museum is inordinately fond of bottles. The most common items in the collection are posters, mainly from the National Rail Museum but after that there are bottles, specimen bottles, specimen jars, shops rounds (also bottles), bottle, drug jars, and albarellos (also bottles). This is no doubt because bottles are typically made of durable materials like glass and ceramics, and they have been ubiquitous in many milieu, and they may contain many and various interesting things.

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Finally I plotted the place made for objects in the collection, this works by grouping objects by location and then finding latitude and longitude for those group location. I then plot a disk sized by the number of items originating at that location. I filtered out items whose place made was simply “England” or “London” since these made enormous blobs that dominated the map.

 

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You can see a live version of these visualisation, and more on Tableau Public.

It’s an interesting pattern that my first action on uploading any data like this to Tableau is to do bar chart frequency plots for each column in the data, this could probably be automated.

In summary, the Science Museum is full of bottles and posters, Tableau wins for initial visualisations of a large and complex dataset.

Sep 08 2014

Book review: Big data by Viktor Mayer-Schönberger and Kenneth Cukier

BigData

This review was first published at ScraperWiki.

We hear a lot about “Big Data” at ScraperWiki. We’ve always been a bit bemused by the tag since it seems to be used indescriminately. Just what is big data and is there something special I should do with it? Is it even a uniform thing?

I’m giving a workshop on data science next week and one of the topics of interest for the attendees is “Big Data”, so I thought I should investigate in a little more depth what people mean by “Big Data”. Hence I have read Big Data by Viktor Mayer-Schönberger and Kenneth Cukier, subtitled “A Revolution That Will Transform How We Live, Work and Think” – chosen for the large number of reviews it has attracted on Amazon. The subtitle is a guide to their style and exuberance.

Their thesis is that we can define big data, in contrast to earlier “little data”, by three things:

  • It’s big but not necessarily that big, their definition for big is that n = all. That is to say that in some domain you take all of the data you can get hold of. They use as one example a study on bout fixing in sumo wrestling, based on  data on 64,000 bouts – which would fit comfortably into a spreadsheet. Other data sets discussed are larger such as credit card transaction data, mobile telephony data, Google’s search query data…;
  • Big data is messy, it is perhaps incomplete or poorly encoded. We may not have all the data we want from every event, it may be encoded using free text rather than strict categories and so forth;
  • Working with big data we must discard an enthusiasm for causality and replace it with correlation. Working with big data we shouldn’t mind too much if our results are just correlations rather than explanations (causation);
  • An implicit fourth element is that the analysis you are going to apply to your big data is some form of machine learning.

I have issues with each of these “novel” features:

Scientists have long collected datasets and done calculations that are at the limit (or beyond) their ability to process the data produced. Think protein x-ray crystallography, astronomical data for navigation, the CERN detectors etc etc. You can think of the decadal censuses run by countries such as the US and UK as n = all. Or the data fed to the early LEO computer to calculate the deliveries required for each of their hundreds of teashops. The difference today is that people and companies are able to effortlessly collect a larger quantity of data than ever before. They’re able to collect data without thinking about it first. The idea of n = all is not really a help. The straw man against which it is placed is the selection of a subset of data by sampling.

They say that big data is messy implying that what went before was not. One of the failings of the book is their disregard for those researchers that have gone before. According to them the new big data analysts are comfortable with messiness and uncertainty, unlike those fuddy-duddy statisticians! Small data is messy, scientists and statisticians have long dealt with messy and incomplete data.

The third of their features: we must be comfortable with correlation rather than demand causation. There are many circumstances where correlation is OK, such as when Amazon uses my previous browsing and purchase history to suggest new purchases but the area of machine learning / data mining has long struggled with messiness and causality.

This is not to say nothing has happened in the last 20 or so years regarding data. The ubiquity of computing devices, cheap storage and processing power and the introduction of frameworks like Hadoop are all significant innovations in the last 20 years. But they grow on things that went before, they are not a paradigm shift. Labelling something as ‘big data’, so ill-defined, provides no helpful insight as to how to deal with it.

The book could be described as the “What Google Did Next…” playbook. It opens with Google’s work on flu trends, passes through Google’s translation work and Google Books project. It includes examples from many other players but one gets the impression that it is Google they really like. They are patronising of Amazon for not making full use of the data they glean from their Kindle ebook ecosystem. They pay somewhat cursory attention to issues of data privacy and consent, and have the unusual idea of creating a cadre of algorithmists who would vet the probity of algorithms and applications in the manner of accountants doing audit or data protection officers.

So what is this book good for? It provides a nice range of examples of data analysis and some interesting stories regarding the use to which it has been put. It gives a fair overview of the value of data analysis and some of the risks it presents. It highlights that the term “big data” is used so broadly that it conveys little meaning. This confusion over what is meant by “Big Data” is reflected on the datascience@Berkeley blog which lists definitions of big data from 30 people in the field (here). Finally, it provides me with sufficient cover to make a supportable claim that I am a “Big Data scientist”!

To my mind, the best definition of big data that I’ve seen is that it is like teenage sex…

  • Everyone talks about it,
  • nobody really knows how to do it,
  • everyone thinks everyone else is doing it,
  • so everyone claims they are doing it too!