Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network

Shen, Yulong; Jin, Ruoming; Dou, Dejing; Chowdhury, Nafisa; Sun, Junfeng; Piniewski, Brigitta; Keator, David B.

doi:10.1109/icdm.2012.94

Cited by 19 publications

(21 citation statements)

References 5 publications

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“…That is useful for physical activity intervention approaches to design more effective strategies. The third observation might be exploited to categorize users or to predict user macro-activities based on their influence behaviors [23].…”

Section: Discussionmentioning

confidence: 99%

Analysis of Physical Activity Propagation in a Health Social Network

Phan

Dou

Xiao

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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Modeling physical activity propagation, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. However, there has been lacking of scientific and quantitative study to elucidate how social communication may deliver physical activity interventions. In this work we introduce a Community-level Physical Activity Propagation (CPP) model to analyze physical activity propagation and social influence at different granularities (i.e., individual level and community level). CPP is a novel model which is inspired by the well-known Independent Cascade and Community-level Social Influence models. Given a social network, we utilize a hierarchical approach to detect a set of communities and their reciprocal influence strength of physical activities. CPP provides a powerful tool to discover, summarize, and investigate influence patterns of physical activities in a health social network. The detail experimental evaluation shows not only the effectiveness of our approach but also the correlation of the detected communities with various health outcome measures (i.e., both existing ones and our novel measure, named Wellness score, which is a combination of lifestyle parameters, biometrics, and biomarkers). Our promising results potentially pave a way for knowledge discovery in health social networks.

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Section: Discussionmentioning

confidence: 99%

Analysis of Physical Activity Propagation in a Health Social Network

Phan

Dou

Xiao

et al. 2014

Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management

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“…We compare the SRBM model with the conventional methods reported in [18]. In addition, we also consider the RBM related extensions, i.e., the CRBM and ctRBM, as competitive models.…”

Section: Methodsmentioning

confidence: 99%

“…Meanwhile our models enrich the application area by incorporating various personal features. The work most closely related to our study is the Socialized Gaussian Process Model (SGP) [18].…”

Section: The Rbms and Related Workmentioning

confidence: 99%

Social Restricted Boltzmann Machine

Phan

Dou

Piniewski³

et al. 2015

Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015

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Modeling and predicting human behaviors, such as the activity level and intensity, is the key to prevent the cascades of obesity, and help spread wellness and healthy behavior in a social network. The user diversity, dynamic behaviors, and hidden social influences make the problem more challenging. In this work, we propose a deep learning model named Social Restricted Boltzmann Machine (SRBM) for human behavior modeling and prediction in health social networks. In the proposed SRBM model, we naturally incorporate self-motivation, implicit and explicit social influences, and environmental events together into three layers which are historical, visible, and hidden layers. The interactions among these behavior determinants are naturally simulated through parameters connecting these layers together. The contrastive divergence and back-propagation algorithms are employed for training the model. A comprehensive experiment on real and synthetic data has shown the great effectiveness of our deep learning model compared with conventional methods.

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“…The resulting model bears similarities to a socialized Gaussian Process [13,14]. For inference, we use a Bayesian method that works directly on z dn and show that this model does not suffer from the problem of hyperparameters setting that affects MF + LIN(r).…”

Section: A Models and Inference Methodsmentioning

confidence: 99%

Online Collaborative Prediction of Regional Vote Results

Etter

Khan

Grossglauser

et al. 2016

2016 IEEE International Conference on Data Science and Advanced Analytics (DSAA)

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Abstract-We consider online predictions of vote results, where regions across a country vote on an issue under discussion. Such online predictions before and during the day of the vote are useful to media agencies, polling institutes, and political parties, e.g., to identify regions that are crucial in determining the national outcome of a vote. We analyze a unique dataset from Switzerland. The dataset contains 281 votes from 2352 regions over a period of 34 years. We make several contributions towards improving online predictions. First, we show that these votes exhibit a bi-clustering of the vote results, i.e., regions that are spatially close tend to vote similarly, and issues that discuss similar topics show similar global voting patterns. Second, we develop models that can exploit this bi-clustering, as well as the features associated with the votes and regions. Third, we show that, when combining vote results and features together, Bayesian methods are essential to obtaining good performance. Our results show that Bayesian methods give better estimates of the hyperparameters than non-Bayesian methods such as cross-validation. The resulting models generalize well to many different tasks, produce robust predictions, and are easily interpretable.

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Socialized Gaussian Process Model for Human Behavior Prediction in a Health Social Network

Cited by 19 publications

References 5 publications

Analysis of Physical Activity Propagation in a Health Social Network

Analysis of Physical Activity Propagation in a Health Social Network

Social Restricted Boltzmann Machine

Online Collaborative Prediction of Regional Vote Results

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