Network Inference From Grouped Observations Using Hub Models

Zhao, Yunpeng; Weko, Charles

doi:10.5705/ss.202016.0397

Cited by 8 publications

(19 citation statements)

References 50 publications

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“…Other related work Such a phenomenon of grouped classes was also discussed by Bouchacourt et al [5] and Zhao [42]. However, they did not provide learning methods to eliminate domain-differences.…”

Section: Hybrid Methodsmentioning

confidence: 98%

Additive Adversarial Learning for Unbiased Authentication

Liang

Cao

Zhang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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Authentication is a task aiming to confirm the truth between data instances and personal identities. Typical authentication applications include face recognition, person re-identification, authentication based on mobile devices and so on. The recently-emerging data-driven authentication process may encounter undesired biases, i.e., the models are often trained in one domain (e.g., for people wearing spring outfits) while required to apply in other domains (e.g., they change the clothes to summer outfits). To address this issue, we propose a novel two-stage method that disentangles the class/identity from domaindifferences, and we consider multiple types of domaindifference. In the first stage, we learn disentangled representations by a one-versus-rest disentangle learning (OVRDL) mechanism. In the second stage, we improve the disentanglement by an additive adversarial learning (AAL) mechanism. Moreover, we discuss the necessity to avoid a learning dilemma due to disentangling causally related types of domain-difference. Comprehensive evaluation results demonstrate the effectiveness and superiority of the proposed method.

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Section: Hybrid Methodsmentioning

confidence: 98%

Additive Adversarial Learning for Unbiased Authentication

Liang

Cao

Zhang

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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“…Whitehead (2008) refers to each party as a group and Table 1 as a group-by-individual matrix. Zhao and Weko (2016) referred to this type of data as grouped data.…”

Section: Child Party Allison Drew Eliot Keith Rossmentioning

confidence: 99%

“…It remains unclear what model assumption justifies the network structure inferred by these measures. Zhao and Weko (2016) proposed a simplistic generating mechanism for grouped data based on a network structure. The hub model (HM) assumes that each observed group is the result of a leader bringing together a subset of the population.…”

Section: Child Party Allison Drew Eliot Keith Rossmentioning

confidence: 99%

“…Without any prior information, it is possible for any node in the group to be the central node for that group. Zhao and Weko (2016) use an Expectation-Maximization (EM) algorithm to identify the central node for each group. As n increases, the possibility for larger and larger groups also increases.…”

Section: Child Party Allison Drew Eliot Keith Rossmentioning

confidence: 99%

“…Sarah 1 1 0 0 0 1 1 2 0 1 1 0 1 1 3 1 0 1 1 1 0 observed behavior. In a previous article, Zhao and Weko (2016) developed a model which used the network as a parameter for the random generation of observed behavior.…”

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confidence: 99%

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Penalized component hub models

Weko

Zhao

2017

Social Networks

Self Cite

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Social network analysis presupposes that observed social behavior is influenced by an unobserved network. Traditional approaches to inferring the latent network use pairwise descriptive statistics that rely on a variety of measures of co-occurrence. While these techniques have proven useful in a wide range of applications, the literature does not describe the generating mechanism of the observed data from the network.In a previous article, the authors presented a technique which used a finite mixture model as the connection between the unobserved network and the observed social behavior. This model assumed that each group was the result of a star graph on a subset of the population. Thus, each group was the result of a leader who selected members of the population to be in the group. They called these hub models.This approach treats the network values as parameters of a model. However, this leads to a general challenge in estimating parameters which must be addressed. For small datasets there can be far more parameters to estimate than there are observations. Under these conditions, the estimated network can be unstable.In this article, we propose a solution which penalizes the number of nodes which can exert a leadership role. We implement this as a pseudo-Expectation Maximization algorithm.We demonstrate this technique through a series of simulations which show that when the number of leaders is sparse, parameter estimation is improved. Further, we apply this technique to a dataset of animal behavior and an example of recommender systems.

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Link predictions for incomplete network data with outcome misclassification

Zhang

et al. 2021

Statistics in Medicine

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Link prediction is a fundamental problem in network analysis. In a complex network, links can be unreported and/or under detection limits due to heterogeneous sources of noise and technical challenges during data collection. The incomplete network data can lead to an inaccurate inference of network based data analysis. We propose a parametric link prediction model and consider latent links as misclassified binary outcomes. We develop new algorithms to optimize model parameters and yield robust predictions of unobserved links. Theoretical properties of the predictive model are also discussed. We apply the new method to a partially observed social network data and incomplete brain network data. The results demonstrate that our method outperforms the existing latent‐link prediction methods.

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Network Inference From Grouped Observations Using Hub Models

Cited by 8 publications

References 50 publications

Additive Adversarial Learning for Unbiased Authentication

Additive Adversarial Learning for Unbiased Authentication

Penalized component hub models

Link predictions for incomplete network data with outcome misclassification

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