Friday, 17 October 2014

Anatomy of an Emerging Knowledge Network: The Zapnito Graph Vizualized


In this article, I take a high-level look Zapnito, a multi-tenant "Networked Knowledge" platform designed around small, expert communities.

Zapnito is a knowledge sharing platform that allows organizations to create branded networks of experts. It's aimed at publishers, consultancies, media companies, and other corporations. Zapnito includes some social features (such as follow relationships, collaboration), but its focus is knowledge sharing rather than social networking.

As the founder puts it: "Zapnito is a white label platform that offers knowledge network capabilities for publishers. We provide both highly private and open networks, and we own neither publisher content or associated data - both of these are retained by publishers." 

The aim of this article is to show some of the interesting insights that can be gained from basic Social Network Analysis (SNA) of Zapnito. I'll be showing visualizations (such as that on the right) built from an anonymized subset of the Zapnito database, and discussing what can be learned from these.

Note: If you're a more SNA-savvy reader, I won't be diving into metrics such as Average Path Length or Clustering Coefficients - please look out for a future article on these topics.

Users and Followers

What does the core social network look like?

The graphs in this article were built using Gephi, a desktop application designed specifically for Social Network Analysis. All that's needed is a list of nodes (Zapnito users) and a list of edges (follow relationships) in flat file format, and you have a network.

The Zapnito team kindly provided me with an anonymized extract of their database covering several representative customers, so with a little data processing the network could be imported as a directed graph, then vizualized:

The core Zapnito network

Here, each node represents a Zapnito user, and each edge represents a follow relationship. Nodes are scaled according to the number of followers, which is a typical measure of influence in a social network.

The graph is organized using a built in algorithm, one that simulates a physical model in order to find an aesthetically pleasing layout. Another effect of the layout algorithm is that related users tend to be close to each other, while unrelated users are further apart.

There are a couple of observations we can make about the above graph.

  • First, there's one user who appears to be central to the network, someone with numerous connections to the rest of the graph, and relationships to many less influential users. This user is in fact the founder of Zapnito, who has had a long time to build up connections and is motivated to connect with as many Zapnito users as possible to encourage use of the network.
  • Second, you may notice several clumps or clusters - there's a large one to the left, one underneath, and one or two to the right. Apart from a small amount of adjustment by me, the graph as you see it represents the output of the layout algorithm, so what's going on? 
To understand further, we need to look at Zapnito's grouping mechanism.

Group Membership

Zapnito is designed to serve the needs of expert communities, so an essential feature is the set of communities that users can be part of.

These range from invitation-only, private communities with exclusive membership, to open communities that encourage public participation around selected contributors. Examples of public communities include the LifeLabs network, and Zapnito itself.

Note that Zapnito typically uses the term network to refer to the expert groups they host; here I'll use the term community to differentiate from the social network that's being analysed.

So what happens if we partition the nodes by community?

The Zapnito network, partitioned by community

We can now start to see the reason for the clumping generated by the layout algorithm: there is fairly high cohesion between members of a community. This is a nice result, and it's interesting to see the network of networks manifested in this way.

However, Zapnito users can actually be members of many different communities, which you can see above as dark grey nodes. It's important to know who these users are, because they can act as bridges in the network and may be instrumental in disseminating information between communities. Again it's understandable that the founder is a bridge, though there are several others worth noting.

As well as the bridge users, there are some interesting anomalies in the graph deserving of further analysis - but that's out of scope here.


Automatically Detected Communities

So we've seen the communities as defined by the Zapnito adminstrators, but there's another perspective we can take. Gephi has a built in feature to detect communities, using the Louvain algorithm. This detects the most strongly connected nodes within the network, and assigns them to groups. 

Here's what it finds in the Zapnito graph:

Automatically detected communities

Here, the algorithmically detected communities are quite similar to the real communities, but with some notable differences:

  • First, there's a distinct community around the founder. Again this simply reinforces the fact that the founder plays a central role in establishing and promoting the network.
  • Second, some smaller communities which are visible in the previous graph have been folded into the larger communities. 
This second point is worth bearing in mind if you're considering using community detection to provide social network features: you may offend your users if you assign them to the wrong group.

Of course the opposite may be true - a user's follow relationships may reveal the truth of their interests (or allegiance), and may be better indicator of community membership than the set of pre-configured communities on offer.


Contributions and Impact

So far, we've looked at the overall network structure, as well as communities within the network. But Zapnito is a content distribution system for experts, so what insights can we gain here?

The Zapnito database provides counts of video submissions, articles, and comments made by each user. By extracting this data we can highlight the users in the network who make the biggest contribution. Contributions can be counted individually by type, but it's more interesting to look at an aggregate view.

