Deriving networks and communities from individual and group attributes is an important task in understanding social groups and relations. In this work we propose a novel methodology to derive networks and communities from sociocultural data. Our methodology is based on socio-cultural cognitive mapping (SCM) and k-NN network modularity maximization (SCM + k-NN) that produces both a latent network and community assignments of entities based upon their socio-cultural and behavioral attributes. We apply this methodology to two real-world data sets and compare the community assignments by our methodology to those communities found by k-Means, Gaussian Mixture Models, and Affinity Propagation. We then analyze the latent networks that are created by SCM + k-NN to derive novel insight into the nature of the communities. The community assignments found by SCM + k-NN are comparable to those produced by current unsupervised machine learning techniques. Additionally, in contrast to current unsupervised machine learning techniques SCM + k-NN also produces a latent network that gives additional insight into community relationships.
CITATION STYLE
Cruickshank, I., & Carley, K. M. (2020). Socio-Cultural Cognitive Mapping to Identify Communities and Latent Networks (pp. 35–48). https://doi.org/10.1007/978-3-030-31463-7_3
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