• Corpus ID: 245334638

Community detection and reciprocity in networks by jointly modeling pairs of edges

@article{Contisciani2021CommunityDA,
  title={Community detection and reciprocity in networks by jointly modeling pairs of edges},
  author={Martina Contisciani and Hadi Safdari and Caterina De Bacco},
  journal={ArXiv},
  year={2021},
  volume={abs/2112.10436}
}
We present a probabilistic generative model and an efficient algorithm to both perform community detection and capture reciprocity in networks. Our approach jointly models pairs of edges with exact 2-edge joint distributions. In addition, it provides closed-form analytical expressions for both marginal and conditional distributions. We validate our model on synthetic data in recovering communities, edge prediction tasks, and generating synthetic networks that replicate the reciprocity values… 

Figures and Tables from this paper

Latent Network Models to Account for Noisy, Multiply-Reported Social Network Data
TLDR
A probabilistic model that incorporates ties reported by multiple individuals to estimate the unobserved network structure and explicitly incorporates a term for “mutuality,” the tendency to report ties in both directions involving the same alter, is proposed.
Principled inference of hyperedges and overlapping communities in hypergraphs
TLDR
This work proposes a framework based on statistical inference to characterize the structural organization of hypergraphs that allows to infer missing hyperedges of any size in a principled way, and to jointly detect overlapping communities in presence of higher-order interactions.
The interplay between ranking and communities in networks
TLDR
A generative model based on an interplay between community and hierarchical structures that assumes that each node has a preference in the interaction mechanism and nodes with the same preference are more likely to interact, while heterogeneous interactions are still allowed.

References

SHOWING 1-10 OF 30 REFERENCES
A generative model for reciprocity and community detection in networks
TLDR
A probabilistic generative model and efficient algorithm to model reciprocity in directed networks that provides a natural framework for relaxing the common assumption in network generative models of conditional independence between edges and outperforms others in both predicting edges and generating networks that reflect the reciprocity values observed in real data.
Reciprocity, community detection, and link prediction in dynamic networks
TLDR
A probabilistic generative model with hidden variables that integrates reciprocity and communities as structural information of networks that evolve in time that captures the reciprocity of real networks better than standard models with only community structure, while performing well at link prediction tasks.
Disentangling homophily, community structure and triadic closure in networks
TLDR
This approach is based on a variation of the stochastic block model with the addition of triadic closure edges, and its inference can identify the most plausible mechanism responsible for the existence of every edge in the network, in addition to the underlying community structure itself.
An efficient and principled method for detecting communities in networks
TLDR
This work describes a method for finding overlapping communities based on a principled statistical approach using generative network models and shows how the method can be implemented using a fast, closed-form expectation-maximization algorithm that allows us to analyze networks of millions of nodes in reasonable running times.
Patterns of link reciprocity in directed networks.
TLDR
It is found that real networks are always either correlated or anticorrelated, and that networks of the same type display similar values of the reciprocity.
Community detection with node attributes in multilayer networks
TLDR
This work develops a principled probabilistic method that does not assume any a priori correlation structure between attributes and communities but rather infers this from data, which leads to an efficient algorithmic implementation that exploits the sparsity of the dataset and can be used to perform several inference tasks.
Community detection, link prediction, and layer interdependence in multilayer networks
TLDR
A generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting, and gives a mathematically principled way to define the interdependence between layers.
Measuring reciprocity: Double sampling, concordance, and network construction
Reciprocity—the mutual provisioning of support/goods—is a pervasive feature of social life. Di- rected networks provide a way to examine the structure of reciprocity in a community. However,
Statistical mechanics of networks.
  • Juyong Park, M. Newman
  • Computer Science
    Physical review. E, Statistical, nonlinear, and soft matter physics
  • 2004
We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of
...
...