Multivariate spatial autoregressive model for large scale social networks

Zhu, Xuening; Huang, Danyang; Pan, Rui; Wang, Hansheng

doi:10.1016/j.jeconom.2018.11.018

Cited by 30 publications

(19 citation statements)

References 27 publications

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“…The second avenue is using the screening or regularization method to obtain the sparse solution for constructing n influence indices λ i (e.g., see Zhu et al 2019a) or to develop the test statistic for testing a subset of λ i s being equal. The third avenue is proposing a computationally feasible estimation approach (e.g., the least squares method in Huang et al 2019 andZhu et al 2019b), to overcome the computational challenge of QMLE under large scale networks (see numerical illustrations in Section S.4 of the Supplementary Material). The fourth avenue is motivated by an anonymous referee's comment, which extends the adaptive SAR model (1.2) to Y i = ∑ n j=1 λ ij Y j + ε i so that the closeness between node i and node j can be characterized via the influence parameter λ ij .…”

Section: Discussionmentioning

confidence: 99%

Network Influence Analysis

Zou¹,

Luo²,

Lan³

et al. 2021

STAT SINICA

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Due to the rapid development of social networking sites, the spatial autoregressive (SAR) model has played an important role in social network studies. However, the underlying structure of SAR implicitly assumes that all nodes (or actors or users) within the network have the same influential power measured by the common autocorrelation parameter. Hence, the classical SAR is unable to identify influential nodes. This paper proposes the adaptive SAR model by introducing the network influence index, which includes the classical SAR model as a special case. Using this proposed model without imposing any specific error distribution, we apply Lee's (2004) quasi-maximum likelihood approach to estimate the unknown parameters of the index, which can then be used to characterize the influential power of each node. The asymptotic properties of parameter estimates are established and three test statistics for assessing the homogeneity of the network influence indices are presented. The usefulness of the adaptive SAR model and its associated network index are illustrated via simulation studies and an empirical investigation of the spillover effects in Chinese mutual fund cash

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Section: Discussionmentioning

confidence: 99%

Network Influence Analysis

Zou¹,

Luo²,

Lan³

et al. 2021

STAT SINICA

View full text Add to dashboard Cite

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“…This strictly stationary model quantifies the impact of a node value on its increments, the momentum effect, and the impact of the neighbouring nodes, the network effect. It is interpreted as a continuous-time extension to the discrete-time Network Vector Autoregression (NAR) model (Zhu et al 2017)-a model used for the modelling of social media (Zhu et al 2020), financial returns (Chen et al 2020) or risk dynamics (Chen et al 2019). We also introduce an alternative formulation with separate momentum and network effects for each node for a finer modelling of the graph dynamics.…”

Section: Introductionmentioning

confidence: 99%

Likelihood theory for the graph Ornstein-Uhlenbeck process

Courgeau

Veraart

2021

Stat Inference Stoch Process

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We consider the problem of modelling restricted interactions between continuously-observed time series as given by a known static graph (or network) structure. For this purpose, we define a parametric multivariate Graph Ornstein-Uhlenbeck (GrOU) process driven by a general Lévy process to study the momentum and network effects amongst nodes, effects that quantify the impact of a node on itself and that of its neighbours, respectively. We derive the maximum likelihood estimators (MLEs) and their usual properties (existence, uniqueness and efficiency) along with their asymptotic normality and consistency. Additionally, an Adaptive Lasso approach, or a penalised likelihood scheme, infers both the graph structure along with the GrOU parameters concurrently and is shown to satisfy similar properties. Finally, we show that the asymptotic theory extends to the case when stochastic volatility modulation of the driving Lévy process is considered.

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“…Multi-task learning has been widely used in various fields, such as bioinformatics [Kim et al, 2009, Hilafu et al, 2020, econometrics , social network analysis [Zhu et al, 2020], and recommender systems [Zhu et al, 2016], when one is interested in uncovering the association between multiple responses and a single set of predictor variables. Multiresponse regression is one of the most important tools in multi-task learning.…”

Section: Introductionmentioning

confidence: 99%

Parallel integrative learning for large-scale multi-response regression with incomplete outcomes

Dong

Zheng

2021

Computational Statistics & Data Analysis

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Multi-task learning is increasingly used to investigate the association structure between multiple responses and a single set of predictor variables in many applications. In the era of big data, the coexistence of incomplete outcomes, large number of responses, and high dimensionality in predictors poses unprecedented challenges in estimation, prediction and computation. In this paper, we propose a scalable and computationally efficient procedure, called PEER, for large-scale multi-response regression with incomplete outcomes, where both the numbers of responses and predictors can be high-dimensional. Motivated by sparse factor regression, we convert the multi-response regression into a set of univariate-response regressions, which can be efficiently implemented in parallel. Under some mild regularity conditions, we show that PEER enjoys nice sampling properties including consistency in estimation, prediction, and variable selection. Extensive simulation studies show that our proposal compares favorably with several existing methods in estimation accuracy, variable selection, and computation efficiency.

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Multivariate spatial autoregressive model for large scale social networks

Cited by 30 publications

References 27 publications

Network Influence Analysis

Network Influence Analysis

Likelihood theory for the graph Ornstein-Uhlenbeck process

Parallel integrative learning for large-scale multi-response regression with incomplete outcomes

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