III: Medium: Collaborative Research: Geometric Network Analysis Tools: Algorithmic Methods for Identifying Structure in Large Informatics Graphs
III:媒介:协作研究:几何网络分析工具:识别大型信息学图中结构的算法方法
基本信息
- 批准号:0964242
- 负责人:
- 金额:$ 78.14万
- 依托单位:
- 依托单位国家:美国
- 项目类别:Continuing Grant
- 财政年份:2010
- 资助国家:美国
- 起止时间:2010-07-01 至 2014-06-30
- 项目状态:已结题
- 来源:
- 关键词:
项目摘要
There has been an enormous amount of work in recent years directedtoward understanding the structural and dynamical properties of"informatics graphs" or "complex networks." Most of this work has beenon small to medium-sized networks, and it has led to an improvedunderstanding of the properties of networks arising in many graphmining applications. In spite of this, formulating appropriate modelsfor and answering even basic questions about larger informaticsgraphs remains challenging. For instance, recent work has shown thatdynamic properties as well as basic structural properties of largeinformatics graphs are not reproduced even qualitatively by popularnetwork generative models.The proposed work will use traditional and recently-developedapproximation algorithms for the graph partitioning problem as"experimental probes" of large informatics graphs in order tocharacterize in a more robust and scalable manner the structural anddynamic properties of very large informatics graphs. This willinclude extending and implementing recently-developed algorithms suchas "local" spectral methods and algorithms that intuitively"interpolate" between spectral and flow-based methods, as well asrevisiting in light of new applications traditional methods such asthe global spectral method and ideas underlying the popular packageMetis. A central goal will be to provide the analyst with tools thathave sufficient algorithmic and statistical flexibility tocharacterize the local and global structures of large networks in arich and robust way.The Intellectual Merit of the proposed work lies in extending recenttheoretical and algorithmic developments and applying them to veryreal-world problems. The Broader Impact of the project lies inenhancing interdisciplinary education at Berkeley and Stanford andmore generally. This will involve the organization of meetings andcourses that will include the opportunity for research projects,including by students from underrepresented groups, that focus onbridging theoretical methods and real-world applications. Forfurther information see the project web page:URL: http://cs.stanford.edu/people/mmahoney/graphmining/
近年来,有大量的工作致力于理解“信息图”或“复杂网络”的结构和动力学性质。“这项工作的大部分都是在中小型网络上进行的,它提高了人们对许多图挖掘应用中出现的网络属性的理解。 尽管如此,为更大的信息图制定适当的模型并回答甚至是基本的问题仍然具有挑战性。 比如说,最近的工作表明,大型信息图的动态特性以及基本结构特性甚至不能被流行的网络生成模型定性地再现。本文将使用传统的和最近发展的图划分问题的近似算法作为“实验探针”。的大型信息图,以便在一个更强大的和可扩展的方式来表征非常大的信息图的结构和动态特性。 这将包括扩展和实施最近开发的算法,如“本地”光谱方法和算法,直观地“插值”之间的光谱和基于流的方法,以及重新访问新的应用传统的方法,如全球光谱方法和思想的基础上流行的packageMetis。 一个中心目标是为分析人员提供具有足够的算法和统计灵活性的工具,以丰富和鲁棒的方式表征大型网络的局部和全局结构。所提出的工作的智力价值在于扩展最近的理论和算法发展,并将其应用于非常现实的问题。 该项目更广泛的影响在于加强伯克利和斯坦福大学的跨学科教育。 这将涉及组织会议和课程,其中包括研究项目的机会,包括来自代表性不足群体的学生,重点是弥合理论方法和现实世界的应用。 欲了解更多信息,请参阅项目网页:URL:http://cs.stanford.edu/people/mmahoney/graphmining/
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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Gunnar Carlsson其他文献
