Visual Mining of Multi-Modal Social Networks at Different Abstraction Levels

L. Singh, Mitchell Beard, L. Getoor, M. Brian Blake
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引用次数: 47

Abstract

Social networks continue to become more and more feature rich. Using local and global structural properties and descriptive attributes are necessary for more sophisticated social network analysis and support for visual mining tasks. While a number of visualization tools for social network applications have been developed, most of them are limited to uni-modal graph representations. Some of the tools support a wide range of visualization options, including interactive views. Others have better support for calculating structural graph properties such as the density of the graph or deploying traditional statistical social network analysis. We present Invenio, a new tool for visual mining of socials. Invenio integrates a wide range of interactive visualization options from Prefuse, with graph mining algorithm support from JUNG. While the integration expands the breadth of functionality within the core engine of the tool, our goal is to interactively explore multi-modal, multi-relational social networks. Invenio also supports construction of views using both database operations and basic graph mining operations.
不同抽象层次的多模态社会网络可视化挖掘
社交网络的功能越来越丰富。使用局部和全局结构属性和描述性属性对于更复杂的社会网络分析和支持视觉挖掘任务是必要的。虽然已经开发了许多用于社交网络应用程序的可视化工具,但大多数工具仅限于单模态图表示。其中一些工具支持广泛的可视化选项,包括交互式视图。其他的则对计算结构图属性(如图的密度)或部署传统的统计社会网络分析有更好的支持。我们介绍Invenio,一个新的社交可视化挖掘工具。Invenio集成了来自Prefuse的各种交互式可视化选项,以及来自JUNG的图形挖掘算法支持。虽然集成扩展了工具核心引擎内的功能广度,但我们的目标是交互式地探索多模态、多关系的社交网络。Invenio还支持使用数据库操作和基本的图挖掘操作来构建视图。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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