Category: social networks

Social Reading

Feedly, the RSS Reader I’ve been using ever since Google Reader shut down, has announced a feature called “Shared Collections“. This is something like the Google Reader shared items (much loved by its loyal users including me, but something that apparently wasn’t good enough for Google to retain), except that it is available only for premium users.

 

While this is in theory a great move by Feedly to start shared collections, recognising the unfulfilled demand for social reading post Google Reader, their implementation leaves a lot to be desired. And I’m writing this without having used the feature, for, in an extremely daft move, it is available only for pro users. My problem is with the pricing model, which charges content creators (or curators or aggregators, if you like to call them that) for sharing content!

There are so many things wrong with this that I don’t know where to start. Firstly, if you charge people for creating content, that significantly increases the barrier to creating content. If there is an article I like and want to share with my (currently non-existent) followers, the fact that I have to create a premium account to do so means that the barrier to doing so is too high.

Secondly, if I’m going to be a consumer of shared collections from other people, I’ll need a certain critical mass of friends before I start using the feature. I won’t start using a feature only because one or two friends are curating content on it. The critical mass is much higher. And by putting barriers to entry to people who want to share, it makes this critical mass even more difficult to obtain.

Thirdly, Feedly doesn’t have a social network of itself so far (though I’m not aware what permissions they’ve taken from my when I used my Google account to log in to the service). And without having a ready social network for discovery (Google Reader leveraged the Google Talk network), how do they expect people to discover each other’s collections, once created? Are they relying on external networks such as Facebook or Twitter?

It is not easy to build a social network of curation. Google Reader had managed it quite well back in the day by first allowing people to share items without comment, then add external content, and then to add comments. It was an extremely powerful way for people to share blogs and other content, and discussion on that was rather active. I even remember quite a few people adding me on Google Talk for the sole reason of wanting to follow my Shared Items.

In recent times we’ve seen the news aggregator app Flipboard starting its personal collections feature. I have a collection, but don’t remember the last time I put something into it – for without any interaction on that, there’s absolutely no motivation. Flipboard, by the way, has access to your Facebook and Twitter graphs, and so has access to some sort of a social network. Yet, despite keeping the feature free, they haven’t been able to generate sufficient activity on it.

Feedly has got just about everything wrong with its Shared Collections feature. There is disincentive for content creators. There is no incentive for content consumers. They don’t have a ready social network. And there doesn’t seem to be any interaction.

If only Google were to bring back Google Reader and Shared Items, now that they’ve decided to dismantle Google+.

 

On apps tracking you and turning you into “lab rats”

Tech2, a division of FirstPost, reports that “Facebook could be tracking all rainbow profile pictures“. In what I think is a nonsensical first paragraph, the report says:

Facebook’s News Feed experiment received a huge blow from its social media networkers. With the new rainbow coloured profile picture that celebrates equality of marriage turned us into ‘lab rats’ again? Facebook is probably tracking all those who are using its new tool to change the profile picture, believes The Atlantic.

I’m surprised things like this still makes news. It is a feature (not a bug) of any good organisation that it learns from its user interactions and user behaviour, and hence tracking how users respond to certain kinds of news or updates is a fundamental part of how Facebook should behave.

And Facebook is a company that constantly improves and updates the algorithm it uses in order to decide what updates to show whom. And to do that, it needs to maintain data on who liked what, commented on what, and turned off what kind of updates. Collecting and maintaining and analysing such data is a fundamental, and critical, part of Facebook’s operations, and expecting them not to do so is downright silly (and it would be a downright silly act on part of the management if they stop experimenting or collecting data).

Whenever you sign on to an app or a service, you need to take it as a given that the app is collecting data and information from you. And that if you are not comfortable with this kind of data capture, you are better off not using the app. Of course, network effects mean that it is not that easy to live like you did in “the world until yesterday”.

This seems like yet another case of Radically Networked Outrage by outragers not having enough things to outrage about.

