I took some time to explore the Studivz wall posts dataset, to see whether it would be useful to use.

The first step was to extract a sub-set of the data, as the entire dataset is a little large to run in LocationSim (some of the CFAs can’t quite handle 28k nodes), scaling it up to this many nodes is a work in progress (it might involve writing something more lightweight to do some of the raw processing).

The approach I have taken so far, is to pick N nodes randomly, and included their immediate neighbours. I do the L more times to get the nodes a depth of L hops away from the source. Using 10 random nodes, with a depth of 2 yields a network of around 3049 nodes (~10% of all nodes). When reduced to 5 seed nodes, we get ~1000 nodes (~4%). Going the other way, 100 seed nodes, with a depth of 1 gives 14571 nodes covering ~50% of the network. These figures change depending on which nodes are selected at random initially. Two other paramters affect the results of this, the first is a threshold, where nodes with a connnected time less than this are not included, the second is the value used to seed the random number generator (if 0, then automatically choose a seed).

In the end I settled on three parameters in the table below – note that the number of nodes in the final set is highly subjective to the initially chosen nodes, so this is very random.

** **

**
**## Studivz Random Node Choices

N |
L |
# Nodes |

3 |
2 |
213 |

4 |
2 |
914 |

10 |
2 |
3049 |

**
**

Interestingly, despite the source or seed nodes being picked at random, the entire graph is connected in all configurations, the graphic below shows the connected_time graph and InfoMap clusterings for N=3, L=2.

InfoMap clustering of Studivz dataset, where N=3 and L=2

This is a promising start, since there are distinct clusters of nodes, which we expected, as this is the concatenation of three egocentric networks, but also there are connections between each egocentric network, meaning there is a route to every other node. However, we can’t tell from this graph how often these contacts occur.

Looking at the whole dataset, we can get an idea about how active it is over time by measuring the number of connections in a given time period, below show the number of weekly connections for the entire dataset.

Weekly number of connections in the Studivz dataset

It shows that this social network seems to have become increasingly popular over time, with a peak of just over 10,000 wall posts made in Jan 2007. If we were to pick a period to concentrate on, it should probably be from October 2006 onwards.

### Studivz N=3, L=2

Initial results for each metric are shown below:

Delivery Ratio for BubbleH vs BubbleRAP for Studivz 3 2 0 0

Cost for BubbleH vs BubbleRAP for Studivz 3 2 0 0

Latency for BubbleH vs BubbleRAP for Studivz 3 2 0 0

Delivery ratio is very poor for all runs, to see what the maximum possible delivery ratio is, we can look at the results for flooding the network below:

Delivery Ratio plot of Unlimited Flood on Studivz 3 2 0 0

This achieves a delivery ratio of roughly 65 percent, so we have a bit of work to do to be able to match this!

### Studivz 4 2 0 0

When we add another nodes to the initial seed set, we get a step up in the total number of nodes, 914 to be exact, this is currently running through the simulator.

Studivz 4 2 0 0

** UPDATE:**

Below is the weekly activity during the set using 914 nodes (4,2,0,0)

Weekly activity in STUDIVZ 4,2,0,0

The results on the larger dataset are shown below, these runs were taking considerably longer, and highlighted a couple of minor bugs in the simulator (not closing files properly! which means that file not found, too many open files messages kept occurring).

Delivery Ratio, Cost, Latency, Average Delivered Hops and Average Undelivered Hops for STUDIVZ with 4 seed nodes and a depth of 2.

We see here that BubbleH is doing well in terms of delivery ratio compared to bubbleRAP , link clustering, which created a huge number of communities does particularly well (at ~3o% for BubbleRAP and BubbleH), this adds weight to the idea that a large number of communities does well, and in fact, (in this case only, where there is only on set of parameters) we see that the Average cost is roughly the same as with the other CFAs. BubbleH also performs well in terms of cost. Latency very high for all CFAs as the dataset is very long.

Unlimited Flood and Prophet on STUDIVZ 4 2 0 0

However, we see from the Unlimited flood run, that we have a way to go to match the best possible delivery ratio, at around 90% delivery ration, it beats BubbleH hands down. Some consolation though, the advanced Prophet algorithm also only gets around 52% delivery ratio.