Modularity and partially observed graphs

TL;DR

This paper investigates how partial observations of a large graph affect the estimation of its modularity, showing that under certain conditions, observed modularity closely reflects the true modularity, with implications for sampling methods.

Contribution

The paper provides theoretical bounds on how well observed modularity estimates the true modularity in partially observed graphs under random and vertex sampling models.

Findings

High modularity in the underlying graph implies high observed modularity under random sampling.

Sampling vertices in dense graphs allows accurate modularity estimation, unlike in sparse graphs.

Under-sampling can lead to overestimating the graph's modularity.

Abstract

Suppose that there is an unknown underlying graph $G$ on a large vertex set, and we can test only a proportion of the possible edges to check whether they are present in $G$. If $G$ has high modularity, is the observed graph $G'$ likely to have high modularity? We see that this is indeed the case under a mild condition, in a natural model where we test edges at random. We find that $q^*(G') \geq q^*(G)-\varepsilon$ with probability at least $1-\varepsilon$, as long as the expected number edges in $G'$ is large enough. Similarly, $q^*(G') \leq q^*(G)+\varepsilon$ with probability at least $1-\varepsilon$, under the stronger condition that the expected average degree in $G'$ is large enough. Further, under this stronger condition, finding a good partition for $G'$ helps us to find a good partition for $G$. We also consider the vertex sampling model for partially observing the underlying graph: we find that for dense underlying graphs we may estimate the modularity by sampling constantly many vertices and observing the corresponding induced subgraph, but this does not hold for underlying graphs with a subquadratic number of edges. Finally we deduce some related results, for example showing that under-sampling tends to lead to overestimation of modularity.