Twitter and Radically Networked Outrage

The concept of Radically Networked Outrage was originally conceived by my Takshashila colleague Pavan Srinath. Having conceived of it, he had promised to blog about it, but it’s been over a month and he’s yet to get down to it. Given this delay, I think I’m justified in stealing this blogpost.

One of the pet themes professed by people at Takshashila, especially Nitin Pai, is the concept of “radically networked societies”. There are too many posts to link to, so I’ll just link to this book chapter that Nitin has written, and to this TEDx talk:

So the whole concept is that societies nowadays are not hierarchical like in the past, but “radically networked”, in that the density of the graph of people in the world has increased significantly with technology. Not only has the density gone up – which means that people are connected to significantly more people than in the past – but technology has enabled people to communicate rapidly.

So you have twitter where you can broadcast your short thoughts. WhatsApp groups enable you to send, and propagate, messages to multiple people at once. This, combined with increased graph density, has resulted in ability for large numbers of people to coordinate and organise, and presents new kinds of governance challenges. For example, it was radically networked societies that resulted in the so-called Arab Spring (which, in hindsight, has mostly led to chaos). Radically networked societies also resulted in the Anna Hazare movement in 2011, which in turn led to the formation of the Aam Aadmi Party, which has taken Delhi by storm.

When societies are so radically networked that they can cause revolutions which can result in the overthrow of governments, they can also such radical networking for lesser causes, such as outraging. When the odd thatha outrages about a certain happening or piece of news, it doesn’t have any impact, and ends up in at best a letter to the editor, and dies a quiet death. If a handful of unconnected thathas outrage about something, it will still not amount to much, and one of their letters to the editor will get published.

However, put together a large number of people densely connected to each other, any outrage in such network will be immediately seen and noticed, and has the potential to go viral. The thing about outrage is positive feedback – when you see someone outraging about a particular topic that you mildly outrage about, you feel encouraged to make your mild outrage public. As the number of people in your network outraging about something increases, the likelihood of you joining in the outrage increases.

So as you can imagine, once there a certain critical mass to outrage about a particular issue, it can go truly viral, until just about everyone is outraging about the topic.

And outrage can have inter-issue positive feedback also. Once you are used to seeing a certain amount of outrage on your twitter timeline, you feel encouraged to make public any marginal outrage about any other issue also. And a number of people getting marginally thus pushed to make their outrage public can result in a further increase in radically network outrage!

We live in a time when societies are radically networked, and outrage is the order of the day. And since outrage causes more outrage, this outrage is unlikely to reduce. It is impossible to say anything remotely controversial on social media nowadays – a pack of outragers will immediately hound you. There are already some victims of such radically networked outrage – like the PR professional Justine Sacco who lost her job after an outraged mob failed to see the humour in her tweet, or scientist James Watson who had to auction his Nobel Prize after outrage about his comments about race had led to speaking assignments dying out, or footballer Ched Evans who is unable to find a club to hire him after doing time for rape. The latest victim of radically networked outrage is Nobel laureate Tim Hunt, who resigned his position as Professor following radically networked outrage about certain remarks he made that were deemed sexist.

And there is no escaping such outrage. In an attempt to escape it, I pruned my Twitter following list a couple of days back, unfollowing people who are highly prone to participate in radically networked outrage. At the end of it, my following list had grown so thin that there was no value for me in Twitter any more. I would just check twitter in the hope of interesting tweets, but come across hardly any tweets.

So today I begin my third sabbatical from Twitter. The first one (January 2014) lasted a month, and the second (August to November 2014) lasted three. I don’t know how long this will last. I’ll be robbed of interesting discussions for sure, but can do without all the negativity prevalent all over my timeline. But I’m sure Radically Networked Outrage will have its way of getting to me again!

In October, during my last sabbatical, I had written about the same topic. And in December, I had written about the “mob courts” of social media.

Value addition through comments

My friend Joy Bhattacharjya is a star on Facebook. He has a large number of friends (I haven’t bothered to see how many), most of whom seem to have him on their “good friends” list thanks to which they get each and every one of his updates (I had recently cribbed about Facebook’s algorithm, but when your friends love you, it doesn’t matter). And most of his updates are extremely insightful, some of them funny. If you are his friend, it is not hard to guess why his updates are so popular.

