Dissecting Cross-Layer Dependency Inference on Multi-Layered Inter-Dependent Networks
Yuchen Yan, Qinghai Zhou, Jinning Li, Tarek Abdelzaher, Hanghang Tong
Abstract
Multi-layered inter-dependent networks have emerged in a wealth of high-impact application domains. Cross-layer dependency inference, which aims to predict the dependencies between nodes across different layers, plays a pivotal role in such multi-layered network systems. Most, if not all, of existing methods exclusively follow a coupling principle of design and can be categorized into the following two groups, including (1) heterogeneous network embedding based methods (data coupling), and (2) collaborative filtering based methods (module coupling). Despite the favorable achievement, methods of both types are faced with two intricate challenges, including (1) the sparsity challenge where very limited observations of cross-layer dependencies are available, resulting in a deteriorated prediction of missing dependencies, and (2) the dynamic challenge given that the multi-layered network system is constantly evolving over time.