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Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network 基于图卷积神经网络的链路预测学习通用网络表示
社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2021.0001
Weiwei Gu;Fei Gao;Ruiqi Li;Jiang Zhang
{"title":"Learning Universal Network Representation via Link Prediction by Graph Convolutional Neural Network","authors":"Weiwei Gu;Fei Gao;Ruiqi Li;Jiang Zhang","doi":"10.23919/JSC.2021.0001","DOIUrl":"https://doi.org/10.23919/JSC.2021.0001","url":null,"abstract":"Network representation learning algorithms, which aim at automatically encoding graphs into low-dimensional vector representations with a variety of node similarity definitions, have a wide range of downstream applications. Most existing methods either have low accuracies in downstream tasks or a very limited application field, such as article classification in citation networks. In this paper, we propose a novel network representation method, named Link Prediction based Network Representation (LPNR), which generalizes the latest graph neural network and optimizes a carefully designed objective function that preserves linkage structures. LPNR can not only learn meaningful node representations that achieve competitive accuracy in node centrality measurement and community detection but also achieve high accuracy in the link prediction task. Experiments prove the effectiveness of LPNR on three real-world networks. With the mini-batch and fixed sampling strategy, LPNR can learn the embedding of large graphs in a few hours.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"43-51"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2021.0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 18
DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms DeepPredict：一个适用于在线住宿平台的区域偏好预测系统
社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2021.0004
Yihan Ma;Hua Sun;Yang Chen;Jiayun Zhang;Yang Xu;Xin Wang;Pan Hui
{"title":"DeepPredict: A Zone Preference Prediction System for Online Lodging Platforms","authors":"Yihan Ma;Hua Sun;Yang Chen;Jiayun Zhang;Yang Xu;Xin Wang;Pan Hui","doi":"10.23919/JSC.2021.0004","DOIUrl":"https://doi.org/10.23919/JSC.2021.0004","url":null,"abstract":"Online lodging platforms have become more and more popular around the world. To make a booking in these platforms, a user usually needs to select a city first, then browses among all the prospective options. To improve the user experience, understanding the zone preferences of a user's booking behavior will be helpful. In this work, we aim to predict the zone preferences of users when booking accommodations for the next travel. We have two main challenges: (1) The previous works about next information of Points Of Interest (POIs) recommendation are mainly focused on users' historical records in the same city, while in practice, the historical records of a user in the same city would be very sparse. (2) Since each city has its own specific geographical entities, it is hard to extract the structured geographical features of accommodation in different cities. Towards the difficulties, we propose DeepPredict, a zone preference prediction system. To tackle the first challenge, DeepPredict involves users' historical records in all the cities and uses a deep learning based method to process them. For the second challenge, DeepPredict uses HERE places API to get the information of POIs nearby, and processes the information with a unified way to get it. Also, the description of each accommodation might include some useful information, thus we use Sent2Vec, a sentence embedding algorithm, to get the embedding of accommodation description. Using a real-world dataset collected from Airbnb, DeepPredict can predict the zone preferences of users' next bookings with a remarkable performance. DeepPredict outperforms the state-of-the-art algorithms by 60% in macro F1-score.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"52-70"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/8964404/9355030/09355036.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Pandemic Policymaking 流行病政策制定
社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2021.0005
Philip D. Waggoner
{"title":"Pandemic Policymaking","authors":"Philip D. Waggoner","doi":"10.23919/JSC.2021.0005","DOIUrl":"https://doi.org/10.23919/JSC.2021.0005","url":null,"abstract":"This study leverages a high dimensional manifold learning design to explore the latent structure of the pandemic policymaking space only based on bill-level characteristics of pandemic-focused bills from 1973 to 2020. Results indicate the COVID-19 era of policymaking maps extremely closely onto prior periods of related policymaking. This suggests that there is striking uniformity in Congressional policymaking related to these types of large-scale crises over time, despite currently operating in a unique era of hyperpolarization, division, and ineffective governance.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"14-26"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2021.0005","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
