Social Network Analysis | Robert Gimeno's Adventures in Data Science

Whist I was away in Moldova I looked through some academic papers on SNA. There are many papers on the subject so I just looked at the ones which seemed to be most relevant: those that discussed SNA using email data. One paper included analysis of the text of the email as well as the less sensitive information such as sender and recipient so this was not directly applicable to email log analysis. Others, however, had recognised that an organisation is unlikely to allow analysis of email contents and concentrated on what could be learnt from other information in the log (excluding message subject). Here is a summary of what these papers demonstrated could be learnt:

The real organisation structure (as opposed to what’s on organisational charts)
Various ranking of individuals
Identification of vulnerabilities
Rate of adaptability to change
Assessing morale

There was also some interesting insight into how to squeeze as much information from email logs as possible. For example I had not really considered the time an email was sent as a particularly useful piece of data but it can be when looking at how quickly an email is sent back by a recipient (i.e. A send B and email, 5 minutes later B sends A and email); the faster the response from, B to A, the more likely A is more important.

Unfortunately what the academic papers do not do is put value on what any of this information and analysis is worth; I expect this will only come from experience or looking at the experience of others, as we learn more I will post more.

For many organisations their “greatest asset”, and usually largest cost, is the people they employ. It would seem sensible, therefore, for them to want to understand as much as possible about employees and especially if they are deriving the optimum value from them. Traditionally organisations have looked individually at employees, for example through annual reviews. What many organisations do not do is to look at all employees as a whole; this may be because back in the 20th century it was not that easy to find and collate data to allow such analysis. Today organisations have a wealth of data that allow them to look at employees as a whole and, specifically, how they communicate with each other, for example: e-mail, telephone, instant messaging, web browsing, meeting arrangements. The use of some of this information is contentious but a useful starting point is e-mail; by removing the message content and subject we are left with a simple “A sent B a message” and if we record and collate all these interactions over a period and load the information into some analysis software we get to see the following:

Yikes! The above represents the email conversations between 2000 people in an organisation over 24 hours. Dots represent people and the lines between them represent emails. The redder a dot is the more email connections the person it represents has and similarly the redder the line the more emails where sent between those people. The analysis tool (Gephi) has used the Fruchterman Reingold algorithm to arrange the dots (referred to as nodes) into the picture above. As can be observed the better connected nodes have migrated towards the centre but, as can also be observed, it is not even and there are ‘clumps’ of nodes.

The big question is what can an organisation learn and do with this information and is it worth paying for such an analysis? To start with it is relatively easy to visually see the cliques (the ‘clumps’) and also the nodes that connect the cliques (the ‘bridges’). The question of whether having cliques is a good or bad thing will depend on the organisation and who is in which clique. For example the organisation pictured above has, like many, been through a number of mergers, acquisitions, splits and sales and may want to ask “has integration been successful ” – if we see distinct cliques based on the originating company the answer is probably “no”. It may also want to ensure it retains the people who connect the cliques because without them the organisation becomes more disjointed; simply looking at the annual review of these people may not reveal their true value to the organisation. Beyond what can be seen visually there is a large body of research in the field of Social Network Analysis (SNA) where mathematical algorithms can be applied to reveal information about the graph (graph is the technical term for the collection of nodes and their connections).

I am off to Moldova for the next two weeks and have a stack of papers to take with me. When I get back I’ll post what I have learnt and I hope to describe in more detail some Social Network Analysis an organisation could conduct that would provide it with real benefits.