There is only one problem – it is impossible to comment on them. I mean, the comments section is always open, but the problem is that by the time you see an update, so many people would have commented on them that adding one more comment there doesn’t add any value. Writing something there, it seems, is not worth the time, for you assume that given the sea of comments the author won’t have time to read and appreciate your wisecrack. And so you move on.

Recently one friend announced his engagement. Another announced the birth of her child. It was again impossible to add value via comments to either – there had already been so many comments that adding one more wouldn’t add any value! I doubt if these “announcers” even bothered to read through all the comments people had posted. A compression algorithm might have done the trick for them, for most of them were extremely banal and non-value-adding “congrats” posts!

The last time my birthday was listed on Facebook (2010, if I’m not wrong), I got so many scraps on my wall that I had no time to read them, let alone respond to them. I promptly delisted my birthday from Facebook, with the result that nowadays hardly anyone wishes me on my birthday. Not on Facebook, at least, and I’m happy about not having to respond to a mechanical action!

On a similar note, one thing I get very pissed off (on Facebook) is “thread hijacking”. You get a nice discussion going in the comments thread on some post, and then someone else comes in (usually an aunty) and says something so banal that you don’t want to be seen on that thread any more, and the discussion goes for a toss. Oh, and such thread hijacking is more prevalent on Facebook’s other product Whatsapp (:P ), especially on groups where lack of threaded conversation means deep discussions are highly prone to being disrupted by long forwards someone sends!

Recently, Facebook introduced the threaded comments feature, one that I loved so much that I resisted a move away from Livejournal for ages just for that one feature, and when I moved to this blog, one of the first plugins I installed was one that allowed for threaded comments. Facebook has done badly, though. I use it primarily through the iPad app, and the threaded comments suck big time, requiring way too many clicks to navigate. If done so badly, I’d prefer blogspot-type dumb linear comment scheme only!

I sometimes wonder why I’m on Facebook at all. I used to use it at one point in time to look at people’s photos, and what they were up to. But now i find that it’s impossible to subscribe to a person’s photos without subscribing to her political views also, which are generally downright uninformed and sometimes extreme. And thanks to blogger-style comments, you cannot keep uninformed people out of your discussion on Facebook, unlike Twitter – they just keep popping up.

And there is no way for me to explicitly tell Facebook I want to see more or less of someone’s feed (like I could with Pandora, back when I used it). I have to rely on the algorithm.

All in all, Facebook seems like a dumb social network. To use a concept I’d mentioned here a few months back, it’s an “events and people” social network, with Twitter being more conducive to ideas. I sometimes end up asking myself why I’m on Facebook at all. And then I realise that there is no other way for me to access Joy’s updates!

Dominant affiliation groups

I was writing an email to connect two friends, when I realised that when you know someone through more than one affiliation group, one of the affiliation groups becomes “dominant”, and you will identify with them through that group at the cost of others. And sometimes this can lead you to even forgetting that you share other affiliation groups with them.

In social networking theory, affiliation groups refer to entities such as families, communities, schools or workplaces through which people get connected to other people. It is not strictly necessary for two connected people to share an affiliation group, but it is commonly the case to share one or more such groups. Social networking companies such as Facebook and LinkedIn sometimes suggest connections to you based on commonly identified affiliation groups.

So my hypothesis is that when you share multiple affiliation groups with someone, you are likely to have been more strongly connected to them through one than through others. For example, you might have gone to the same school and then worked together, but your interaction in school would have been so little that it almost doesn’t count. Yet, the school  remains as a common affiliation group.

Does it happen to you as well? Do you forget that you share an affiliation group with someone because it is not the “dominant” one, since you share another? And due to that do you miss out on making connections, and thus on opportunities?

