Robust Community Detection Methods with Resolution Parameter for Complex Detection in Protein Protein Interaction Networks

Abstract

Abstract. Unraveling the community structure of real-world networks is an important and challenging problem. Recently, it has been shown that methods based on optimizing a clustering measure, in particular modularity, have a resolution bias, e.g. communities with sizes below some threshold remain unresolved. This problem has been tackled by incorporating a parameter in the method which influences the size of the communities. Methods incorporating this type of parameter are also called multi-resolution methods. In this paper we consider fast greedy local search optimization of a clustering objective function with two different objective functions incorporating a resolution parameter: modularity and a function we introduced in a recent work, called w-log-v. We analyze experimentally the performance of the resulting algorithms when applied to protein-protein interaction (PPI) networks. Specifically, publicly available yeast protein networks from past studies, as well as the present BioGRID database, are considered. Furthermore, to test robustness of the methods, various types of randomly perturbed networks obtained from the BioGRID data are also considered. Results of extensive experiments show improved or competitive performance over MCL, a state-of-the-art algorithm for complex detection in PPI networks, in particular on BioGRID data, where w-log-v obtains excellent accuracy and robustness performance.

The relative vertex clustering value - a new criterion for the fast discovery of functional modules in protein interaction networks

“…We used the overlapping score function given in [12,25,29,35]. The overlapping score, O ( C, K ), between a discovered complex C and a known complex K is defined as:…”

A Fast Agglomerative Community Detection Method for Protein Complex Discovery in Protein Interaction Networks

“…Though Q is widely used, it is known to have serious limitations which has been discussed at length in [5]. The second partition scoring function we used has been introduced in [10] and is defined as…”

“…We could not find complexes for the remaining species in due time. We proceeded similarly to Laarhoven et al [10] and considered only the known complexes (i.e., not those obtained by computational means) containing at least three proteins. Since FAC-PIN generates non-overlapping communities, we considered only complexes which are at the bottom of the MIPS hierarchy of complexes and subcomplexes.…”

“…The validation proceeds by determining the degree of overlap between the communities identified by FAC-PIN and the protein complexes; that is, we want to determine how effectively a community matches a known complex. We used the overlapping score function given in [2,3,10,19]. The overlapping score, O(C, K), between a community C and a known complex K is defined as:…”

“…We have a perfect match only when O(C, K) = 1. Threshold value τ = 0.2 was used in [2,3,19] whereas [10] used τ = 0.25. We used both values of τ in our complex validation.…”