Higher order graph centrality measures for Neo4j

Graphs are currently the epicenter of intense research as they lay the theoretical groundwork in diverse fields ranging from combinatorial optimization to computational neuroscience. Vertex centrality plays a crucial role in graph mining as it ranks them according to their contribution to overall graph communication. Specifically, within the social network analysis context centrality identifies influential indivduals, whereas in the bioinformatics field centrality locates dominant proteins in protein-to-protein interaction. In recent years graph databases, part of the rising NoSQL movement, have been added to the graph analysis toolset. An implementation of eigenvector centrality, a prominent member of the broad class of spectral centrality, in Java and NetBeans designed for use with Neo4j, a major schemaless graph database, is outlined and the findings resulting from its application to a real world social graph are discussed.