I had this hilarious incident two weeks back where I was meeting this guy W with whom I share three affiliation groups – BASE (the local JEE coaching factory), IIT Madras and IIM Bangalore. Due to the extent of overlap and degree of interaction, I know him fundamentally as an “IITM guy”. And there’s this other guy X who I also know through three affiliation groups – BASE (again), IIM Bangalore (again) and a shared hobby (the strongest).

So I was talking to W and was going to bring up the topic of X’s work, and suddenly wondered if W knows X, so I said “do you remember X, he was in your batch at BASE?”. And then a minute later “oh yeah, you guys were classmates at IIMB also!”.

The rather bizarre thing is that I had completely stopped associating both W and X with IIMB, since I have much stronger affiliation groups with them. And then when I had to draw a connection between them, I even more bizarrely picked BASE, where I hadn’t interacted with either of them, rather than IIMB, where I interacted with both of them to a reasonable degree (X much more than W).

I know I didn’t do much damage, but in another context, not realising connections that exist might prove costly. So I find this “interesting”!

Is there anybody else in here who feels the way I do?

Pricing likes and the facebook algorithm

There is a good friend of mine who is a compulsive “LinkedIn liker”. Anything anyone in his network writes (either a LinkedIn blog or a status update or a job announcement), he is extremely likely to “like” them. While that helps the authors of such updates in getting their messages across to this guy’s networks also, the thing is that such likes add little value. If an update has come on my timeline because this guy has liked it, I’ll take it with salt since I know that this guy’s likes are “cheap”.

I don’t want to single out this guy, but there are several others on my Facebook friend list who are also compulsive likers. They like just about everything that they see, but the Facebook algorithm (by which not all of your updates are shared with all of your friends) means that their incidence is less than that of the LinkedIn liker. Then I have this one follower on Twitter who unfailingly likes each tweet of mine with a link. He engages in conversation very very sporadically, but like he does all the time!

So this got me thinking on the value of people’s likes, and what would happen if likes were to be rationed. I know it’s going to be hard to implement, but if you wee told that you had a quota of 10 likes that you could dole out in a day, how would you then ration your likes? Would such a cap make likes more valuable?

The reason this matters is that the number of likes has now become a metric that social media marketers track, and if some people’s likes are less valuable than others’, it is essentially a useless metric (and I know the problem is with the metric, not with likes). Even otherwise, from an information perspective, knowing the value of each person’s likes is useful for you in making up your mind on something!

So if say facebook decides that you get 10 free likes a day and have to pay for any more, how does that change your liking behaviour? For your 11th like, will you pay or go unlike something you’ve already liked? As a thought experiment, it is fascinating!

And while we are discussing Facebook, I must mention that I absolutely loathe its algorithm. I don’t know how it works, but it seems to me that the better updates that I put there just never get carried to my network, but some random updates that I sometimes put get propagated like crazy. I’ve been trying to reduce the number of updates there so that each update has a greater probability of getting propagated, but it just doesn’t seem to help!

And I was thinking about Facebook’s algorithm, and Twitter’s non-algorithm where every tweet you put gets carried to all your followers. Since Twitter doesn’t filter, all your followers have an opportunity to see all that you say. But the problem there is that since your followers see tweets of everyone on their timelines, your tweet is likely to get lost in the competition for attention.

So basically Twitter is like a free market where you have everyone’s tweets that get shown and compete for a follower’s attention. Facebook is like a more regulated market where there is no clutter, so every update gets undivided attention, but there is a Big Brother which decides who should see what!

I wonder if Facebook has considered making its algorithm public, and if it does, if it will have any impact on how people share. The value it will have for me is that at least I will know whether an update will get carried or not, and time and space my updates properly. But considering that one of Facebook’s revenue sources is to be paid by users to propagate their updates further, revelation of the algorithm will result in lower revenues for Facebook, so they’ll never do that.

I might just get all disgusted with the algorithm and quit Facebook some day.

What makes a Gencu successful?

