• Corpus ID: 252531633

Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths

@inproceedings{Peixoto2021DescriptiveVI,
  title={Descriptive vs. inferential community detection in networks: pitfalls, myths, and half-truths},
  author={Tiago P. Peixoto},
  year={2021}
}
Community detection is one of the most important methodological fields of network science, and one which has attracted a significant amount of attention over the past decades. This area deals with the automated division of a network into fundamental building blocks, with the objective of providing a summary of its large-scale structure. Despite its importance and widespread adoption, there is a noticeable gap between what is arguably the state-of-theart and the methods that are actually used in… 
1 Citations

Brain tumour genetic network signatures of survival

The findings illuminate the complex dependence between features across the genetic landscape of brain tumours and show network analysis reveals distinct signatures of survival with better prognostic fidelity than current gold standard diagnostic categories, paving the way for personalised prognostication.

References

SHOWING 1-10 OF 128 REFERENCES

Revealing consensus and dissensus between network partitions

This work provides a comprehensive set of methods designed to characterize and summarize complex populations of partitions in a manner that captures not only the existing consensus, but also the dissensus between elements of the population.

Hierarchical block structures and high-resolution model selection in large networks

A nested generative model is constructed that, through a complete description of the entire network hierarchy at multiple scales, enables the detection of modular structure at levels far beyond those possible with current approaches, and is based on the principle of parsimony.

Consistency of community structure in complex networks

It is argued that traditional community detection does in fact give a significant amount of insight into network structure, and an information theoretic method for discovering the building blocks in specific networks is proposed.

Identification of core-periphery structure in networks

The method is found to be efficient, scaling easily to networks with a million or more nodes, and it is demonstrated that the method is immune to the detectability transition observed in the related community detection problem, which prevents the detection of community structure when that structure is too weak.

Community detection for correlation matrices

This work introduces, via a consistent redefinition of null models based on random matrix theory, the appropriate correlation-based counterparts of the most popular community detection techniques, and can filter out both unit-specific noise and system-wide dependencies, and the resulting communities are internally correlated and mutually anti-correlated.

Evaluating Overfit and Underfit in Models of Network Community Structure

A broad investigation of over and underfitting across 16 state-of-the-art community detection algorithms applied to a novel benchmark corpus of 572 structurally diverse real-world networks finds that algorithms vary widely in the number and composition of communities they find, given the same input.

Structure and inference in annotated networks

This work focuses in particular on the problem of community detection in networks and develops a mathematically principled approach that combines a network and its metadata to detect communities more accurately than can be done with either alone.

The many facets of community detection in complex networks

A focused review of the different motivations that underpin community detection and highlights the different facets of community detection, which delineates the many lines of research and points out open directions and avenues for future research.

Statistical inference of assortative community structures

This approach succeeds in finding statistically significant assortative modules in networks, unlike alternatives such as modularity maximization, which systematically overfits both in artificial as well as in empirical examples.

Disentangling homophily, community structure and triadic closure in networks

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.
...