The Role of Geometry in Convolutional Neural Networks for Medical Imaging
几何在医学成像卷积神经网络中的作用
- DOI:
- 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Yashbir Singh;Colleen Farrelly;Quincy A. Hathaway;Ashok Choudhary;Gunnar Carlsson;Bradley Erickson;T. Leiner - 通讯作者:
T. Leiner
Current Topological and Machine Learning Applications for Bias Detection in Text
当前用于文本偏差检测的拓扑和机器学习应用
- DOI:
10.1109/icspis60075.2023.10343824 - 发表时间:
2023 - 期刊:
- 影响因子:0
- 作者:
Colleen Farrelly;Yashbir Singh;Quincy A. Hathaway;Gunnar Carlsson;Ashok Choudhary;Rahul Paul;Gianfranco Doretto;Yassine Himeur;Shadi Atalla;W. Mansoor - 通讯作者:
W. Mansoor
Topological methods for data modelling
用于数据建模的拓扑方法
- DOI:
10.1038/s42254-020-00249-3 - 发表时间:
2020-11-10 - 期刊:
- 影响因子:39.500
- 作者:
Gunnar Carlsson - 通讯作者:
Gunnar Carlsson
The shape of biomedical data
- DOI:
10.1016/j.coisb.2016.12.012 - 发表时间:
2017-02-01 - 期刊:
- 影响因子:
- 作者:
Gunnar Carlsson - 通讯作者:
Gunnar Carlsson
The integral K-theoretic Novikov conjecture for groups with finite asymptotic dimension THANKSREF="*" ID="*"The authors gratefully acknowledge support from the National Science Foundation.
- DOI:
10.1007/s00222-004-0401-4 - 发表时间:
2004-12-22 - 期刊:
- 影响因子:3.600
- 作者:
Gunnar Carlsson;Boris Goldfarb - 通讯作者:
Boris Goldfarb
Gunnar Carlsson的其他文献
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{{ truncateString('Gunnar Carlsson', 18)}}的其他基金
III: Workshop support for meeting on algorithms for modern massive data sets, MMDS 2010
III:为现代海量数据集算法会议提供研讨会支持,MMDS 2010
- 批准号:
0949412 - 财政年份:2009
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
Investigations in the application of homotopy theory
同伦理论的应用研究
- 批准号:
0905823 - 财政年份:2009
- 资助金额:
$ 78.14万 - 项目类别:
Continuing Grant
Special Meeting: Fields Program in Geometric Applications of Homotopy Theory - International US Participation
特别会议:同伦理论几何应用领域计划 - 国际美国参与
- 批准号:
0603411 - 财政年份:2006
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
FRG: Algebraic topology as a tool in feature location, feature classification, shape recognition, and shape description
FRG:代数拓扑作为特征定位、特征分类、形状识别和形状描述的工具
- 批准号:
0354543 - 财政年份:2004
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
Homotopy Theoretic Investigations in Higher K-theory, High-dimensional Data Analysis, and High Dimensional Manifold Theory
高阶 K 理论、高维数据分析和高维流形理论中的同伦理论研究
- 批准号:
0406992 - 财政年份:2004
- 资助金额:
$ 78.14万 - 项目类别:
Continuing Grant
Algebraic Topological Methods in Computer Science
计算机科学中的代数拓扑方法
- 批准号:
0106804 - 财政年份:2001
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
Representation of Galois groups and descent in algebraic K-theory
代数 K 理论中伽罗瓦群的表示和下降
- 批准号:
0104162 - 财政年份:2001
- 资助金额:
$ 78.14万 - 项目类别:
Continuing Grant
FRG: Topological methods in data analysis
FRG:数据分析中的拓扑方法
- 批准号:
0101364 - 财政年份:2001
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
Equivariant stable homotopy theory and K-theory
等变稳定同伦理论和K理论
- 批准号:
0075689 - 财政年份:2000
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
Topology, Geometry and Algebra: Interactions and New Directions
拓扑、几何和代数:相互作用和新方向
- 批准号:
9970944 - 财政年份:1999
- 资助金额:
$ 78.14万 - 项目类别:
Standard Grant
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