Eigenvector Centrality of Social Network and Choquet Integral Under 2-Additive Fuzzy Measures for Group Decision Making

Social networks capture the intricate relationships among individuals in a group. Existing methods on social network of decision makers (DMs) often relay on the numbers of connections each DMs has, not adequately account the quality of those connections. The Eigen ceneratlity measure the node degree through numbers of connections as well as their quality. Thus, the interactive relationships among DMs need to be explored through interaction indices with eigen centrality of each DM. To address this, we proposed a new framework that integrates eigenvector centrality with Möbius transforms under 2-additive fuzzy measures for evaluating the interactive indices among experts. The pair interactions are computed by performing equilibrium optimizer for the obtained inequalities. Further, the 2-additive Choquet integral along with strict t-norms based operations for q-rung orthopair preferences and consensus degrees is implemented to form a cohesive group decision making model.