检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

4 篇 会议

馆藏范围

4 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

4 篇 工学
- 4 篇 计算机科学与技术...
1 篇 管理学
- 1 篇 管理科学与工程(可...

主题

4 篇 anytime anywhere...
3 篇 social network a...
2 篇 centrality analy...
2 篇 parallel and dis...
2 篇 dynamic graphs
2 篇 graph algorithms...
1 篇 buffer-based met...
1 篇 large-scale dyna...
1 篇 parallel/distrib...
1 篇 dynamic vertex a...
1 篇 memory efficient...
1 篇 parallel/distrib...
1 篇 large-scale dyna...
1 篇 performance anal...
1 篇 memory scalabili...

机构

2 篇 iit dept comp sc...
2 篇 univ illinois sc...
2 篇 calif state poly...

作者

4 篇 murugappan vaira...
4 篇 korah john
4 篇 santos eunice e.
1 篇 subramanian sure...

语言

4 篇 英文

检索条件"主题词=Anytime Anywhere algorithms"

共 4 条记录，以下是1-10 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Efficient anytime anywhere algorithms for Vertex Additions in Large and Dynamic Graphs 31

Efficient Anytime Anywhere Algorithms for Vertex Additions i...

引用

31st IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan Subramanian, Suresh IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9780769561493

Over the past decade, there has been a dramatic increase in the availability of large and dynamic social network datasets. Conducting social network analysis (SNA) on these networks is critical for understanding underlying social phenomena. However, continuously evolving graph structures require massive recomputations and conducting SNA is infeasible if the computations have to be restarted for every change. Many recent works have proposed large-scale graph processing systems and frameworks, but less attention has been given to scalable SNA algorithm designs that can efficiently adapt to dynamic graph changes. Moreover, continuously adapting to dynamic graph changes such as node/vertex/actor additions/deletions in a parallel/distributed computational environment can skew the initial graph partitions, leading to load imbalance issues and performance degradation. Previous approaches that focus on computing SNA measures on dynamic graphs either ignore this critical load-balancing aspect or focus only on measures that are straightforward and inherently adjustable to changes in the graph topology. In this work, we have designed an anytime anywhere closeness centrality algorithm that can efficiently incorporate vertex additions while avoiding massive recomputations, by leveraging a generic framework for designing parallel/distributed algorithms called anytime anywhere. Furthermore, we have also performed an analysis of the effectiveness of various processor assignment strategies to mitigate the load imbalances caused by dynamic graph changes.

关键词： social network analysis parallel and distributed processing centrality analysis dynamic graphs anytime anywhere algorithms dynamic vertex addition

来源：评论

学校读者我要写书评

暂无评论

New Approaches for Performance Optimization and Analysis of Large-Scale Dynamic Social Network Analysis using anytime anywhere algorithms 34

New Approaches for Performance Optimization and Analysis of ...

引用

34th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Murugappan, Vairavan Korah, John Univ Illinois Sch Informat Sci Champaign IL 61820 USA Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA

ISBN: (纸本)9781728174457

During the last decade, the availability of large amounts of social network information from various social and socio-technical networks has increased dramatically. These data sources are inherently dynamic with constantly evolving relationships and connections between entities. Research in this area must address the challenge of analyzing these dynamic datasets under potentially strict time constraints. In addition, due to the sheer size of these networks, they tend to be stored and analyzed on distributed platforms. In our previous work, we designed methodologies which are anytime and anywhere to design scalable parallel/distributed algorithms that incorporate different forms of network changes. In this work, we will investigate various schemas to balance the incorporation of dynamic network changes that will substantially reduce idleness and load imbalances among processors. We will show theoretically that in most cases our buffer-based methodology performs better than the more common way of handling changes as they come in.

关键词： anytime anywhere algorithms Buffer-based methods Graph algorithms and analysis Large-scale dynamic social network analysis Parallel/Distributed algorithms Performance analysis

来源：评论

学校读者我要写书评

暂无评论

Handling Vertex Deletions in Memory Scalable anytime anywhere algorithms for Large and Dynamic Social Networks 32

Handling Vertex Deletions in Memory Scalable Anytime Anywher...

引用

32nd IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Korah, John Murugappan, Vairavan IIT Dept Comp Sci Chicago IL 60616 USA

ISBN: (纸本)9781538655559

Due to the dramatic increase in the availability of dynamic data in various domains, including computational social systems, there is a need to formulate processing and analysis methodologies that can efficiently incorporate data changes while reducing recomputations. This is especially critical for network analysis techniques where current methodologies pursue strategies based on maintaining intermediate or partial results. However, there are critical trade-offs with respect to memory and processing time overheads that have prevented these designs from scaling with larger network sizes and higher rates of network dynamism. In this work, we demonstrate the capability of our anytime anywhere framework to formulate closeness centrality algorithms that can adapt to memory availability by varying the number of partial results that are stored and maintained over the course of the analysis. Additionally, using a combination of theoretical analysis and experimental results we compare the performance of these algorithm designs, and identify conditions under which one design performs better than the others.

关键词： social network analysis parallel and distributed processing memory scalability centrality analysis dynamic graphs anytime anywhere algorithms

来源：评论

学校读者我要写书评

暂无评论

Memory Efficient Edge Addition Designs for Large and Dynamic Social Networks

Memory Efficient Edge Addition Designs for Large and Dynamic...

引用

35th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Santos, Eunice E. Murugappan, Vairavan Korah, John Univ Illinois Sch Informat Sci Champaign IL 61820 USA Calif State Polytech Univ Pomona Dept Comp Sci Pomona CA 91768 USA

ISBN: (纸本)9781665435772

The availability of large volumes of social network data from a variety of social and socio-technical networks has greatly increased. These networks provide critical insights into understanding various domains including business, healthcare, and disaster management. The relationships and interactions between different entities represented in most of these data sources are constantly evolving. Graph processing and analysis methodologies that can effectively integrate data changes while minimizing recomputations are needed to handle these dynamic networks. In addition, the size of these information sources is constantly increasing, therefore we need designs that can perform analysis that are memory efficient in order to address resource constraints. In this paper, we show how our anytime anywhere framework can be used to construct memory-efficient closeness centrality algorithms. In particular, we will show how dynamic edge additions can be efficiently handled in the proposed scheme.

关键词： anytime anywhere algorithms Memory efficient graph algorithms Graph algorithms and analysis Large-scale dynamic social network analysis Parallel/Distribated algorithms

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共1页 << < 1 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：