There are more and more great tools getting developed for visualizing our social networks. One of the more beautiful ones I have come across is Fidg’t. While not quite a SN visualization tool, it does operate on data from SN’s.
Fidg’t is an interactive display that looks at your tags in Flickr and LastFM, and shows the relationships visually. There’s even a movie of the tool in action.
Please Facebook: give us filtering!
There has been a lot of talk lately about the increasing and sometimes overwhelming amount of data we are exposed to daily. [Google: information-overload]
What happens when it’s from your friends?
I have several hundred friends across the social networks I use, and even with the short updates SN’s usually enforce, these can add up. Combine that with updates internal to the SN (e.g. Bob just became friends with Sally), and I get enough information that if I were to process it all, it would really interfere with my day. However, my inability to process it all reduces and limits the value I receive from SN’s.
There are companies looking into this, including Socialmedian. If they figure it out before Facebook, Myspace, and friends do, expect Socialmedian to steal some serious thunder.
Effective use of social networks (SN’s) is closer to sales than it is to marketing. You want to build momentum in the network, and marketing alone will not provide that.
There’s a lot more to SN’s than better demographics, and given the abysmal value advertisers are are placing on Facebook (suggested $0.32 CPM vs. $1.15 for average online CPM in 2007 as per CPM Advisors LLC), demographics just aren’t cutting it.
The alternative SN’s should looking to is helping companies sell to their networks. With all of the embedded relationship information, any salesman would love to get their hands on that data for companies they are selling to.
As SN’s age and continue to fill in, this becomes a reasonable opportunity (LinkedIn is already there). In the meantime, SN’s have to provie value to retails scale vendors. Since the per-sale return from using a sales team is likely to be negative, they need to place their bets on individuals likely to get others to buy too.
In other industries, we’ll use pharmaceuticals as our example, market research teams will do extensive survey work to determine the most influential figures in decision making relevant to their products.
Even after the enormous expense of conducting these 6 month or more research projects, and taking into account all of the known problems with determining influence with surveys, pharmaceutical companies often dedicate a specialized sales team to act on the data. One company analyzed in a current paper showed approximately a 20% increase in revenue from this collaboration between marketing and sales.
Unfortunately, survey based methodologies become prohibitively expensive when moving from a 1,000 to 10,000 doctor network to a 10 or 100 million customer retail market.
The good news is, SN data is better and more accurate than surveys, and the data already exists. You have the actual relationship matrix, rather than skewed survey information. That alone provides quite a punch to sales. Marry that with frequency of communication data, and you’ve got a goldmine for sales.
[Photo by: Beth and Christian]
From eponymous Social Network data alone, I can tell you who has, for any group, the most influence, who the leaders are, and who you need to convince in order to turn the opinion of the group as a whole. The question is are you going to be a trusted adviser, or a hard sell?
This ability to analyze a network often causes a knee-jerk reaction of unease by people new to the field, myself included when I first started. But, after considered thought and discussion, there are no new ethical questions here, just the same old difficult ones. First, a discussion of sales.
We all have friends whose opinions we trust above others about certain product classes. My brother-in-law is an incredible and studied amateur photographer (not that I can ever get him to update his gallery), and to him I turn for all things photographic. Another friend is an insatiable and articulate consumer of modern fiction, and whom feeds me many great book recommendations. For electronic gadgets, I turn to yet another. I trust their judgment and opinions; if you can convince them that your product is great, you have gone a long way toward convincing me. Further, switching to the general, we look to our gurus for information and ideas about the new. If you as a manufacturer/service/producer bring new ideas to my gurus, you are helping them seek out new information, which they tend to do naturally.
So, as a Social Network provider, or as a consumer of social network data for sales and advertising, you have a choice: treat networks as just another advertising platform, and be treated by the participants as just another advertiser; or provide value into the network, and reap the rewards.
[Photo by bonkedproducer]
Finding good data in this field is difficult, even most of the academic literature references relatively small networks of less than 100 or so individuals. I suggest that the academic research is just starting to take off now (although the field is very far from new), because of availability of large real world datasets available in the social networking sites.
Nathan Eagle (Reality Mining at MIT) was kind enough to share 330,000 hours of proximity and cell phone communications data he and the team collected from volunteers over the course of the project. To say I am quite excited about digging into it, would be an dramatic understatement.
For other large data sets, Duncan Watts is spending his sabbatical over at Yahoo!, and I can only hope there are other people looking really hard at the data available there, Facebook, Hi5, Google, and many more. Research into people’s behavior, especially in a commercial setting is not only a great thing for the unprecedented data, but at least equally as important, this also brings to front the ethical implications.
[Image: Luc Legay‘s Facebook network]
Like all good science, analyzing social networks sometimes works out to proving things we always thought were true. Sometimes, we never even had any idea how right we were. For example, we really do spend more time with people we like.
A few really bright folks from MIT and the Kennedy School, have a paper pending publishing:
[analyzing] 330,000 hours of continuous behavioral data logged by the mobile phones of 94 subjects, and compar[ing] these observations with self reported relational data.
