A recent question on Mathematics Stack Exchange asked about using a mixed integer linear program (MILP) to find a connected subgraph of a specified size (vertex count) within a given graph. Specifically, it asked how to formulate constraints to enforce the requirement that the subgraph be connected.

As I have previously mentioned, one way to force a graph to be connected is to add flows between vertices, treating one vertex as a source and the other vertices as sinks. In the context of the current question, that works as follows. Let $V$ and $E$ be the sets of vertices and edges respectively in our (undirected) graph and $M<\vert V \vert$ the number of vertices required in the connected subgraph. Note that we do not assume the parent graph is connected, and in fact the sample graph in the Math SE post is not. Since flows are inherently directional, we replace each edge $e=(v,w)\in E$ with two arcs, $(v,w)$ and $(w,v).$ Let $A$ be the set of all arcs.

We designate (arbitrarily) one vertex in the subgraph to be the "root" (source) vertex, with a supply of $M-1$ thingies. (I was going to say "honest politicians", but then the problem becomes infeasible, hence "thingies".) Each of the remaining vertices in the subgraph consumes one thingy. Thingies flow along arcs, but only if both endpoints are selected. Since $M-1$ thingies enter the network at the root vertex and each of the remaining $M-1$ vertices consumes one thingy, the only way for flow to be balanced is if the selected vertices form a connected graph.

For each vertex $v\in V,$ let $x_v \in \lbrace 0, 1 \rbrace$ be 1 if and only if vertex $v$ is selected and let $y_v\in \lbrace 0, 1 \rbrace$ be 1 if and only if vertex $v$ is chosen to be the root vertex. For each arc $a\in A,$ let $f_a \in [0, M-1]$ be the flow across arc $a.$ The objective function is immaterial, since we just want a feasible solution, so we will minimize 0. The constraints are as follows:

- The correct number of vertices must be chosen: $$\sum_{v\in V} x_v = M.$$
- One vertex must be designated the root: $$\sum_{v \in V} y_v = 1.$$
- The root vertex must be one of the chosen vertices: $$y_v \le x_v \quad \forall v\in V.$$
- The flow on any arc must be 0 unless both head and tail are among the chosen vertices: $$f_{(v,w)}\le (M-1)x_v \quad \forall (v,w)\in A$$ and $$f_{(v,w)}\le (M-1)x_w \quad \forall (v,w)\in A.$$
- The net flow out of any vertex (flow out - flow in) must be $M - 1$ for the source vertex, -1 for every other selected vertex and 0 otherwise: $$\sum_{(v,w)\in A} f_{(v,w)} - \sum_{(w,v)\in A} f_{(w,v)} = M\cdot y_v - x_v \quad \forall v\in V.$$

This works in a MILP model, but a MILP model is probably not the best way to solver the underlying problem. Consider the following algorithm, which is based on constructing a layered subgraph. Assume the vertices are in some order. Make the first vertex the root and create a graph containing just it (which we will call layer 0). Add up to $M-1$ vertices connected to the root by an edge (layer 1). If the graph now contains $M$ vertices, declare victory and stop. Otherwise, for each vertex in layer 1, find all vertices connected to it by an edge and not in layers 0 or 1 and as many as them as are needed to reach $M$ vertices (or all of them if you are still short). Repeat until done or until all vertices in layer 1 have been processed. The added vertices form layer 2 because they are two edges from the root. Repeat with each layer until either you have $M$ vertices (victory) or there are no vertices left to process.

If all connected vertices have been found and the count is less than $M,$ discard the current root and start over with the next vertex as root. Note that when processing subsequent roots, you can ignore any vertices already tried as root and just look at vertices later in the vertex ordering.

I ginned up some Java code (open source, as always) to test both the MILP model and the layered graph approach, using CPLEX as the MILP solver. Both got the examples from the Math SE post correct in a trivial amount of time. On a randomly generated graph with 5,000 nodes and 624,713 edges (about 5% of the edges in a complete graph that size), with a target subgraph size of 1,000 nodes, the MILP model took about 8 seconds to build and 12 seconds to solve. The layered graph search needed 3 milliseconds. I tried a larger graph, but my computer ran out of heap space trying to build the model (mainly due to the expanding number of flow variables and related constraints).

So there are two things to take away. One is that flows and flow balance constraints can be used to enforce connection among vertices, even when there is no actual thing flowing. The other is that, much as I like MILP models, sometimes simpler approaches are better.

## No comments:

## Post a Comment

Due to intermittent spamming, comments are being moderated. If this is your first time commenting on the blog, please read the Ground Rules for Comments. In particular, if you want to ask an operations research-related question not relevant to this post, consider asking it on Operations Research Stack Exchange.