Community detection in networks without observing edges
We develop a Bayesian hierarchical model to identify communities in networks for which we do not observe the edges directly, but instead observe a series of interdependent signals for each of the nodes. Fitting the model provides an end-to-end community detection algorithm that does not extract information as a sequence of point estimates but propagates uncertainties from the raw data to the community labels. Our approach naturally supports multiscale community detection as well as the selection of an optimal scale using model comparison. We study the properties of the algorithm using synthetic data and apply it to daily returns of constituents of the S&P100 index as well as climate data from US cities.
Code (1)
Tasks
Community DetectionSimilar Papers 제목 키워드 기반
Hide and Seek: Outwitting Community Detection Algorithms
Community affiliation of a node plays an important role in determining its contextual position in the network, which may raise privacy concerns when a sensitive node wants to hide its identity in a network. Oftentimes, a…
Community DetectionDynamic Network Model from Partial Observations
Can evolving networks be inferred and modeled without directly observing their nodes and edges? In many applications, the edges of a dynamic network might not be observed, but one can observe the dynamics of stochastic c…
modelOpen-Ended Question AnsweringExact Matching in Correlated Networks with Node Attributes for Improved Community Recovery
We study community detection in multiple networks whose nodes and edges are jointly correlated. This setting arises naturally in applications such as social platforms, where a shared set of users may exhibit both correla…
AttributeCommunity DetectionGraph MatchingStochastic Block ModelCertified Robustness of Community Detection against Adversarial Structural Perturbation via Randomized Smoothing
Community detection plays a key role in understanding graph structure. However, several recent studies showed that community detection is vulnerable to adversarial structural perturbation. In particular, via adding or re…
Community DetectionVEC-SBM: Optimal Community Detection with Vectorial Edges Covariates
Social networks are often associated with rich side information, such as texts and images. While numerous methods have been developed to identify communities from pairwise interactions, they usually ignore such side info…
Community DetectionStochastic Block Model