Tensorial and bipartite block models for link prediction in layered networks and temporal networks

Tarrés-Deulofeu, Marc; Godoy-Lorite, Antonia; Guimerà, Roger; Sales‐Pardo, Marta

doi:10.1103/physreve.99.032307

Cited by 26 publications

(38 citation statements)

References 28 publications

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“…Previous work shows that using these approaches we are able of retrieving the correct model for model generated data [27,28]. For each fold of the data, we show the predictive accuracy of the three models: naive, single-membership SBM and MMSBM.…”

Section: Inference and Predictionmentioning

confidence: 99%

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

et al. 2021

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Many studies have shown that there are regularities in the way human beings make decisions. However, our ability to obtain models that capture such regularities and can accurately predict unobserved decisions is still limited. We tackle this problem in the context of individuals who are given information relative to the evolution of market prices and asked to guess the direction of the market. We use a networks inference approach with stochastic block models (SBM) to find the model and network representation that is most predictive of unobserved decisions. Our results suggest that users mostly use recent information (about the market and about their previous decisions) to guess. Furthermore, the analysis of SBM groups reveals a set of strategies used by players to process information and make decisions that is analogous to behaviors observed in other contexts. Our study provides and example on how to quantitatively explore human behavior strategies by representing decisions as networks and using rigorous inference and model-selection approaches.

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Section: Inference and Predictionmentioning

confidence: 99%

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

et al. 2021

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“…There have been several recent attempts to perform edge prediction in multilayer networks [15,20,28]. All these methods use multilayer information to infer mesoscale structures in networks, but then they perform edge prediction independently in each layer, conditioned on the inferred mesoscale structure.…”

Section: A Edge Prediction Using Correlated Modelsmentioning

confidence: 99%

“…served edges. SBMs have often been used for edge prediction, both for monolayer networks [4,9,27] and for multilayer networks [15,20,28]. The models that we propose should yield better performance than past efforts, as they take advantage of interlayer edge correlations in data.…”

Section: Introductionmentioning

confidence: 99%

Inference of Edge Correlations in Multilayer Networks

Pamfil,

Howison,

Porter

2019

Preprint

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Many recent developments in network analysis have focused on multilayer networks, which one can use to encode time-dependent interactions, multiple types of interactions, and other complications that arise in complex systems. Like their monolayer counterparts, multilayer networks in applications often have mesoscale features, such as community structure. A prominent type of method for inferring such structures is the employment of multilayer stochastic block models (SBMs). A common (but inadequate) assumption of these models is the sampling of edges in different layers independently, conditioned on community labels of the nodes. In this paper, we relax this assumption of independence by incorporating edge correlations into an SBM-like model. We derive maximum-likelihood estimates of the key parameters of our model, and we propose a measure of layer correlation that reflects the similarity between connectivity patterns in different layers. Finally, we explain how to use correlated models for edge prediction in multilayer networks. By taking into account edge correlations, prediction accuracy improves both in synthetic networks and in a temporal network of shoppers who are connected to previously-purchased grocery products.

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“…Since one of the SBM's parameters is a division of the nodes into groups, community detection with the SBM simply requires a method to fit the model to network data. With inference methods becoming increasingly sophisticated [7], many variants of the SBM have been proposed, including those that accommodate overlapping communities [8,9], broad degree distributions [10], multilayer networks [11], hierarchical community structures [12], and networks with metadata [13][14][15]. SBMs have also been used to estimate network structure or related observational data even if the measurement process is incomplete and erroneous [1,[16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Community Detection in Bipartite Networks with Stochastic Blockmodels

Yen,

Larremore

2020

Preprint

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In bipartite networks, community structures are restricted to being disassortative, in that nodes of one type are grouped according to common patterns of connection with nodes of the other type. This makes the stochastic block model (SBM), a highly flexible generative model for networks with block structure, an intuitive choice for bipartite community detection. However, typical formulations of the SBM do not make use of the special structure of bipartite networks. In this work, we introduce a Bayesian nonparametric formulation of the SBM and a corresponding algorithm to efficiently find communities in bipartite networks without overfitting. The biSBM improves community detection results over general SBMs when data are noisy, improves the model resolution limit by a factor of √ 2, and expands our understanding of the complicated optimization landscape associated with community detection tasks. A direct comparison of certain terms of the prior distributions in the biSBM and a related high-resolution hierarchical SBM also reveals a counterintuitive regime of community detection problems, populated by smaller and sparser networks, where non-hierarchical models outperform their more flexible counterpart.

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Tensorial and bipartite block models for link prediction in layered networks and temporal networks

Cited by 26 publications

References 28 publications

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

Complex decision-making strategies in a stock market experiment explained as the combination of few simple strategies

Inference of Edge Correlations in Multilayer Networks

Community Detection in Bipartite Networks with Stochastic Blockmodels

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