Last evening I participated in a gencu with Cueballs and Zulu. First of all, let me explain what “gencu” is. It’s a term coined by the wife, and is short for “general catch up”. The reason she coined it was that for a while I was meeting so many people without any real agenda (I still do. Did four such meetings yesterday including the aforementioned) that she felt it deserves its own coinage.

So she would ask “what are you doing today?”. “Meeting this person”, I would respond. “Why?” would be the obvious next question. “No specific reason. Gen catch up”, I would respond.

I ended up saying “Gen catch up” so many times that she decided to shorten it to “gencu”, and we use the term fairly often now. This is the first attempt at publicising it, though. And no, unlike me, she still doesn’t do too many gencus.

So the thing with gencus is that you have no specific agenda, so if you don’t have anything to talk about, or don’t find each other particularly interesting, the meeting can quickly unravel. You can soon run out of things to talk about, and quickly you will start discussing who you are in touch with. So in that sense, gencus can have a high chance of failure (especially if you are meeting the counterparty for the first time or after a long time), and this is one of the reasons why the wife doesn’t do gencus.

One way of insuring against gencus going bad is to have more players. When you have three people, the chances of the gencu going bad are reduced (can’t be ruled out, but the probability decreases). In that sense, you get to meet two people at the same time with the insurance that you will not get bored. On the downside, if there is something specific that two of you want to talk about, you either have to shelve it or let the third person get bored.

While riding to another gencu after the one with Cueballs and Zulu (I must mention that none of the three of us felt the need for a third person to “insure” the gencu. Those two were planning a gencu openly on twitter and since I wanted to meet them both, I invited myself, that’s all!), I was thinking of what can make a multiparty (> 2) gencu successful. I was thinking of my recent multiparty gencus, and most of them had been pleasant and enjoyable, and never boring for any party.

The key to making a multiparty gencu successful, I realised, is mutual respect (ok I’m globing now, I admit). I’ve been through bad 3-way gencus too, and the problem with those has been that two of the three dominate, and don’t let the third person speak (a group discussion like atmosphere). Or two of three have a common interest or connection and speak too much about that, excluding the third person. Such meetings might be okay for one or two parties (among those that are dominating) but definitely uncomfortable for the third.

The above point had two people dominating the gencu at the cost of the third being a problem. Sometimes you don’t even need two people for that. One of the three people can simply hijack the whole thing by talking about themselves, or their pet topic, at the exclusion of the other two people (such people don’t really need counterparties for conversation, but still choose to attend multiparty gencus).

The network structure before the meeting is also important. In our case yesterday, we knew each other “pair-wise”, so it was a complete graph at the beginning of the meeting itself. Not all three-party gencus are like this, and it is possible for two people at one such gencu to not know each other before. This can occasionally be troublesome, since the law of transitivity doesn’t hold for people getting along with or liking people, so if A knows B and B knows C, there is no guarantee that A will get along with C. It can also happen that B will give more importance to talking to A than to talking to C (been affected by this from all three sides in the past). It might be hard to find stuff that everyone finds interesting, resulting in leaving out people. And so forth.

What about larger groups? Groups of five or bigger I’ve seen usually devolving into smaller groups (a notable exception was this one drinking session in late January, where we were 7 people and still had only one (excellent) conversation going), so they need not be analysed separately. Groups of 4 can work, but I prefer groups of 3 (maybe I’ll do a more rigorous analysis of this in a later post).

So what’s your experience with Gencus? What is the ideal number, and how do you go about it?

Useless LinkedIn

I’m not a big fan of LinkedIn. I mean, I use it, and fairly regularly at that (check it at least once a day), and I think conceptually it’s quite useful. However, in practice, I think there are a number of sticking points about the service, which makes it quite useless.

For starters its apps (iPad and Android) are quite lousy, and offer nowhere close to the kind of experience that the web interface offers. Things are extremely unintuitive (down to the tabbing order – you compose message, hit tab and enter, and you don’t send the message. It takes you to the profile of the person you’re messaging instead) on the website. Sometimes the apps show notifications even after you’ve checked them on the web, and so on.

