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

Multi-feature data analysis (e.g., on Facebook, LinkedIn) is challenging especially if one wants to do it efficiently and retain the flexibility by choosing features of interest for analysis. Features (e.g., age, gender, relationship, political view etc.) can be explicitly given from datasets, but also can be derived from content (e.g., political view based on Facebook posts). Analysis from multiple perspectives is needed to understand the datasets (or subsets of it) and to infer meaningful knowledge. For example, the influence of age, location, and marital status on political views may need to be inferred separately (or in combination). In this paper, we adapt multilayer network (MLN) analysis, a nontraditional approach, to model the Facebook datasets, integrate content analysis, and conduct analysis, which is driven by a list of desired application based queries. Our experimental analysis shows the flexibility and efficiency of the proposed approach when modeling and analyzing datasets with multiple features.Comment: 18 page

Structure-Preserving Community In A Multilayer Network: Definition, Detection, And Analysis

Multilayer networks or MLNs (also called multiplexes or network of networks) are being used extensively for modeling and analysis of data sets with multiple entity and feature types as well as their relationships. As the concept of communities and hubs are used for these analysis, a structure-preserving definition for them on MLNs (that retains the original MLN structure and node/edge labels and types) and its efficient detection are critical. There is no structure-preserving definition of a community for a MLN as most of the current analyses aggregate a MLN to a single graph. Although there is consensus on community definition for single graphs (and detection packages) and to a lesser extent for homogeneous MLNs, it is lacking for heterogeneous MLNs. In this paper, we not only provide a structure-preserving definition for the first time, but also its efficient computation using a decoupling approach, and discuss its characteristics & significance for analysis. The proposed decoupling approach for efficiency combines communities from individual layers to form a serial k-community for connected k layers in a MLN. We propose several weight metrics for composing layer-wise communities using the bipartite graph match approach based on the analysis semantics. Our proposed approach has a number of advantages. It: i) leverages extant single graph community detection algorithms, ii) is based on the widely-used maximal flow bipartite graph matching for composing k layers, iii) introduces several weight metrics that are customized for the community concept, and iv) experimentally validates the definition, mapping, and efficiency from a flexible analysis perspective on widely-used IMDb data set. Keywords: Heterogeneous Multilayer Networks; Bipartite Graphs; Community Definition and Detection; Decoupling-Based CompositionComment: 27 pages, Submitted to VLDB 201