Abstract
We propose a novel centrality measure that is called Content Centrality for a given network that considers the feature vector of each node generated from its posting activities in social media, its own properties and so forth, in order to extract nodes who have neighbors with similar features. We assume that nodes with similar features are located near each other and unevenly distributed over a network, and the density gradually or rapidly decreases according to the distance from the center of the feature distribution (node). We quantify the degree of the feature concentration around each node by calculating the cosine similarity between the feature vector of each node and the resultant vector of its neighbors with distance-based decay weights, then rank all the nodes according to the value of cosine similarities. In experimental evaluations with three real networks, we confirm the validity of the centrality rankings and discuss the relation between the estimated parameters and the nature of nodes.
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Notes
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We use the term “content distribution” in the same sense with “feature distribution”.
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Acknowledgements
This work was supported by JSPS KAKENHI Grant No. 15J00735 and by NII’s strategic open-type collaborative research. In our experiments, we used recipe data provided by Cookpad and the National Institute of Informatics.
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Fushimi, T., Satoh, T., Saito, K., Kazama, K., Kando, N. (2016). Content Centrality Measure for Networks: Introducing Distance-Based Decay Weights. In: Spiro, E., Ahn, YY. (eds) Social Informatics. SocInfo 2016. Lecture Notes in Computer Science(), vol 10047. Springer, Cham. https://doi.org/10.1007/978-3-319-47874-6_4
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DOI: https://doi.org/10.1007/978-3-319-47874-6_4
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