Graph Theory and Its Applications in Social Networks with Uncertain Parameters

Dear Colleagues,

Recently, fuzzy graph theory has been the backbone of the representations of all ambiguous networks, representing an important field of research. Crisp graph theory is not sufficient to reflect the uncertainty of network metrics such as strong linkages, efficient people, and influential people in social networks. The linguistic variables in these fuzzy graphs are correctly represented. There are different types of graph theory extensions which represent ambiguity. Some of the examples include bipolar, hesitant, neutrosophic, and m-polar fuzzy graphs. These graphs represent many networks with ambiguity well. All theoretical and practical subjects (applications) will be covered in this Special Issue. In particular, such graph deviations need to be identified effectively.

Furthermore, this Issue will cover subjects, centralities, and link prediction for interconnected/overlapping social networks. There are numerous problems in the field. The topics to be covered include capturing influential nodes, predicting missing linkages, and making comparisons with non-existent links. In addition, many types of fuzzy graphs must be generalized so that a representation of the network can be performed using such diagrams. The primary problems of this particular topic are general fuzzy resource development framework (RDF) graph models to capture the fuzziness of vertices and edges and formalize fuzzy graphs and graph matches.

The following list provides a summary of the topics of interest to this issue. This list is not exhaustive; related articles may be submitted.

Dr. Sovan Samanta
Guest Editor

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• graph theory and representations of complex networks
• fuzzy graph theory
• social networks with fuzzy parameters
• centrality measurements in social networks
• link predictions in social networks
• bipolar fuzzy graphs
• m-polar fuzzy graphs
• intuitionistic fuzzy graphs
• neutrosophic graphs
• resource development framework with uncertainty
• fuzzy decision making in web frameworks as social networks