How to Better Identify Venture Capital Network Communities: Exploration of A Semi-Supervised Community Detection Method 如何更好地识别风险投资网络社区——半监督社区检测方法的探索
社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSC.2020.0008
Hong Xiong;Ying Fan
{"title":"How to Better Identify Venture Capital Network Communities: Exploration of A Semi-Supervised Community Detection Method","authors":"Hong Xiong;Ying Fan","doi":"10.23919/JSC.2020.0008","DOIUrl":"https://doi.org/10.23919/JSC.2020.0008","url":null,"abstract":"In the field of Venture Capital (VC), researchers have found that VC companies are more likely to jointly invest with other VC companies. This paper attempts to realize a semi-supervised community detection of the VC network based on the data of VC networking and the list of industry leaders. The main research method is to design the initial label of community detection according to the evolution of components of the VC industry leaders. The results show that the community structure of the VC network has obvious distinguishing characteristics, and the aggregation of these communities is affected by the type of institution, the source of capital, the background of personnel, and the field of investment and the geographical position. Meanwhile, by comparing the results of the semi-supervised community detection algorithm with the results of community detection using extremal optimization, it can be shown to some extent that the semi-supervised community detection results in the VC network are more accurate and reasonable.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"27-42"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0008","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50426306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 3
Cover 盖
社会计算(英文) Pub Date : 2021-02-16 DOI: 10.23919/JSCTUP.2021.9355051
{"title":"Cover","authors":"","doi":"10.23919/JSCTUP.2021.9355051","DOIUrl":"https://doi.org/10.23919/JSCTUP.2021.9355051","url":null,"abstract":"","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"2 1","pages":"1-1"},"PeriodicalIF":0.0,"publicationDate":"2021-02-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSCTUP.2021.9355051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50225258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
A Pattern Recognition Framework for Detecting Changes in Chinese Internet Management System 一种用于检测中国互联网管理系统变化的模式识别框架
社会计算(英文) Pub Date : 2020-09-01 DOI: 10.23919/JSC.2020.0004
Yu-Sung Su;Yanqin Ruan;Siyu Sun;Yu-Tzung Chang
{"title":"A Pattern Recognition Framework for Detecting Changes in Chinese Internet Management System","authors":"Yu-Sung Su;Yanqin Ruan;Siyu Sun;Yu-Tzung Chang","doi":"10.23919/JSC.2020.0004","DOIUrl":"https://doi.org/10.23919/JSC.2020.0004","url":null,"abstract":"Past studies on the Chinese Internet management system have revealed a smart Internet management system that takes advantage of time to filter content with collective action potential. How and why such a system was institutionalized? We offer a historical institutional analysis to explain the way in which the system evolved. We implement social network analysis to examine the Weibo posts of recurrent events, the elections in Area A in 2016 and 2018, to identify pattern changes in the system. There are two aspects of the changes: the centralization of the command line to a single authority and the implementation of a discriminatory strategy to deal with the various online expressions together forming this intelligent system. The improved Chinese information surveillance system demonstrates both a top-down information management and a bottom-up opinion formation.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"1 1","pages":"28-39"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Diversity of Idea Flows and Economic Growth 思想流动的多样性与经济增长
社会计算(英文) Pub Date : 2020-09-01 DOI: 10.23919/JSC.2020.0007
Alex Pentland
{"title":"Diversity of Idea Flows and Economic Growth","authors":"Alex Pentland","doi":"10.23919/JSC.2020.0007","DOIUrl":"https://doi.org/10.23919/JSC.2020.0007","url":null,"abstract":"What role does access to diverse ideas play in economic growth? New forms of geo-located communications and economic data allow measurement of human interaction patterns and prediction of economic outcomes for individuals, communities, and nations at a fine granularity, with the strongest predictors of income, productivity, and growth being measures of diversity and frequency of physical interaction between communities (clusters of interaction). This finding provides both new investment opportunities and new methods of risk assessment. Access and use of these data raise privacy and security risks, and the final section of the paper describes how these challenges can be controlled.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"1 1","pages":"71-81"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0007","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 4
Measuring Cities with Software-Defined Sensors 用软件定义的传感器测量城市
社会计算(英文) Pub Date : 2020-09-01 DOI: 10.23919/JSC.2020.0003
Charlie Catlett;Pete Beckman;Nicola Ferrier;Howard Nusbaum;Michael E. Papka;Marc G. Berman;Rajesh Sankaran
