Fuzzy graph theory is an important area of research that is the backbone of representations of any network with ambiguity. Crisp graphs are not sufficient to capture the uncertainty of parameters in networks, for example, strong relationships, effective persons, and influential or popular persons in social networks. These linguistic variables are perfectly represented in fuzzy graphs.

Social networks have large amounts of data with ambiguity and can thus be characterized by fuzzy graphs. Measuring centrality and link prediction or link deletion are essential tools for any kind of social network. There are many challenges in the field, such as capturing influential nodes and predicting missing links and comparing with non-existent links. Centrality measurements depend on both direct and indirect followers/ links, but how to best capture the nature of the direct or indirect links is currently being researched.

This aim of this Special Issue is to focus on problems including the effects of overlapped associated networks on the nodes of primary networks in centrality measurements, fuzzy graphs related to social network representations, and new techniques to measure centralities and link prediction or link deletion. Other extensions of fuzzy graphs, including neutrosophic graphs, bipolar fuzzy graphs, and intuitionistic fuzzy graphs with applications on social networks, will also be considered, as will the theoretical and practical topics relating to centrality and link prediction. We welcome both original research and review articles.

Potential topics include but are not limited to the following:

• Fuzzy graph theory
• Social networks with fuzzy parameters
• Tools and techniques for centrality measurements in social networks
• Methods of link prediction in social networks
• Centrality and its variations in fuzzy social networks based on fuzzy hypergraph theory
• Influential nodes in social networks
• Overlapping and associated network effects on centrality
• Churn prediction using fuzzy techniques
• Measuring the falsity of central nodes/influential nodes using neutrosophic graphs
• Social networks and m-polar fuzzy graphs
• Social networks and intuitionistic fuzzy graphs
• Decision making in the detection of central nodes