Showing papers by "Pang-Ning Tan published in 2020"

Bursting the Filter Bubble: Fairness-Aware Network Link Prediction

Abstract: Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting links that may lead to increase segregation of the network—an effect known as filter bubble. In this study, we examine the filter bubble problem from the perspective of algorithm fairness and introduce a dyadic-level fairness criterion based on network modularity measure. We show how the criterion can be utilized as a postprocessing step to generate more heterogeneous links in order to overcome the filter bubble problem. In addition, we also present a novel framework that combines adversarial network representation learning with supervised link prediction to alleviate the filter bubble problem. Experimental results conducted on several real-world datasets showed the effectiveness of the proposed methods compared to other baseline approaches, which include conventional link prediction and fairness-aware methods for i.i.d data.

OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting

Abstract: Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane preparedness and mitigation of storm impacts. In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. OMuLeT employs an online learning with restart strategy to incrementally update the weights of the ensemble model combination as new observation data become available. It can also handle the varying dynamical models available for predicting the trajectories of different hurricanes. Experimental results using the Atlantic and Eastern Pacific hurricane data showed that OMuLeT significantly outperforms various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center (NHC), by more than 10% in terms of its 48-hour lead time forecasts.

Ecological prediction at macroscales using big data: Does sampling design matter?

Abstract: Although ecosystems respond to global change at regional to continental scales (i.e., macroscales), model predictions of ecosystem responses often rely on data from targeted monitoring of a small proportion of sampled ecosystems within a particular geographic area. In this study, we examined how the sampling strategy used to collect data for such models influences predictive performance. We subsampled a large and spatially extensive data set to investigate how macroscale sampling strategy affects prediction of ecosystem characteristics in 6,784 lakes across a 1.8-million-km2 area. We estimated model predictive performance for different subsets of the data set to mimic three common sampling strategies for collecting observations of ecosystem characteristics: random sampling design, stratified random sampling design, and targeted sampling. We found that sampling strategy influenced model predictive performance such that (1) stratified random sampling designs did not improve predictive performance compared to simple random sampling designs and (2) although one of the scenarios that mimicked targeted (non-random) sampling had the poorest performing predictive models, the other targeted sampling scenarios resulted in models with similar predictive performance to that of the random sampling scenarios. Our results suggest that although potential biases in data sets from some forms of targeted sampling may limit predictive performance, compiling existing spatially extensive data sets can result in models with good predictive performance that may inform a wide range of science questions and policy goals related to global change.

Increasing accuracy of lake nutrient predictions in thousands of lakes by leveraging water clarity data

Abstract: Aquatic scientists require robust, accurate information about nutrient concentrations and indicators of algal biomass in unsampled lakes in order to understand and predict the effects of global climate and land-use change. Historically, lake and landscape characteristics have been used as predictor variables in regression models to generate nutrient predictions, but often with significant uncertainty. An alternative approach to improve predictions is to leverage the observed relationship between water clarity and nutrients, which is *Correspondence: txw19@psu.edu Author Contribution Statement: All authors contributed to the development of the paper. T.W. and N.R.L. led the writing of the manuscript. E.M.S., E.M.H., N.B.W., and M.L.B. performed the statistical analysis. K.B.S.K., I.M., and J.S. performed database queries and summaries. All coauthors edited and contributed to writing. After the coleads, authors are listed in alphabetical order by groups according to level of contribution. Data Availability Statement: All data are available from the LAGOSNE R package (https://cran.r-project.org/web/packages/LAGOSNE). The code and data required to perform the analysis described in this article are located at https://doi.org/10.5281/zenodo.3484680. Associate editor: Mark Scheuerell Additional Supporting Information may be found in the online version of this article. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Improving Heart disease risk through quality-focused diet logging: pre-post study of a diet quality tracking app

