2011 IEEE Conference on Visual Analytics Science and Technology (VAST)最新文献_第6页

Query-based coordinated multiple views with Feature Similarity Space for visual analysis of MRI repositories 基于查询的多视图协同特征相似空间的MRI库可视化分析

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102467

Ian Bowman, Shantanu H. Joshi, J. D. Horn

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引用次数: 8

G-PARE: A visual analytic tool for comparative analysis of uncertain graphs G-PARE:一个用于不确定图比较分析的可视化分析工具

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-12-15 DOI: 10.1109/VAST.2011.6102442

Hossam Sharara, Awalin Sopan, Galileo Namata, L. Getoor, L. Singh

{"title":"G-PARE: A visual analytic tool for comparative analysis of uncertain graphs","authors":"Hossam Sharara, Awalin Sopan, Galileo Namata, L. Getoor, L. Singh","doi":"10.1109/VAST.2011.6102442","DOIUrl":"https://doi.org/10.1109/VAST.2011.6102442","url":null,"abstract":"There are a growing number of machine learning algorithms which operate on graphs. Example applications for these algorithms include predicting which customers will recommend products to their friends in a viral marketing campaign using a customer network, predicting the topics of publications in a citation network, or predicting the political affiliations of people in a social network. It is important for an analyst to have tools to help compare the output of these machine learning algorithms. In this work, we present G-PARE, a visual analytic tool for comparing two uncertain graphs, where each uncertain graph is produced by a machine learning algorithm which outputs probabilities over node labels. G-PARE provides several different views which allow users to obtain a global overview of the algorithms output, as well as focused views that show subsets of nodes of interest. By providing an adaptive exploration environment, G-PARE guides the users to places in the graph where two algorithms predictions agree and places where they disagree. This enables the user to follow cascades of misclassifications by comparing the algorithms outcome with the ground truth. After describing the features of G-PARE, we illustrate its utility through several use cases based on networks from different domains.","PeriodicalId":103998,"journal":{"name":"2011 IEEE Conference on Visual Analytics Science and Technology (VAST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125812583","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: 22

Analyst's workspace: Protecting vastopolis 分析师的工作空间:保护大都市

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-10-01 DOI: 10.1109/VAST.2011.6102495

Christopher Andrews, M. S. Hossain, S. Gad, Naren Ramakrishnan, Chris North

引用次数: 2

City sentinel - VAST 2011 mini challenge 1 award: “Outstanding integration of computational and visual methods” 城市哨兵- VAST 2011迷你挑战赛1奖:“计算与视觉方法的杰出结合”

2011 IEEE Conference on Visual Analytics Science and Technology (VAST) Pub Date : 2011-10-01 DOI: 10.1109/VAST.2011.6102485

N. Banfi, L. Dudas, Zsolt Fekete, J. Gobolos-Szabo, András Lukács, A. Nagy, A. Szabó, Z. Szabo, G. Szücs

引用次数: 0