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Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challengingespecially if one wants to do it efficiently and retain the flexibility bychoosing features of interest for analysis. Features (e.g., age, gender,relationship, political view etc.) can be explicitly given from datasets, butalso can be derived from content (e.g., political view based on Facebookposts). Analysis from multiple perspectives is needed to understand thedatasets (or subsets of it) and to infer meaningful knowledge. For example, theinfluence of age, location, and marital status on political views may need tobe inferred separately (or in combination). In this paper, we adapt multilayernetwork (MLN) analysis, a nontraditional approach, to model the Facebookdatasets, integrate content analysis, and conduct analysis, which is driven bya list of desired application based queries. Our experimental analysis showsthe flexibility and efficiency of the proposed approach when modeling andanalyzing datasets with multiple features.

Generic Multilayer Network Data Analysis with the Fusion of Content and Structure