# Greedy algorithm for maximum independent set

One more post of our GT CoA series. The introductory post is here. We skip the third talk, Lempel-Ziv: a “one-bit catastrophe” but not a tragedy because we have already covered this paper, see this post. The fourth talk of the meeting was about greedy algorithms for maximum independent set, presented by Mathieu Mari. Once again, the text will not deal much with the actual work of the speaker, but more with the background of it.

## Maximum independent sets are hard to find

Maximum independent set is an algorithmic problem, which asks to find the maximum set of nodes of the input graph such that not two nodes of the set are adjacent. As the problem is NP-hard, one naturally look for approximation algorithms. Unfortunately it is impossible to design a non-trivial polynomial-time approximation algorithm, unless you know what happens1.

On the other hand, these hardness results are for worst-case general graphs, and it is still interesting to study the behaviour of very simple algorithms. This post is about greedy algorithms.

## A greedy algorithm and when it fails

A greedy algorithm for maximum independent set is the following:

Until all nodes are labelled:
Choose an unlabelled node with the minimum of unlabelled neighbours;
Label this node with 1, and its unlabelled neighbours with 0;
Output the set of nodes labelled with 1.

The idea of choosing a node whose degree in the remaining unlabelled part of the graph comes from a simple intuition: if I choose a node with high degree, I will label a lot of nodes in one step, which is bad because it decreases a lot the number of nodes that are candidates to be in the independent set. But because of what we said before, we know that this has to fail sometimes. Here is an example. There are three sets of nodes : ${x}$, ${a_1,…., a_k}$ and ${b_1,…,b_k}$. The vertex $x$ is linked to every $a_i$, every $a_i$ is linked to every $b_j$, and the $b_j$ form a clique. On this graph, the algorithm will first pick $x$, and then pick a node in ${b_1,…,b_k}$, and stop. The independent set returned has size 2, but ${a_1,…., a_k}$ is an independent set of size $k$, therefore the approximation ratio is roughly the size of the instance.

## And when it succeeds

The first thing one can say about this graph, is that it uses very high degree nodes. What happens if we consider bounded degree graphs? Then for maximum degree $\Delta$, greedy achieves the approximation of ratio $\frac{\Delta+2}{3}$2, which is not that bad. A second thing one can say, is that this graph does not seem generic, for example it does not look like a random graph. And greedy is quite good on random graphs actually, it achieves a ratio 2 in expectation3.

Random graph are not that common in applications though, either on real-world graphs or on network architecture. What about trees, what about planar graphs? Once again greedy is a good choice: it provides a 6-approximation on planar graphs, and is optimal for trees.

There is a general method to prove that greedy is optimal for trees, and also on other graph classes, such as maximal outerplanar graphs, cographs, and split graphs. First note that if there is a node $v$ in the graph such that its neighbours and itself form a clique, then this node belongs to one of the maximum independent sets. Indeed only one node from the clique can be in the set, and we can always take the node $v$, because there cannot be a conflict with a node outside the clique. If the class of graphs at hand has the property that the nodes with the lowest degree have such clique neighbourhoods, then the greedy algorithm never makes a bad choice, and is optimal. In trees, the lowest degree nodes are the leaves, and they have such a property, because they have only one neighbour, thus the greedy algorithm is optimal.

## How good is greedy on a particular graph?

Ok, so greedy can be very bad, but it can also be good. And this can depend not only on the graph but also on the choices made by the algorithm : at every step there can be several nodes with minimum degree, and the choice can dramatically change the output. But can we predict how good or bad greedy can be? Can we know which execution of the algorithm will give the best outcome, without running it ? Mathieu and his co-author’s answer is basically “no”.

### Footnotes

1. Some precisions about the non-approximability results. The precise results is that it is NP-hard to get a $n^{1-\epsilon}$ approximation of the maximum independent set. It comes from the paper Clique is hard to approximate within $n^{1–\epsilon}$ by Johan Håstad and is proved with the PCP machinery. Note that the title of the paper is about the maximum clique problem and not the independent set, but for general graphs this is the same problem: just take the complement of the input graph.

2. See Greed is good: Approximating independent sets in sparse and bounded-degree graphs by Halldórsson and Radhakrishnan.

3. See On independent sets in random graphs by Coja-Oghlan and Efthymiou.