Simulation of Network Growth Using Community Discovery in Biological Networks

Abstract

Interactions between proteins in a cell can be modeled as a graphical network. The problem addressed in this paper is to model the network evolution in biological networks in order to understand the underlying mechanism that morphs a normal cell into a disease (cancer) cell. In this paper, concepts from social networks are utilized for this purpose. Though many models for network evolution exist in the literature, they have not been applied in the context of evolution of normal cell into a disease state. In this work, target network is evolved in two ways: (i) starting from common subgraph of the normal and cancer networks and (ii) using a divide and conquer approach, the network is grown from communities using preferential attachment models. Triadic model yields good performance with respect to the global characteristics, but actual edge prediction performance is very low when applied on the entire network. In the case of community approach, the results of edge prediction for two dense communities are satisfactory with precision of 62% and recall 62%. Since edge prediction is a challenging problem, the approach needs to be refined further so that it works for small and sparse communities as well before it can become a full-fledged algorithm.