Identifying hubs in directed networks
arXiv:2312.03347 · doi:10.1103/PhysRevE.109.034310
Abstract
Nodes in networks that exhibit high connectivity, also called ``hubs'', play a critical role in determining the structural and functional properties of networked systems. However, there is no clear definition of what constitutes a hub node in a network, and the classification of network hubs in existing work has either been purely qualitative or relies on ad hoc criteria for thresholding continuous data that do not generalize well to networks with certain degree sequences. Here we develop a set of efficient nonparametric methods that classify hub nodes in directed networks using the Minimum Description Length principle, effectively providing a clear and principled definition for network hubs. We adapt our methods to both unweighted and weighted networks and demonstrate them in a range of example applications using real and synthetic network data.
References in corpus (13)
- Stochastic blockmodels and community structure in networks
- Connectivity of Growing Random Networks
- From mobile phone data to the spatial structure of cities
- Consistency and differences between centrality measures across distinct classes of networks
- Hierarchical Block Structures and High-resolution Model Selection in Large Networks
- Uncovering the spatial structure of mobility networks
- A Clarified Typology of Core-Periphery Structure in Networks
- Impact of urban structure on infectious disease spreading
- Analyzing complex networks through correlations in centrality measurements
- The impact of inter-city mobility on urban welfare
- Spatial regionalization based on optimal information compression
- Compressing network populations with modal networks reveals structural diversity
- Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection