Social Network Metrics and Idea Infection
May 3, 04:21 PM
Let’s say you want to hold an in-game event in your virtual world. In fact, let’s say you’re doing a sci-fi MMO, and you want people to know that the DCJ MegaCorp is giving away experimental weapons to anyone who shows up to their corporate headquarters and says the password “salamander.”
In my mind, there are three ways to go about doing this.
- Send an email to your user base, telling them where and when the event will take place.
- Post to your forums with the same information.
- Spread the idea through word of mouth.
Most people know how to do #1 or #2, but… how do you organize #3? It’s actually not that difficult, provided you’ve been collecting player metrics on how your social networks are organized.
Social Network Metrics
To collect good metrics on your game’s social networks, you really don’t need much more than the following table:
| Player1 | Player2 |
| Alice | Bob |
| Alice | John |
| Alice | Mary |
| Bob | Alice |
| Bob | Lisa |
| ... | ... |
That’s it. It’s just people’s friends lists. If Alice has Bob on her buddy list, create a row for them. Tada. Now, you can always augment this if your game has types of relationships or something, but this is almost all you need, at least for the purposes of viral marketing.
Given a table like the one above, you can do a simple SQL query on it to figure out who has the most friends. Now take the top 50 people out of that list, and tell them about your event. But make it cool. Since you’re only contacting 50 people, you can probably put a customer service or OCR rep on the task, and have them cook up a neat rabbit-hole style invitation. Disguise the admin as something cool in your game, a big mech assassin or whatever, and then hand them a little script and give them some room to improvise. Make it fun! But most importantly, make it so that you really leave a big impression on the 50 people you contact.
Once you’ve done that, 50% of your game world will know about the event in no time!
Don’t believe me, huh?
Some Simulation Models
I wrote up a simulation in Prefuse, the most excellent graph visualization toolkit in the world (it’s free, too!). The first thing it does is generate a random social network. My algorithm is along these lines:
- Given N nodes (people), divide those into social groups of 1 to 40 people (uniform distribution).
- Give each person within those social groups on average 7 close friends within the group (0 to 14, uniform distribution)
- Give each person on average 2 random acquaintances
That’s it. It’s not a perfect network generation algorithm by any means, but what it does is it models the fact that human networks consist of cliques (in MMOs they typically max out at 40 people or so in a given guild) where everyone has a high chance of knowing everyone else. And then I throw in some randomness because in virtual life, as in real life, we often pick up random friends for no good reason.
I was happy to see that the graphs it generated actually looked more or less like the real social network graphs I used to visualize when I was working on large-scale MMOs. Here’s a small graph (200 nodes) which visualizes nicely. (Once you get to about 500 nodes or more, visualizations turn into spaghetti.)
The Simulation
Now what better to do with a social network graph than to run simulations on it? I wrote a quick-and-dirty sim which takes 4 parameters.
- Threshold is the minimum number of friends you need to have in order to be considered a viable target of our viral marketing event.
- Spread1, Spread2, and Spread3 are probabilities. Spread1 tells you how likely the person you contact will tell any given one of her friends. Spread 2 is the probability that those secondary people will tell their friends. And Spread3 is the probability that those friends will tell their friends.
So Threshold defines how many players we are contacting, and the SpreadX parameters define how “strong” our viral marketing is. I decided to limit it to 3 layers of friends, to keep things simple.
In our simulation, we assume that there’s an 80% the person your admin contacts will tell their friends, there’s a 40% chance that their friends will do so, and a 20% that the people who heard it third-hand will do so.
The Findings
In a network where people have on average 7 close friends and 2 random friends, we found that you don’t need to talk to very many people before word gets around.
In this table, the first column is the number of people in the sim, the second is the number of people that are contacted directly by an admin, and the third is the percent of the total population that gets exposed to your event.
| Population | # Contacted | % Exposure |
| 1000 | 7 | 30 |
| 2000 | 6 | 20 |
| 3000 | 10 | 20 |
| 4000 | 8 | 12 |
| 4000 | 21 | 26 |
| 4000 | 100 | 61 |
| 5000 | 9 | 12 |
| 5000 | 24 | 26 |
| 5000 | 59 | 44 |
| 5000 | 140 | 66 |
These numbers might not look so great. But consider this. My friends in marketing tell me that if you send an email to all of your paid subscribers you can expect about a 5% return on that email. So that’s 100% exposure (via spam), and a 5% return. But I would wager that while you might only be getting exposure to 10% or 30% of your population through this seeded word of mouth, those people who hear about it will be
- more likely to attend your event
- more likely to ask around about future cool events
- more likely to attend future events if they end up being the first one contacted instead of just hearing about it
In the case of getting 30% exposure, I still think you’d get about a 5% attendance rate to your event, out of the cool factor alone.
Now, I personally think these exposure numbers are low, that these ideas will spread better and faster than our simulation indicated, for the following reasons:
- We limited ourselves to three layers of word of mouth. In practice, it’s unlimited, although with diminishing returns (usually).
- The 80/40/20 ratio might be better or worse, depending on how cool your seed event is. We’d like to think you can do better.
- We don’t account for the simple fact that as the worlds get bigger (more nodes), people will have more random acquaintances. We kept it at a firm 2 acquaintances all the way through, when really we should have been increasing the number along with the total node count.
In fact, if we go into the sim and take our 3000 node network and increase the average number of acquaintances from 2 to 4, while seeding the same number of people (10), we jump from 20% exposure to 35% exposure! Just goes to show: in graph theory, edges mean everything.
So What?
If you’re an OCR (Online Community Rep), you already know all this. A well-run in-game event that targets the right people will spread like wildfire around the game world. Nothing new here.
Except that metrics can help you pick who you contact initially. You can use metrics to identify the pressure points of your social network, letting your in-game event get the maximum impact for the minimum effort.
