This page provides supplementary information for the following paper:

D.A. Bader and K. Madduri. A graph-theoretic analysis of the human protein-interaction network using multicore parallel algorithms. Parallel Computing, 34(11):627--639, 2008.

Protein interaction networks play an important role in understanding the functional and organizational principles of biological processes. Promising computational techniques for key systems biology research problems such as identification of signaling pathways, novel protein function prediction, and the study of disease mechanisms, are based on topological characteristics of the protein interactome. Several complex network models have been proposed to explain the evolution of protein networks, and these models primarily try to reproduce the topological features observed in yeast, the model eukaryote interactome. We study the structural properties of a high-confidence human interaction network, constructed by assimilating recent experimentally derived interaction data. We identify topological properties common to the yeast and human protein networks. A novel contribution of the work is the analysis of the degree-betweenness centrality correlation in these networks. Jeong et al. empirically showed that betweenness is positively correlated with the essentiality and evolutionary age of a protein. We observe that proteins with high betweenness centrality, but low connectivity are abundant in the human PIN.

We analyzed protein interaction data primarily from HPRD in this study. Here (gzipped txt file) is the undirected graph corresponding to the largest connected component in this dataset (HPRD Release 6, Jan 2007). Please contact Kamesh Madduri if you would like access to additional data presented in the analysis sections of the paper.

The multicore algorithms designed for this study are now available as part of the SNAP graph analysis framework.