Tensor fusion of social structural and functional analytics over Neo4j
For large directed graphs ranking vertices is an algorithmic as well as a computational challenge. Also it is a cornerstone problem in fields so diverse such as online social media, combinatorial optimization, deep learning, econometrics, and computational neuroscience. Gell point centrality metric is a common case of structural vertex ranking which can be efficiently computed through power method. This paper proposes harmonic centrality, also a structural ranking which can be computed equally efficiently. When the graph represents a network, both methods are oblivious to its functionality. To address that issue, a tensor based methodology is proposed for combining functional characteristics with these structural metrics. As a concrete demonstration, the tensor fusion methodology was implemented in Java over Neo4j and Tensor toolbox and was applied to a Twitter subgraph. The functional features were directly related to affective computing, while the hashtags in this subgraph were relared to a current controversial political topic, namely #brexit.
2016 7th International Conference on Information, Intelligence, Systems Applications (IISA)