Supervised community detection with line graph neural networks

A Bayesian model+algorithm for community detection in bipartite networks

Community Detection in Graphs (master's degree short project)

Bayesian network models for inferring core-periphery structure

A package to sample and estimate variants of the stochastic blockmodel from network data

MATLAB toolbox for fitting discrete-time dynamic stochastic block models

Pruning tool to identify small subsets of network partitions that are significant from the perspective of stochastic block model inference. This method works for single-layer and multi-layer networks, as well as for restricting focus to a fixed number of communities when desired.

An R package for adjusting Stochastic Block Models from networks data sampled under various missing data conditions

C++ implementation of a MCMC sampler for the (canonical) SBM

C++ implementation of a MCMC sampler for the (microcanonical) bipartite SBM

Community detection engine for the degree-corrected Stochastic Block Model, using the Belief Propagation algorithm.

Software that simulates voting processes and compares electoral systems. A network of voters is generated by the Stochastic Block Model or a distance-based model. Opinion dynamics are run on the network with options for zealots and media bias. Different electoral systems are supported with high flexibility to accommodate real-world systems.”

MATLAB code for incorporating friendship networks into dynamic link prediction on interaction networks

[NeurIPS 2023] Official implementation of "A Neural Collapse Perspective on Feature Evolution in Graph Neural Networks"

Advanced Graph Clustering method documentation and implementation (From Spectral Clustering to Deep Graph Clustering)

Neural Network based Stochastic Blockmodel using Variational Inference

Graph partitioning engine for the degree-corrected bipartite Stochastic Block Model, using the Kernighan-Lin algorithm.

Project for the Bayesian Statistics course of the MSc in Mathematical Engineering @ Polimi (A.Y. 2022-2023).

Relational Data Learning