Abstract: Diet-tracking mobile apps have been effective in behavior change. At the same time, quantity-focused diet tracking (e.g., calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet Quality--focusing on high-quality dietary patterns rather than quantifying diet into calories--has shown effectiveness in improving heart disease risk. Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on quality-focused food category consumptions, rather than detailed serving sizes. No studies have examined how mobile health apps focusing on diet quality can bring promising results on health outcomes and ease of adoption. We designed a mobile app to support the HHS informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of HHS. We measured the apps feasibility and efficacy on improving individuals clinical and behavioral factors affecting future heart disease risks and app use. We recruited 38 overweight or obese participants at high heart disease risk, who used the app for 5 weeks and measured weight, blood sugar, and blood pressure, HHS, and Diet Score (DS) measuring diet quality at baseline and the fifth week of the intervention. The majority used the application every week (84%) and significantly improved DS and HHS at the fifth week (p<0.05), although only 10 participants (31%) checked their risk scores more than once. Other outcomes did not show significant changes. Our study showed logging simplified diet quality significantly improved dietary behavior. The participants were not interested in seeing HHS, and the participants perceived logging diet categories irrelevant to improving HHS as important. We discuss the complexities of addressing health risks, quantity vs. quality-based health monitoring, and incorporating secondary behavior change goals that matter to users when designing mobile health.

Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App.

Abstract: Background: Diet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet quality—focusing on high-quality dietary patterns rather than quantifying diet into calories—has shown effectiveness in improving heart disease risk. The Healthy Heart Score (HHS) predicts 20-year cardiovascular risks based on the consumption of foods from quality-focused food categories, rather than detailed serving sizes. No studies have examined how mobile health (mHealth) apps focusing on diet quality can bring promising results in health outcomes and ease of adoption. Objective: This study aims to design a mobile app to support the HHS-informed quality-focused dietary approach by enabling users to log simplified diet quality and view its real-time impact on future heart disease risks. Users were asked to log food categories that are the main predictors of the HHS. We measured the app’s feasibility and efficacy in improving individuals’ clinical and behavioral factors that affect future heart disease risks and app use. Methods: We recruited 38 participants who were overweight or obese with high heart disease risk and who used the app for 5 weeks and measured weight, blood sugar, blood pressure, HHS, and diet score (DS)—the measurement for diet quality—at baseline and week 5 of the intervention. Results: Most participants (30/38, 79%) used the app every week and showed significant improvements in DS (baseline: mean 1.31, SD 1.14; week 5: mean 2.36, SD 2.48; 2-tailed t test t29=−2.85; P=.008) and HHS (baseline: mean 22.94, SD 18.86; week 4: mean 22.15, SD 18.58; t29=2.41; P=.02) at week 5, although only 10 participants (10/38, 26%) checked their HHS risk scores more than once. Other outcomes, including weight, blood sugar, and blood pressure, did not show significant changes. Conclusions: Our study showed that our logging tool significantly improved dietary choices. Participants were not interested in seeing the HHS and perceived logging diet categories irrelevant to improving the HHS as important. We discuss the complexities of addressing health risks and quantity- versus quality-based health monitoring and incorporating secondary behavior change goals that matter to users when designing mHealth apps.

Celebration Drinking around the Clock.

Abstract: The fact that St. Patrick's Day (SPD) celebration drinking occurs during a specified, public, and socially-acceptable time frame which spans the better part of a day and evening makes it an important time to understand and attempt to influence celebration drinking behaviors among young adults. SPD has been identified as the celebration during which college students consume more alcohol than any other point during the school year. Intervention opportunities can be more successful with an understanding of the factors associated with alcohol consumption at specific times on particular celebrations. This study examined the factors associated with celebration drinking at different time periods on SPD which included perceived descriptive and injunctive norms, the numbers of close friends and acquaintances present, social media relationships, demographic variables, past drinking behavior, and intent to drink on SPD at the three time points of interest. Findings showed variability in the predictive factors on SPD celebration drinking at different times of the day. The theoretical and practical intervention implications of the findings are discussed.

Fairness Perception from a Network-Centric Perspective

Abstract: Algorithmic fairness is a major concern in recent years as the influence of machine learning algorithms becomes more widespread. In this paper, we investigate the issue of algorithmic fairness from a network-centric perspective. Specifically, we introduce a novel yet intuitive function known as network-centric fairness perception and provide an axiomatic approach to analyze its properties. Using a peer-review network as case study, we also examine its utility in terms of assessing the perception of fairness in paper acceptance decisions. We show how the function can be extended to a group fairness metric known as fairness visibility and demonstrate its relationship to demographic parity. We also illustrate a potential pitfall of the fairness visibility measure that can be exploited to mislead individuals into perceiving that the algorithmic decisions are fair. We demonstrate how the problem can be alleviated by increasing the local neighborhood size of the fairness perception function.