The ground truth about metadata and community detection in networks
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TLDR
It is proved that no algorithm can uniquely solve community detection, and a general No Free Lunch theorem for community detection is proved, which implies that there can be no algorithm that is optimal for all possible community detection tasks.Abstract:
Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.read more
Citations
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Journal ArticleDOI
Community detection in bipartite networks with stochastic block models.
Tzu-Chi Yen,Daniel B. Larremore +1 more
TL;DR: This work introduces a Bayesian nonparametric formulation of the stochastic block model and a corresponding algorithm to efficiently find communities in bipartite networks which parsimoniously chooses the number of communities, and expands the understanding of the complicated optimization landscape associated with community detection tasks.
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Non-assortative community structure in resting and task-evoked functional brain networks
TL;DR: In this paper, the authors leverage weighted stochastic blockmodeling, a community detection method capable of detecting diverse classes of communities, to study the community structure of functional brain networks while subjects either rest or perform cognitively demanding tasks.
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Adaptive modularity maximization via edge weighting scheme
TL;DR: Experimental results on real and synthetic networks show that the state-of-the-art community detection algorithms improve their performance significantly by finding communities in the weighted graphs produced by the model.
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Cross-validation estimate of the number of clusters in a network
TL;DR: In this paper, the authors propose principled, scalable, and widely applicable assessment criteria to determine the number of clusters in modular networks based on the leave-one-out cross-validation estimate of the edge prediction error.
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An evolutionary autoencoder for dynamic community detection
TL;DR: This paper proposes a semi-supervised algorithm (sE-Autoencoder) to overcome the effects of nonlinear property on the low-dimensional representation of networks by extending the typical nonlinear reconstruction model to the dynamic network by constructing a temporal matrix.
References
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