In other words it’s an extremely poorly engineered product, but which is surviving (and thriving) thanks to network effects!

I might have commented on this in the past but there is this thing on endorsements. This was something that coincided with the time when LinkedIn went public (if I’m not wrong), and you could endorse people for their “skills” on LinkedIn. For a while I played along with the game. But then I completely lost it when a distant uncle who I’m sure has never traded derivatives endorsed me for “derivatives”. I quickly deleted my skills.

Then there are the LinkedIn recommendations, which has inherent selection bias and hence adds no value. And then you have the “say goncrats” feature, where LinkedIn prompts you to “say congrats” on people changing jobs or hitting job anniversaries. I’ve found this mildly useful (dropping a note when someone switches jobs is a good way to stay in touch), but there are the bugs in terms ofjob downgrades and people getting fired.

And of late, there has been serious spam in terms of people’s status updates. I don’t know when it became popular to post silly puzzles on professional networking sites, yet I find several of them popping up on my timeline every day, and the number of people who have shared each is not funny. Then you have these cartoons (Dilbert and the copycats), and “guru quotes” that appear in the form of images that further spam your timelines! The only way I can think of these being useful is that they act as a negative indicator when you’re checking out the profile of someone you are looking to hire or do business with!

To summarise, LinkedIn seems to be an extremely badly engineered product on several counts, but thanks to network effects (so many people are already on it that entry barriers for competitors are really high) the site still manages to do well! I wonder what it will take to disrupt it. Facebook for business is not the answer for sure – the potential havoc caused by a breach in chinese walls there will scare people enough to not sign up.

What do you think? Here is their stock price movement for reference:

 

 

Gossip Propagation Models

More than ten years ago, back when I was at IIT Madras, I considered myself to be a clearinghouse of gossip. Every evening after dinner I would walk across to Sri Gurunath Patisserie, and plonk myself at one of the tables there with a Rs. 5 Nescafe instant coffee. And there I would meet people. Sometimes we would discuss ideas (while these discussions were rare, they were most fulfilling). Other times we would discuss events. Most of the time, and in conversations that would be entertaining if not fulfilling, we discussed people.

Constant participation in such discussions made sure that any gossip generated anywhere on campus would reach me, and to fill time in subsequent similar conversations I would propagate them. I soon got to know about random details of random people on campus who I hardly cared about. Such information was important purely because someone else might find it interesting. Apart from the joy of learning such gossip, however, I didn’t get remunerated for my services as clearinghouse.

I was thinking about this topic earlier today while reading this studmax post that the wife has written about gossip distribution models. In it she writes:

This confirmed my earlier hypothesis that gossip follows a power law distribution – very few people hold all the enormous hoards of information while the large majority of people have almost negligible information. Gossip primarily follows a hub and spoke model (eg. when someone shares inappropriate pictures of others on a whatsapp group) and in some rare cases especially in private circles (best friends, etc.), it’s point to point.

 

For starters, if you plot the amount of gossip that is propagated by different people (if a particular quantum of gossip is propagated to two different people, we will count it twice), it is very well possible that it follows a power law distribution. This well follows from the now well-known result that degree distribution in real-world social networks follows a power law distribution. On top of this if you assume that some people are much more likely to propagate quantums of gossip they know to other people, and that such propensity for propagation is usually correlated with the person’s “degree” (number of connections), the above result is not hard to show.

The next question is on the way gossip actually propagates. The wife looks at the possibilities through two discrete models – hub-and-spoke and peer-to-peer. In the hub-and-spoke models, gossip is likely to spread along the spokes. Let us assume that the high-degree people are the hubs (intuitive), and according to this model, these people collect gossip from spokes (low degree people) and transmit it to others. In this model, gossip seldom propagates directly between two low-degree people.

At the other end is the peer-to-peer model where the likelihood of gossip spreading along an edge (connection between two people) is independent of the nature of the nodes at the end of the edge. In this kind of a model, gossip is equally likely to flow across any edge. However, if you overlay the (scale free/ power law) network structure over this model, then it will start appearing to be like a hub and spoke model!