Below, users are shaded relatively according to an overall contribution score - where video submissions scored ten points, articles five points, and comments one point:

Biggest contributors

Here we can see that most users have modest numbers of contributions compared to a handful of very active users. Given the nature of expert communities, this is expected: apart from a small number of prolific content producers, most users will generate high quality submissions, but infrequently.

It's also worth noting that the largest contributor is not the most influential, at least in terms of followers. This is a useful thing to know - it may be beneficial, for example, to promote this user to increase their reach.

We may also want to find users who make little or no contribution, but have influence within the network. We can find these users by modifying the shading in Gephi to give more weight to users who have made at least a small contribution; this brings out the lurkers!

Lurkers (shown in orange)


Above, the red nodes represent users who have made no contributions - comments, posts or otherwise. These individuals, especially those with reasonable numbers of followers, are prime targets to encourage greater participation.

We can use a similar principle to bias the shading to the highest scoring contributors only:

Heroes (in purple)


Here again we're showing the heroes of the system - this is just an alternative view to the graph showing the overall contribution score, but here the biggest hitters in terms of contribution are emphasized.


Conclusions

There are a few key conclusions to take from the above analysis.

First, it's clear that Zapnito's founder has an important role to play in the emerging network, as a well-connected influencer and as a bridge between different communities. However the centrality of the founder's node in the graph is mostly related to his activities in promoting Zapnito and encouraging participation by following and engaging with other Zapnito users, and it will be interesting to see how this changes over time as the network grows.

Next, the difference between official and detected communities suggests that group membership is not clear cut, and is likely to shift over time. This may provide opportunities in the form of emergent groups that were not originally foreseen, as well as potential issues such as split loyalties or schisms in existing communities.

Finally, the process of scoring contributions to build an aggregate score is a useful technique for identifying key contributors, and contrasting such a score with a measure of reputation or impact helps identify influential lurkers, as well as major contributors with limited reach. The former can be encouraged to contribute, while the latter can be supported in building their network of followers, both of which will support dissemination of quality content across the network.

Thursday, 10 April 2014

I Know Where You Were Last Summer: London's public bike data is telling everyone where you've been

This article is about a publicly available dataset of bicycle journey data that contains enough information to track the movements of individual cyclists across London, for a six month period just over a year ago.

I'll also explore how this dataset could be linked with other datasets to identify the actual people who made each of these journeys, and the privacy concerns this kind of linking raises.

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It probably won't surprise you to learn that there is a publicly available Transport For London dataset that contains records of bike journeys for London's bicycle hire scheme. What may surprise you is that this record includes unique customer identifiers, as well as the location and date/time for the start and end of each journey. The public dataset currently covers a period of six months between 2012 and 2013.

What are the consequences of this? It means that someone who has access to the data can extract and analyse the journeys made by individual cyclists within London during that time, and with a little effort, it's possible to find the actual people who have made the journeys. 

To show what's possible with this data, I built an interactive map to vizualize a handful of selected profiles.

Please note: the purpose of this article is to expose the risks that can come with open datasets. However I've held off from actually trying to find the people behind this data, mostly because of the privacy concerns but also because (thankfully) it requires a fair bit of effort to actually identify individuals from the data...

Below, you'll find a map of all journeys made by one specific cyclist (commuter X), selected because they're one of the top users of a familiar bicycle hire station near where I work:

Bike journeys map - commuter X [interactive version]


Each line represents a particular journey, the size of the line showing the number of times that journey was made. The size of the circle represents the number of different destinations that the cyclist has travelled to and from that bike station. Purple lines indicate there were journeys in both directions, while orange lines (with arrows) indicate journeys that were one-way only.

Bigger, therefore, implies the route or station has greater significance for the person.

NOTE: if you think you might be this person, and you're unhappy having your personal journey data here, please contact me and I will remove the offending map. Then contact TFL (as I have) and tell them to remove customer record numbers from the data.

So what can we tell about this person?

First impressions suggests that they probably live near Limehouse, work in Kings Cross, and have friends or family in the Bethnal Green / Mile End areas of London. This story is strengthened if we filter down to journeys made between 4.00am and 10.00am:

Commuter X - morning journeys [interactive version]


We can see that this person only travels to Kings Cross in the morning, when departing from the Limehouse area or from Bethnal Green. So a morning commute from home, and/or a partner's abode? Applying a similar filter for the afternoon and evening shows return journeys, so the commuting hypothesis becomes stronger still.

Like me, you're probably starting to feel a bit uncomfortable at this point - after all I'm putting a story to this person's data, and it's starting to sound quite personal.

What's more interesting (and worrying) is that:

  1. I'm not really trying very hard, and a deeper inspection of dates, times, locations etc. can reveal far more detail
  2. There's enough here to start thinking about putting a name to the data.

All that's needed to work out who this profile belongs to is one bit of connecting information.