{"title":"Measuring Cities with Software-Defined Sensors","authors":"Charlie Catlett;Pete Beckman;Nicola Ferrier;Howard Nusbaum;Michael E. Papka;Marc G. Berman;Rajesh Sankaran","doi":"10.23919/JSC.2020.0003","DOIUrl":"https://doi.org/10.23919/JSC.2020.0003","url":null,"abstract":"The Chicago Array of Things (AoT) project, funded by the US National Science Foundation, created an experimental, urban-scale measurement capability to support diverse scientific studies. Initially conceived as a traditional sensor network, collaborations with many science communities guided the project to design a system that is remotely programmable to implement Artificial Intelligence (AI) within the devices-at the “edge” of the network-as a means for measuring urban factors that heretofore had only been possible with human observers, such as human behavior including social interaction. The concept of “software-defined sensors” emerged from these design discussions, opening new possibilities, such as stronger privacy protections and autonomous, adaptive measurements triggered by events or conditions. We provide examples of current and planned social and behavioral science investigations uniquely enabled by software-defined sensors as part of the SAGE project, an expanded follow-on effort that includes AoT.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"1 1","pages":"14-27"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0003","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50424125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 20
Inaugural Message from Editors-in-Chief 总编辑致开幕词
社会计算(英文) Pub Date : 2020-09-01 DOI: 10.23919/JSC.2020.0001
{"title":"Inaugural Message from Editors-in-Chief","authors":"","doi":"10.23919/JSC.2020.0001","DOIUrl":"https://doi.org/10.23919/JSC.2020.0001","url":null,"abstract":"On behalf of the Editorial Board, it is our privilege to present the first issue of the Journal of Social Computing, affectionately shortened JoSoCo. Social computing concerns the intersection of social behavior and computational systems. Historically focused on recreating human social conventions and contexts through software and technology, we propose its expansion to the full interface between social interaction and computation.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"1 1","pages":"i-iv"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Darks and Stripes: Effects of Clothing on Weight Perception 深色和条纹：服装对体重感知的影响
社会计算(英文) Pub Date : 2020-09-01 DOI: 10.23919/JSC.2020.0006
Kirill Martynov;Kiran Garimella;Robert West
{"title":"Darks and Stripes: Effects of Clothing on Weight Perception","authors":"Kirill Martynov;Kiran Garimella;Robert West","doi":"10.23919/JSC.2020.0006","DOIUrl":"https://doi.org/10.23919/JSC.2020.0006","url":null,"abstract":"In many societies, appearing slim (corresponding to a small body-mass index) is considered attractive. The fashion industry has been attempting to cater to this trend by designing outfits that can enhance the appearance of slimness. Two anecdotal rules, widespread in the world of fashion, are to choose dark clothes and avoid horizontal stripes, in order to appear slim. Thus far, empirical evidence has been unable to conclusively determine the validity of these rules, and there is consequently much controversy regarding the impact of both color and patterns on the visual perception of weight. In this paper, we aim to close this gap by presenting the results from a series of large-scale crowdsourcing studies that investigate the above two claims. We gathered a dataset of around 1000 images of people from the Web together with their ground-truth weight and height as well as clothing attributes about colors and patterns. To elicit the effects of colors and patterns, we asked crowd workers to estimate the weight in each image. For the analysis, we controlled potential confounds by matching images in pairs where the two images differ with respect to color or pattern, but are similar with respect to other relevant aspects. We created image pairs in two ways: firstly, observationally, i.e., from two real images; and secondly, experimentally, by manipulating the color or pattern of clothing in a real image via photo editing. Based on our analysis, we conclude that dark clothes indeed decrease perceived weight slightly but statistically significantly, and horizontal stripes have no discernible effect compared to solid light-colored clothes. These results contribute to advancing the debate around the effect of specific clothing colors and patterns and thus provide empirical grounds for everyday fashion decisions. Moreover, our work gives an outlook on the vast opportunities of using crowd sourcing in the modern fashion industry.","PeriodicalId":67535,"journal":{"name":"社会计算(英文)","volume":"1 1","pages":"53-70"},"PeriodicalIF":0.0,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.23919/JSC.2020.0006","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50349373","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
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