Student Blog: Introducing Bipartite Graphs in Alga

Posted on May 29, 2019 by Vasily Alferov (permalink)

I am a student at Summer of Haskell this year and I am very thankful to authors of this site who provided me this platform to have a blog about GSoC. This is an introduction post about my project. I am planning to write regular posts on my progress and when I find something interesting.

The idea of the project was on the ideas list published earlier. Two of us were accepted for this project, the other one being Adithya Kumar and who will be doing the work described on the ideas list. He told me his GSoC blog will probably be here.

My task is to introduce bipartite graphs to Alga and that is what I am going to tell you about now.

Algebraic graphs

There are three common ways to represent graphs in computing:

Adjacency matrix

Adjacency lists

Edge lists.

All three of them have their advantages and disadvantages. The most commonly used is the adjacency lists approach: that is storing a list of neighbors for each vertex. In fact, I can think of only one common algorithm for which this approach is not perfect: it is Kruskal’s algorithm for finding the minimum spanning tree.

However, the problem is that feeding graphs formed this way to algorithms is not always safe. For example, if the algorithm is designed for bidirectional graphs, it may rely on the fact that if some vertex u is in the list of neighbors of some another vertex v then v is in the list of neighbors of u .

A traditional solution for functional programming would be to guarantee the consistency of input data for the algorithm by taking a representation of the graph that would not allow a wrong graph to be passed. That’s what we call type safety.

Alga is a library that provides such a safe representation with a beautiful algebraic interpretation. It also has a nice set of algorithms out of the box. You can find the paper on Alga by its author here, I’m just going to provide some basics.

Consider the following definition for the graph data type:

data Graph a = Empty | Vertex a | Overlay ( Graph a) ( Graph a) a) (a) | Connect ( Graph a) ( Graph a) a) (a)

The constructors mean the following:

Empty constructs an empty graph.

constructs an empty graph. Vertex v constructs a graph of single vertex labeled v .

constructs a graph of single vertex labeled . Overlay g h constructs a graph with sets of vertices and edges united from graphs g and h .

constructs a graph with sets of vertices and edges united from graphs and . Connect g h does the same as Overlay and also connects all vertices of g to all vertices of h .

One can easily construct a Graph of linear size having a list of edges of the desired graph. In fact, this approach may even save memory for dense graphs comparing to adjacency lists. And this approach is surely type safe in the sense described above. Comparing to adjacency lists, there is no problem with an edge not present in the list of neighbours of another vertex. Another possible problem with adjacency lists not present here is that an edge might lead to a vertex with no associated adjacency list.

Why algebraic? Well, if we write down simple laws for these graphs we will see that laws for Connect and Overlay operations are very similar to those for multiplication and addition in a semiring, respectively.

This was just a brief description of Alga. There are many other parts not covered here. One example is that Graph might also be provided as a type class rather than a data type. This approach is much more flexible.

Bipartite graphs

An important part of Alga is providing different type-safe representations for different kinds of graph. For example, one for edge-labeled graphs was introduced last year.

Another option is to add a representation that restricts the set of possible graphs. One example from the ideas list is to represent only acyclic directed graphs. This is what Adithya will be doing. And my task for the first evaluation period is to provide bipartite graphs.

We often meet bipartite graphs in real world: connections between entities of different kinds are common. For example, graph of clients and backends they use is bipartite. Another example I can think of is about content recommendation systems: graph of users and films or songs they like is bipartite, too.

There are many ideas on how to do so. For example, in my proposal I suggested an approach that seems to match Alga’s design:

data Bigraph a b = Empty a b | LeftVertex a | RightVertex b | Overlay ( Bigraph a b) ( Bigraph a b) a b) (a b) | Connect ( Bigraph a b) ( Bigraph a b) a b) (a b)

Here, Connect only connects left vertices to the right. As my mentor Andrey figured, there is an interesting addition to the laws: (LeftVertex u) * (LeftVertex v) = (LeftVertex u) + (LeftVertex v) . Of course, the same holds for the right vertices.

By now, we agreed that first, I will focus on implementing adjacency maps for bipartite graphs (hey, didn’t I mention that Alga uses adjacency maps on the inside?). It doesn’t make much sense to make a separate algebraic representation, but I may do it if I find something interesting in it.

Now, the first task is to implement the conversion function, which I’m going to start right now. This implementation will simply ignore the edges between vertices of the same part.

fromGraph :: Graph ( Either a b) -> Bipartite.AdjacencyMap a b a b)a b = undefined fromGraph

With this stub, my summer-long dive into Haskell begins!

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