BackgroundGraph theory has been widely applied to the studies in biomedicine such as structural measures including betweenness centrality. However, if the network size is too large, the result of betweenness centrality would be difficult to obtain in a reasonable amount of time.ResultIn this paper, we describe an approach, 1+ɛ lossy graph reduction algorithm, to computing betweenness centrality on large graphs. The approach is able to guarantee a bounded approximation result. We use GSE48216, a breast cancer cell line co-expression network, to show that our algorithms can achieve a higher reduction rate with a trade-off of some bounded errors in query results. Furthermore, by comparing the betweenness centrality of the original graph and the reduced graph, it can be shown that a higher reduction rate does not sacrifice the accuracy of betweenness centrality when providing faster execution time.ConclusionsOur proposed 1+ɛ lossy graph reduction algorithm is validated by the experiment results which show that the approach achieves a faster execution within a bounded error rate.

Influence analysis is one of the most important research in social network. Specifically, more and more researchers and advertisers are interested in the area of influence maximization (IM). The concept of influence among people or organizations has been the core basis for making business decisions as well as performing everyday social activities. In this research, we begin by extending a new influence diffusion model information diffusion model (IDM) using various constraints. We incorporate colors and additional nodes constraints. By adding colors and constraints for different types of nodes in a graph, we would be able to answer complex queries on multi-dimensional graphs such as ‘find at most two most important genes that are related to lung disease and heart disease’. More specifically, we discuss the following variations of IM-IDM; Colorblind IM-IDM, Colored IM-IDM and Colored IM-IDM with constraints. We also present our experiment results to prove the effectiveness of our model and algorithms.