Hierarchical Decomposition of Networks into Multiple Cores Formed by Local Hubs
Networks can be decomposed into an onion-like hierarchical structure by iteratively removing edges connecting locally least important nodes, revealing multiple cores formed by local hubs.
Maximizing Network Degree Correlation through Degree-Preserving Rewiring
The core message of this article is to propose efficient methods to maximize the degree correlation of a network through a limited number of degree-preserving rewirings.
Quantifying the Relationship Between Network Metadata and Mesoscale Structure Using Description Length
The core message of this article is to introduce a novel measure called metablox that quantifies the relevance of node metadata to the mesoscale structure of a network, and identifies the likely structural arrangement of the metadata partition.
Estimating Mixed-Memberships Using the Symmetric Laplacian Inverse Matrix for Community Detection
The authors propose a new spectral clustering method called Mixed-SLIM for detecting mixed memberships in networks under the degree-corrected mixed membership model. They provide theoretical bounds for the estimation error of the proposed algorithm and its regularized version.
Estimating Common Mixed Memberships in Multi-Layer Networks
The core message of this paper is to propose novel spectral methods to estimate the common mixed memberships in the multi-layer mixed membership stochastic block model, and establish their theoretical consistency as the number of nodes and/or layers increases.
Traffic Divergence Theory: A Unified Approach for Analyzing Network Traffic Dynamics
The core message of this article is to introduce a novel theoretical framework called Traffic Divergence Theory that provides a unified approach for analyzing and modeling network traffic dynamics. The theory captures the flow (im)balance of network nodes and links, enabling the investigation of both spatial and temporal traffic dynamics.
Efficient Community Detection in Networks via Local Dominance
The core message of this article is that the notion of local dominance can be used to efficiently detect communities in complex networks. The proposed Local Search (LS) algorithm identifies community centers based on local information, such as node degree and distance to other local leaders, and then assigns nodes to communities based on these local dominance relations.
Compressing Temporal Networks While Preserving Epidemic Dynamics
Compressing the chronology of a temporal network while preserving the underlying epidemic dynamics by quantifying the error induced by aggregating consecutive network snapshots.
RCoCo: Contrastive Collective Link Prediction in Riemannian Space
Proposing RCoCo for collective link prediction in multiplex networks, leveraging Riemannian spaces for intra- and inter-network behaviors.
Dynamic Edge Partition Models for Temporal Networks with Scalable Inference
Proposing a novel dynamic edge partition model for temporal networks with scalable inference using SG-MCMC algorithms.
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