A dynamic and context-aware social network approach for multiple criteria decision making through a graph-based knowledge learning

Complexity and dynamics characterize a social network and its processes. Social network analysis and graph theory could be used to describe and explore the connectedness among the different entities. Network dynamics further increases the complexity, as each entity with its personal knowledge, cognitive and reasoning capabilities, thinks, decides and acts in a social network, characterized by the heterogeneity of nodes and ties among them. Social network analysis becomes critical to the decision-making process, where a network node will consider both its personal knowledge and the influences received from its neighbors. Network dynamics and the node's context-awareness affect the relationships among criteria, modifying their ranking in a multiple criteria decision-making process, and hence the decision itself. Thus, the main aim has been to model the decision-making process within a social network, considering both context-awareness and network dynamics. Moreover, we have introduced a process of knowledgetransfer, where the criteria are represented by the knowledge-related values.