Network Composition from Multi-layer Data

It is common for people to access multiple social networks, for example, using phone, email, and social media. Together, the multi-layer social interactions form a “integrated social network.” How can we extend well developed knowledge about single-layer networks, including vertex centrality and community structure, to such heterogeneous structures? In this paper, we approach these challenges by proposing a principled framework of network composition based on a unified dynamical process. Mathematically, we consider the following abstract problem: Given multilayer network data, (G, . . . , G) over a vertex set V and additional parameters for intra and inter-layer dynamics, construct a (single) weighted network G that best integrates the joint process. We use transformations of dynamics to unify heterogeneous layers under a common dynamics. For inter-layer compositions, we will consider several cases as the interlayer dynamics plays different roles in various social or technological networks. Empirically, we provide examples to highlight the usefulness of this framework for network analysis and network design.