Three significant conclusions:
So, what do these conclusions suggest for practitioners?
Observed vs Reported Data: Surveys are great for all the reasons surrounding explicit participation, but the bias effects are significant. Find a way to marry active participation with empirical exploration and analysis of social networks.
Friendship and After-hours: Don’t under estimate the power of emotion on business decisions. Since we’re more likely to agree with data that confirms any already held thoughts, let’s be realistic and recognize the impact that, viewed through friendship, has on communication in our firms.
Proximity and Friendship: While I was unable to tease out any correlation/causation relationship from this paper, if we consider friendship as a proxy both for trust and ease of ability to work with (through shared history, goals, culture, etc.), there are some solid implications on the upper limit to the value of outsourcing.
[Photo by Ed Yourdon]
Good news, from Roland Piquepaille over at ZDNet…
According to Nature News, a team of French researchers has used medieval documents to create the oldest detailed social network ever constructed. The mathematicians and computer scientists looked through thousands of records of land transactions dating back as far as 1260 in a Southwest part of France.
Makes me wonder why I did not come across it earlier. Oh, right, because they titled the paper Batch kernel SOM and related Laplacian methods for social network analysis. Shame on you French Scientists, don’t hide the good stuff.
Martin Krzywinski at the Genome Sciences Centre of the BC Cancer Agency, created software called Circos designed to help elucidate the interaction of genes, and has used it to create some truly beautiful graphs.
The software is pretty complex, and I have only figured out how to use his simple on-line version, which limits the number of inputs. But, even so, here is an image I created using Circos. The image represents the number of emails exchanged by the top 10 most connected participants with each other, from an active large email list.
Are there lower limits to the size of Social Networks worth analyzing? The upper limit seems to be a function of how much time (and compute power) you have, but the lower?
When we do analyses, these are certain characteristics we look for as verification that our models hold. A key characteristic is the shape of the distribution of the number of relationships.
Power-law relationships in social networks are so pervasive, we are surprised when we do not see them. The co-sponsorship of Senate Resolutions, is one such example where we do not see a power-law distribution. In the current session, the 110th, the distribution is just about as different as you can get from a power-law: the result is linear (R-sq > 0.97). My hypothesis is the number of Senators is just too few for the power-law distribution to emerge.
Since there are some significant constitutional barriers to increasing the number of Senators and taking a new sample, in order to test this hypothesis, I opted for locating a different larger set. Wanting to find a network with similar culture and behaviors to control where possible for other variables, I analyzed the House Resolutions from the same period.
Running the same test across the House Resolutions (1,986 resolutions vs 784 in the Senate), the shape was still not best fit to a power-law. Instead, a logarithmic fit was near perfect (>0.99 R-sq). So, while his does not prove my hypothesis, the shape of a log fit is similar to, but less dramatic than, power-law. This result certainly suggests further exploration.
One possible logical conclusion is distribution forms change with scale. Much of the research on social networks has been on large scale networks, where the math is at its most difficult. At this smaller end of spectrum, especially with the Senate, the networks may be small enough that other analyses could be simpler.
Anthropologist Robin Dunbar, has done research showing humans can keep the interrelations of about 150 people in their heads. More than that number, and we are out of luck. With this in mind, it would make sense that relationship/contact distribution would stretch and distort as the number increases in scale.
So, if you are looking for the key members of a network smaller than “Dunbar’s Number,” there is an easier way to find out who they are: ask. If you can get a few people who are already invested in the network to answer you truthfully, they will be able to give you a really good idea who the key people are.
I always enjoy analyzing social networks (SN’s) that have had a lot less press than the Goliaths of MySpace and Facebook. I have done an awful lot of them, but one of my favorites was looking at the co-sponsorship patterns in the US Senate, 110th session (the current one).
This analysis was especially enjoyable because the graph is just one giant cluster, so conclusions took some real digging. So, what did we learn?
We learned a few things: graphs are just the beginning of analysis (but we knew that already); not all junior Senators are as strategic as others; and directionality of the relationships can have a large impact.
There are a handful of junior Senators setting themselves up for favor by strategically co-sponsoring specific bills. However, most junior Senators are building reputation by supporting anything that makes it to the floor. I am going to have to go back and analyze previous sessions to see which approach seems to provide the better payoff.
Relationships by-and-large are unequal between participants. Sometimes they are very close, sometimes they are very different. In our analysis of the Senate, many of these relationships are very unequal; a junior Senator is much more likely to co-sponsor a bill of a senior Senator than vice versa. Without bringing this inequality into play, our notion of network centrality is challenged. In this case, the two Senators most central include one first elected in 2004, followed closely by one who is a member of the powerful Senate Appropriations Committee. If we view networks as expressions of influence over flow of information, including favors, that just doesn’t make sense.
When we start to bring directionality into consideration, which I did by splitting out the sponsors and co-sponsors for half of the Senate’s 104th session (1995); results become much more as expected. The most central Senator was John Warner, then president pro tem.