A Foursquare check-in could be connected to a bike journey, though it would be difficult to connect it to the cycle scheme. More likely would be a time-stamped Facebook comment or tweet, saying that the Kings Cross boris bike station is full. Or a geo-coded Flickr photograph, showing someone riding one of the bikes...

Any seemingly innocuous personal signal would be enough to get a detailed record for someone's life in London ... travelling to work, meeting up with friends, secret trysts, drug deals - details of any of these supposedly private aspects of our lives can be exposed.

Here's another profile, chosen because of the volume of journeys made:


Complex bike journey map [interactive version]


Hopefully you can see the richness of the information that is available in the TFL dataset. Every connection on the map represents something of significance to the cyclist, each bike station has some meaning. As well as being a digital fingerprint that can be linked to personally identifiable information, the journey data is a window on this person's life.

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On a final note, I'd like to point out that there are positives to releasing such data, which can be seen (for example) in the following map:

Commuter destinations around Victoria [interactive version]


The above map shows commuter journeys from a bike station near embankment to various stations around Victoria. These are journeys made between approximately 4.00pm and 5.30pm - so return commutes from work, presumably followed by a train journey from Victoria southwards. Here, there is one point of departure but three destinations, probably because Victoria Rail Station is a major transport hub, so the bike stations nearby will be popular and may often fill up.

The point is that there are benign insights that can be made by looking at individual profiles - but the question remains whether these kind of insights justify the risks to privacy that come with releasing journey data that can be associated with individual profiles.

Credits

Leaflet.js - web mapping library
Cloudmade - map tiles
Transport For London - datasets of Boris Bike data



Sunday, 2 March 2014

London maps and bike rental communities, according to Boris Bike journey data

Every time someone in London makes a journey on a Boris Bike (officially, the Barclays Cycle Hire Scheme), the local government body Transport For London (TFL) record that journey. TFL make some of this data available for download, to allow further analysis and experimentation.

Below, you'll find maps of the most popular bike stations and routes in London, created from the TFL data using Gephi, plus a few simple data processing scripts that I threw together. The idea for these maps originated within a project group at a course on Data Visualisation, held at the Guardian last year. We're working on a more publisher friendly form, so thank you to my course mates for giving me the go ahead to include them here.

First, here's a map showing all bike stations and all popular journeys.


Popular Boris Bike journeys and stations. Full version.

The first map shows the most popular routes and bike stations, those with more than ~150 journeys made during the six months of data that TFL make available. The size of each bike station in this map is based on the number of popular journeys that start or end at that station, a measure of the connectedness of the location. Note: the labels just show the rental area, not the specific station name.

Next, a map where the stations have been grouped together into rental areas, as allocated by TFL:


Rental areas and traffic volumes in the Boris Bike network. Full version | Alternative.

The second map is a version of the first map where related bike stations have been grouped together, and the volume of journeys between areas determines the weight of each connection. Colours in the second map are related to distinct communities in the network - more on this later. The position of the rental areas is approximate and calculated by Gephi. So please don't blame me for any geographical inaccuracies in this map ;)

Some interpretation, along with inspection of underlying data shows that:
  • Major entry points for Boris Bike use are via Kings Cross and Waterloo, more than likely due to commuters arriving from the North and South then heading deeper into London for work.
  • The most popular journeys are those around Hyde Park, corresponding to a popular tourist activity. 
  • The most popular journey (by a long way) is from Hyde Park Corner ... to Hyde Park Corner, presumably a nice trip round the park.
  • The most popular commuter route is between Waterloo (station 3) and Holburn, probably via the Waterloo Bridge.
Of course that's just scratching the surface, and just one example of how to vizualize the data. There's much more that can be done, and similar maps have been created before. Here are a couple of my favourites, plus another of my own, afterwards:
  • This delightful video by specialist Jo Wood at City University in London, published by New Scientist also shows popular routes in the network.
  • A recent BMJ article included a street-level map showing predicted routes and volumes, the focus here is on the health impact of bike sharing schemes.
A bit of experimentation with Gephi's community detection tool results in this map:


Rental communities in the Boris Bike network. Full version.

Here, major connected clusters of bike stations are shown in the same colour (red for Waterloo and environs, Green for around Hyde Park, etc). The communities are detected using Gephi's implementation of the Louvain Method, which finds local communities within large networks. This algorithm has a random element, and generates slightly different communities on each run. However it's clear from repeated runs that distinct local communities exist in the network, in particular around Hyde Park, Kings Cross, Waterloo, and Canary Wharf.

The map that shows bike rental areas (rather than stations) was coloured according to these communities, with journeys between different communities having a colour that's a mixture of the two that are involved.

If you fancy playing with the data or trying out some other visualizations, you can find everything in this GitHub repository.