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Found 26 papers, 22 papers with code
Scalable network reconstruction in subquadratic time
Network reconstruction consists in determining the unobserved pairwise couplings between $N$ nodes given only observational data on the resulting behavior that is conditioned on those couplings -- typically a time-series or independent samples from a graphical model.
Implicit models, latent compression, intrinsic biases, and cheap lunches in community detection
Here we present a solution to this problem that associates any community detection objective, inferential or descriptive, with its corresponding implicit network generative model.
Systematic assessment of the quality of fit of the stochastic block model for empirical networks
We perform a systematic analysis of the quality of fit of the stochastic block model (SBM) for 275 empirical networks spanning a wide range of domains and orders of size magnitude.
Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths
In this way, they are able to provide insights into the mechanisms of network formation, and separate structure from randomness in a manner supported by statistical evidence.
Multilayer Networks for Text Analysis with Multiple Data Types
We are interested in the widespread problem of clustering documents and finding topics in large collections of written documents in the presence of metadata and hyperlinks.
Disentangling homophily, community structure and triadic closure in networks
Network homophily, the tendency of similar nodes to be connected, and transitivity, the tendency of two nodes being connected if they share a common neighbor, are conflated properties in network analysis, since one mechanism can drive the other.
Hypergraph reconstruction from network data
Pairwise representations nonetheless remain ubiquitous, because higher-order interactions are often not recorded explicitly in network data.
Statistical inference of assortative community structures
We develop a principled methodology to infer assortative communities in networks based on a nonparametric Bayesian formulation of the planted partition model.
Revealing consensus and dissensus between network partitions
As an attempt to extract understanding from a population of alternative solutions, many methods exist to establish a consensus among them in the form of a single partition "point estimate" that summarizes the whole distribution.
Merge-split Markov chain Monte Carlo for community detection
We present a Markov chain Monte Carlo scheme based on merges and splits of groups that is capable of efficiently sampling from the posterior distribution of network partitions, defined according to the stochastic block model (SBM).
Latent Poisson models for networks with heterogeneous density
Empirical networks are often globally sparse, with a small average number of connections per node, when compared to the total size of the network.
Network reconstruction and community detection from dynamics
We present a scalable nonparametric Bayesian method to perform network reconstruction from observed functional behavior that at the same time infers the communities present in the network.
Reconstructing networks with unknown and heterogeneous errors
These approaches, however, rely on assumptions of uniform error rates and on direct estimations of the existence of each edge via repeated measurements, something that is currently unavailable for the majority of network data.
Change points, memory and epidemic spreading in temporal networks
Dynamic networks exhibit temporal patterns that vary across different time scales, all of which can potentially affect processes that take place on the network.
Physics and Society Social and Information Networks Data Analysis, Statistics and Probability
A network approach to topic models
By adapting existing community-detection methods -- using a stochastic block model (SBM) with non-parametric priors -- we obtain a more versatile and principled framework for topic modeling (e. g., it automatically detects the number of topics and hierarchically clusters both the words and documents).
Nonparametric weighted stochastic block models
We present a Bayesian formulation of weighted stochastic block models that can be used to infer the large-scale modular structure of weighted networks, including their hierarchical organization.
Bayesian stochastic blockmodeling
This chapter provides a self-contained introduction to the use of Bayesian inference to extract large-scale modular structures from network data, based on the stochastic blockmodel (SBM), as well as its degree-corrected and overlapping generalizations.
Consistencies and inconsistencies between model selection and link prediction in networks
A principled approach to understand network structures is to formulate generative models.
Nonparametric Bayesian inference of the microcanonical stochastic block model
A very efficient inference algorithm that scales well not only for networks with a large number of nodes and edges, but also with an unlimited number of modules.
Network structure, metadata and the prediction of missing nodes and annotations
The empirical validation of community detection methods is often based on available annotations on the nodes that serve as putative indicators of the large-scale network structure.
Modeling sequences and temporal networks with dynamic community structures
In evolving complex systems such as air traffic and social organizations, collective effects emerge from their many components' dynamic interactions.
Inferring the mesoscale structure of layered, edge-valued and time-varying networks
These different types of interactions are often represented as layers, attributes on the edges or as a time-dependence of the network structure.
Physics and Society Data Analysis, Statistics and Probability
Model selection and hypothesis testing for large-scale network models with overlapping groups
In this work, we present a method of model selection based on the minimum description length criterion and posterior odds ratios that is capable of fully accounting for the increased degrees of freedom of the larger models, and selects the best one according to the statistical evidence available in the data.
Data Analysis, Statistics and Probability Disordered Systems and Neural Networks Social and Information Networks Computational Physics Physics and Society
Hierarchical Block Structures and High-resolution Model Selection in Large Networks
Discovering and characterizing the large-scale topological features in empirical networks are crucial steps in understanding how complex systems function.
Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models
We present an efficient algorithm for the inference of stochastic block models in large networks.
Parsimonious module inference in large networks
We investigate the detectability of modules in large networks when the number of modules is not known in advance.