In reality, neither of these models is strictly true since we also need to consider each person’s propensity to propagate gossip. There are some people who are extremely “sadhu” and politically correct, who think it is morally wrong to propagate unsubstantiated stories. They are sinks as far as any gossip is concerned. The amount of gossip that reaches them is also lower because their friends know that they’re not interested in either knowing or propagating it. On the other hand you have people (like I used to be) who have a higher propensity of propagating gossip. This also results in their receiving more gossip, and they end up propagating more.

So does gossip propagation follow the hub-and-spoke model or peer-to-peer model? The answer is “somewhere in between”, and a function of the correlation between the likelihood of a node propagating gossip and the degree of the node. If the two are uncorrelated (not unreasonable), then the flow will be closer to peer-to-peer (though degree distribution being a power law makes it appear as if it is hub-and-spoke). If there is very high positive correlation between likelihood of propagation and node degree, the model is very close to hub-and-spoke, since the likelihood of gossip flowing between low degree nodes in such a case is very very low, and thus most of the gossip flow happens through one of the hubs. And if the correlation between likelihood of propagation and node degree is low (negative), then it is likely to lead to a flow that is definitely peer-to-peer.

I plan to set up some simulations to actually study the above possibilities and further model how gossip flows!

What makes for a successful networking event?

So the second edition of the NED Talks took place last night. And no, this is not a “match report”. So the purpose of the NED Talks is to put together a bunch of interesting people who are interesting in different ways, and get them to talk. There are approximately ten talks (the first edition had thirteen, the second eight) followed by interaction, and a round of interaction before the event. So in certain ways, NED Talks are networking events.

So the question is what the ideal “network structure” of the networkers is in order to ensure a successful networking session. The idea is that the network at the beginning of the session needs to be only slightly dense – if there are too many people who know each other at the beginning of the session, there is not much of a point in the event as a networking event, for the value add in a networking event is to bring together people who hitherto didn’t know each other, and to strengthen existing weak ties.

The network being not dense enough also is also a problem, for that means that people might be lost. So if you have a lot of people who have never known each other earlier, and if some of them are introverted (as is likely to happen when you put together intelligent people), the conversation can be a bit of a non-starter. So low density is also not a good thing.

Then there is the issue of cliques – if you have a bunch of people who all know each other from earlier, then the others might feel “left out”, and not be able to get into the conversation. There are likely to be “in-jokes” and “in-stories” which everyone else finds irrelevant. I remember being  at one such gathering where I was the only person who was not thus “in”, and so I got up and announced that I was getting bored and walked out.

Anyway, so let us represent all attendees to a party or event in the form of a graph (undirected). Each vertex represents an attendee and two attendees are connected by an edge if they know each other from before. Given such a graph, can we construct an algorithm to “verify” if it is a good set of people to have for the party? Oh, and this is one of the insights from yesterday’s NED talks – computational complexity can be measured in terms of how “easy” it is to verify a given solution, rather than generate a new solution.

The first thing you can do is to find the size of the largest clique – if it exceeds a certain proportion (a third maybe? a fourth?) of the total number of attendees, it is a bad idea, for that means that this clique might dominate the conversation.

Then you can calculate the “edge density” of the graph – the total number of edges on the graph to the number of “possible edges” (given by NC2 where N is the number of attendees). For example, the edge density of the first NED Talks was 3/26 (largely due to 5 attendees who were not connected to any other nodes) . The edge density of the second NED Talks was 1/3 (might have been higher but a not-so-well-connected attendee backed out at the last moment). What range of edge density makes sense? Or should we use the variance in edge density also?

Then there is the number of “components” in the graph – if the graph is mostly disconnected, the group might split up into small cliques which might defeat the purpose of the networking event itself and lead to disconnected conversation. Note that nodes of zero degree don’t matter here – it’s components wiht at least two people.

And so forth. So can anyone help me build an algo to “verify” if a party / networking event is going to be good given a graph of who knows who from before?