Community-Consideration Centrality, a Case Study of Lung Cancer Proteins

One of the most recent studies on the analysis of complex systems is to understand the role of community structure and centrality in analyzing the networks of complex systems such as protein and social networks. Traditional measures of centrality – degree centrality, closeness centrality, and betweenness centrality – cannot capture how community structures within these networks configure them. In this regard, we propose a new community-consideration centrality method to fill this gap. This method includes a weight of consideration, α, ranging from 0.0 to 1.0, to balance the focus between community and network-wide importance in the centrality calculations. Our analysis of two zachary karate and dolphin datasets shows that including community consideration in the degree, closeness, and betweenness centrality measures accurately captures the proportional significance of both communities and networks. In particular, for the lung adenocarcinoma cancer protein case study, our method not only identified more cancer hallmark genes than the traditional centrality measures without considering communities but also outperformed several other advanced centrality algorithms regarding the detection of crucial cancer-related genes. A balanced objective between network and community impacts was observed at an optimum performance α values of 0.1 and 0.2. It finds a strong significance of community structure in network analysis and features a more nuanced perspective on centrality in complex systems.