## The Eternal Struggle between Partitions and Overlapping Coverages

New year, old topic. I could make a lot of resolutions for this new year, but for sure to stop talking about community discovery is not among them. At least this time I tried to turn it up a notch in the epicness of the title. My aim is to give some substance to one of the many typical filler phrases in science writing. The culprit sentence in this case is “different application scenarios demand different approaches”. Bear with me for a metaphoric example.

When presenting a new **toaster**, it is difficult to prove that it toasts everything better under any point of view, under any circumstances. It usually does most toasts okay, and for **one kind** of toasts it really shines. Or its toasts really suck, but it can toast underwater. That’s fine. We are all grown up here, we don’t believe in the **fairy tales of the silver bullets** any more. At this point, our toaster salesman is forced to say it. “Different application scenarios demand different approaches”. In some cases this is a shameful fig leaf, but in many others it is simply true. Problem is: **nobody really checks**.

I decided to check. At least one of them. Teaming up with Diego Pennacchioli and Dino Pedreschi, I put the spotlight on one of the strongest dichotomies in community discovery. As you may remember, community discovery algorithms can **force every node to belong to just one community**, or allow them to be **in many of them**. The former approach is called “graph partitioning”, whilst the latter aims to find an “overlapping coverage”. Are these two strategies yielding interesting, yet completely different, results? This question has been dissected in the paper: “Overlap Versus Partition: Marketing Classification and Customer Profiling in Complex Networks of Products“, that will be presented in one workshop of the 2014 edition of the International Conference of Data Engineering. Let me refresh your mind about overlaps and partitions.

Above you have the *nec plus ultra* scenario for a partitioning algorithm. If a partitioning algorithm sees the graph on the left, it would just **die of happiness**. In the graph, in fact, it appears very clearly that each node belongs to a very specific community. And it can’t belong to any other. If we assume that our algorithm works on edge strength (e.g. the inverse of the edge betweenness), then what the algorithm **really sees** is the graph on the right. It then proceeds to group together the nodes for which the edge strength is maximal, *et voilà*.

Here we have an example that’s a bit more complex. The picture has too many overlapping parts, so let me describe the connection pattern. In the graph on the left there are several groups of 6 nodes, each node connected to all other members of the group. In practice, **each diagonal is completely connected to the two neighbouring diagonals**. Clearly, here there is no way we can put each node in a disjoint group. Why put together nodes 0,1,2 with 3,4,5 and not with 9,10,11? But at that point, why 9,10,11 should be in a community with them and not with 6,7,8? The correct approach is just to **allow every completely connected group to be a community**, thus letting nodes to be part of more than a community. Some overlapping algorithms see the graph as it has been depicted on the right, with an edge colour per densely connected group.

Time to test which one of these approaches is **The Right One**! For our data quest we focused on supermarket transactions. We created a network of products that you can buy in supermarkets. **To be connected, two products have to be bought together by the same customers in a significant number of times**. What does that mean? By pure intuition, bread and water aren’t going to be connected: both of them are bought very frequently, but they have little to do with each other, thus they are expected to be in the same shopping cart **by chance**. Eggs and flour are too very popular, but probably **more than chance**, since there are a lot of things you can do with them together. Therefore they are connected. Other specific pairs of products, say **bacon flavoured lipstick** and **liquorice shoelaces**, may ended up in the same, quite weird, shopping cart. But we don’t connect them, as their volume of sales is too low (or at least I hope so).

Here are some of the facts we found. **First**. The overlapping approach* tends to return **relatively more communities with a larger amount of nodes** than the partition approach**. In absolute terms that’s obvious, since the same node is counted more than once, but here the key term is “relatively”. See the plot above on the right, where we graph the probability (y axis) of finding a community with a given number of nodes (x axis). **Second**. The overlapping approach returns more “messy” communities. Our **messiness** measure checks **how many different product categories are grouped together on average in the same community**. Again, **larger communities are expected to be messier**, but the messiness measure that we used controls for community size. See the plot on the right, again the probability (y axis) of finding a community with a given entropy (x axis, “entropy” is the fancy scientific term for “messiness”). **Third**. The partition approach returned denser communities, whose link strength (the number of people buying the products together) is higher.

What is the meaning of all this? In our opinion, the two algorithms are aiming to do something completely different. The partition approach is aiming to create a new **marketing classification**. It more or less coincides with the established one (thus lower messiness), most customers buy those products together (high link strength) and there are very few giant categories (most communities are small). The overlapping approach, instead, wants to do **customer profiling**. A customer rarely buys all products of a marketing category (thus increasing its messiness), it has specific needs (that not many people have, thus lowering edge weight) and she usually needs a bunch of stuff (thus larger communities, on average).

Who’s right? That’s the catch: **both**. The fact that two results are incompatible, in this case, does not mean that one is right and one is wrong. They are just different applications. Which was exactly what I wanted to prove, in this narrow and very specific, probably unsurprising, scenario. Now you should feel better: I gave you a small proof that the hours you spend to choose the perfect toaster for you are really worth your time!

* As overlapping approach, we used the Hierarchical Link Clustering.

** As partitioning approach, we used Infomap.

“That’s fine. We are all grown up here, we don’t believe in the fairy tales of the silver bullets any more. At this point, our toaster salesman is forced to say it. “Different application scenarios demand different approaches”. In some cases this is a shameful fig leaf, but in many others it is simply true. Problem is: nobody really checks.”

As someone finishing up a Masters broadly based on overlapping community detection, thanks for verbalising my internal monologue :). Even if i try two different overlapping community detection algorithms with the same datasets, you get two wildly divergent results!

Glad to hear that! After a phd and a postdoc I can also confirm you that it does not get any better! 🙂