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Found 8 papers, 2 papers with code
Consistency between ordering and clustering methods for graphs
A relational dataset is often analyzed by optimally assigning a label to each element through clustering or ordering.
Graph-based open-ended survey on concerns related to COVID-19
The GOS framework is a hybrid of the two traditional survey frameworks that allows respondents to post their opinions in a free-format style, which can subsequently serve as one of the choice items for other respondents, just as in a multiple-choice survey.
Social and Information Networks Physics and Society
Mean-field theory of graph neural networks in graph partitioning
A theoretical performance analysis of the graph neural network (GNN) is presented.
Algorithmic detectability threshold of the stochastic block model
Because the algorithm cannot always learn the planted model parameters correctly, the algorithmic detectability threshold is qualitatively different from the one with the Nishimori condition.
Algorithmic infeasibility of community detection in higher-order networks
We focus on the expectation-maximization (EM) algorithm with belief propagation (BP), and analytically derive its algorithmic detectability threshold, i. e., the limit of the modular structure strength below which the algorithm can no longer detect any modular structures.
Comparative analysis on the selection of number of clusters in community detection
We conduct a comparative analysis on various estimates of the number of clusters in community detection.
Social and Information Networks Physics and Society
Cross-validation estimate of the number of clusters in a network
Network science investigates methodologies that summarise relational data to obtain better interpretability.
Social and Information Networks Physics and Society
A Tractable Fully Bayesian Method for the Stochastic Block Model
The stochastic block model (SBM) is a generative model revealing macroscopic structures in graphs.
