Previous studies on risk management in Electrical Power Infrastructure Systems (EPIS) have focused mainly on the identification of risks as discrete events and on how they impact the service performance of the systems. The aforementioned studies are biased toward an inward-looking perspective that treats risks as isolated events without considering the role of stakeholders who might be affected if those risks occurred. However, in reality, most EPIS risks are interrelated, and have internal or external stakeholders who have varying levels of connectivity. To address this gap, a novel approach is proposed in this study that combines two methods—Social Network Analysis (SNA) and a new modified version of fuzzy cross-impact matrix multiplication applied to classification (FMICMAC)—for the modeling and analysis of interrelationships among stakeholder-associated risks in the EPIS. The visualization feature of SNA presents the decision-makers with a holistic view of the hidden interrelationships among risks that go unnoticed via using traditional practices. Selected metrics of SNA, namely in-degree centrality and out-degree centrality, as well as the MICMAC metrics, namely dependence power and driving power, have been used to classify risks into categories that account for the likelihood of occurrence and its mutual impact. Consequently, decision-makers can differentiate between the dependent and independent risks to identify and visualize their mutual relationships, thereby determining the hierarchy of criticality of the risks involved. The proposed approach also integrates the identification of propagation paths associated with critical risks. The significance and novelty of the methodology lie in its application in developing an optimal plan for the alleviation of stakeholder-associated risks in EPIS. This study makes several contributions to literature. It is the first study that integrates SNA with a new modified version of FMICMAC analysis In addition to its several inherent strengths, which include