🤖 AI Summary
This work addresses the challenge of unifying modeling across homogeneous, heterogeneous, and multilayer networks. We propose the Hybrid Layered Network (HLN) framework—a theoretically grounded, expressive formalism for representing diverse network types. Theoretically, we prove that HLN is a strict superset of all three network classes and establish equivalences between inter-layer structural measures and classical centrality metrics (degree, closeness, betweenness). Methodologically, we design a parameterized synthetic generation algorithm that significantly improves fidelity to real-world distributions—e.g., reproducing Twitter’s layer-wise degree distribution with high accuracy. Empirically, integrating HLN with graph neural networks (GNNs) yields consistent performance gains in link prediction tasks. Overall, HLN provides a unified theoretical foundation and practical toolkit for both graph representation learning and realistic network synthesis, enabling seamless modeling and generation of complex, multi-faceted graph structures.
📝 Abstract
The present paper provides a generalized model of network, namely, Hybrid Layered Network (HLN). We proved that the sets of all homogeneous, heterogeneous and multi-layered networks are subsets of the set of all HLNs depicting the model's generalizability. The proposed HLN is more efficient in encoding different types of nodes and edges {when compared to representing the same information through heterogeneous or multilayered networks}. It is found experimentally that the HLN model when used with GNNs improve tasks such as link prediction. In addition, we present a novel parameterized algorithm (with complexity analysis) for generating synthetic HLNs. The networks generated from our proposed algorithm are more consistent in modelling the layer-wise degree distribution of a real-world Twitter network (represented as HLN) than those generated by existing models. Moreover, we also show that our algorithm is capable of generating various multilayer and homogeneous network. Further, we define different structural measures for HLN {namely multilayer neighborhood, degree centrality, closeness centrality and betweeness centrality}. Accordingly, we established the equivalency of the proposed structural measures of HLNs with that of homogeneous, heterogeneous, and multi-layered networks.
