Guest Editorial Special Issue on Computational Intelligence in Big Graph Data Management

The papers in this special section focus on emerging computational intelligence (CI) based theories and methodologies for big graph data management. The emerging CI techniques cover a broad range of nature-inspired, multidisciplinary computational methodologies, such as fuzzy logic, graph pattern matching, graph neural networks, graph embedding, graph attention, evolutionary computing, cognitive computing, learning theory, and probabilistic methods. The objective of this special issue is to explore how CI models and their variants can be adapted, augmented and extended to deal with applications involving very large scale graph data.