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    • 1. 发明授权
    • Content-based and time-evolving social network analysis
    • 基于内容和时间的社交网络分析
    • US08204988B2
    • 2012-06-19
    • US12552812
    • 2009-09-02
    • Ching-Yung LinSpyridon PapadimitrionJimeng SunKun-Lung Wu
    • Ching-Yung LinSpyridon PapadimitrionJimeng SunKun-Lung Wu
    • G06F15/173
    • G06K9/00677G06K9/6224G06Q30/02
    • System and method for modeling a content-based network. The method includes finding single mode clusters from among network (sender and recipient) and content dimensions represented as a tensor data structure. The method allows for derivation of useful cross-mode clusters (interpretable patterns) that reveal key relationships among user communities and keyword concepts for presentation to users in a meaningful and intuitive way. Additionally, the derivation of useful cross-mode clusters is facilitated by constructing a reduced low-dimensional representation of the content-based network. Moreover, the invention may be enhanced for modeling and analyzing the time evolution of social communication networks and the content related to such networks. To this end, a set of non-overlapping or possibly overlapping time-based windows is constructed and the analysis performed at each successive time interval.
    • 用于建模基于内容的网络的系统和方法。 该方法包括从网络(发送者和接收者)和表示为张量数据结构的内容维度中找到单一模式集群。 该方法允许推导出有用的交叉模式集群(可解释模式),其显示用户社区之间的关键关系和关键字概念,以有意义和直观的方式呈现给用户。 另外,通过构建基于内容的网络的减少的低维表示来促进有用的交叉模式集群的推导。 此外,本发明可以被增强用于建模和分析社交通信网络的时间演进和与这样的网络有关的内容。 为此,构建一组不重叠或可能重叠的基于时间的窗口,并且在每个连续的时间间隔执行分析。
    • 2. 发明申请
    • CONTENT-BASED AND TIME-EVOLVING SOCIAL NETWORK ANALYSIS
    • 基于内容和时间演化的社会网络分析
    • US20110055379A1
    • 2011-03-03
    • US12552812
    • 2009-09-02
    • Ching-Yung LinSpyridon PapadimitriouJimeng SunKun-Lung Wu
    • Ching-Yung LinSpyridon PapadimitriouJimeng SunKun-Lung Wu
    • G06F17/10G06F15/173
    • G06K9/00677G06K9/6224G06Q30/02
    • System and method for modeling a content-based network. The method includes finding single mode clusters from among network (sender and recipient) and content dimensions represented as a tensor data structure. The method allows for derivation of useful cross-mode clusters (interpretable patterns) that reveal key relationships among user communities and keyword concepts for presentation to users in a meaningful and intuitive way. Additionally, the derivation of useful cross-mode clusters is facilitated by constructing a reduced low-dimensional representation of the content-based network. Moreover, the invention may be enhanced for modeling and analyzing the time evolution of social communication networks and the content related to such networks. To this end, a set of non-overlapping or possibly overlapping time-based windows is constructed and the analysis performed at each successive time interval.
    • 用于建模基于内容的网络的系统和方法。 该方法包括从网络(发送者和接收者)和表示为张量数据结构的内容维度中找到单一模式集群。 该方法允许推导出有用的交叉模式集群(可解释模式),其显示用户社区之间的关键关系和关键字概念,以有意义和直观的方式呈现给用户。 另外,通过构建基于内容的网络的减少的低维表示来促进有用的交叉模式集群的推导。 此外,本发明可以被增强用于建模和分析社交通信网络的时间演进和与这样的网络有关的内容。 为此,构建一组不重叠或可能重叠的基于时间的窗口,并且在每个连续的时间间隔执行分析。
    • 4. 发明授权
    • Determining the importance of data items and their characteristics using centrality measures
    • 使用中心性措施确定数据项的重要性及其特征
    • US08818918B2
    • 2014-08-26
    • US13096220
    • 2011-04-28
    • Ching-Yung LinHanghang TongJimeng SunSpyridon PapadimitriouU Kang
    • Ching-Yung LinHanghang TongJimeng SunSpyridon PapadimitriouU Kang
    • G06F17/30G06F15/18
    • G06N5/003
    • Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.
    • 用于确定数据项的重要性的计算机实现的方法,系统和制造。 一种方法包括:(a)接收节点图; (b)近似一个节点的邻居节点数; 和(c)使用近似步骤计算节点与剩余节点的平均最短路径长度,其中该计算表明由节点表示的数据项的重要性。 另一种方法包括:(a)接收节点图; (b)构建节点图的分解线图; (c)计算分解线图中节点图形节点的入射边缘的固定概率,以及(d)计算与节点相关联的入射边缘的固定概率的总和,其中求和表示数据项的重要性 由节点表示。 两种方法都使用计算机设备进行至少一个步骤。
    • 5. 发明申请
    • DETERMINING THE IMPORTANCE OF DATA ITEMS AND THEIR CHARACTERISTICS USING CENTRALITY MEASURES
    • 确定数据项的重要性及其使用中心度量的特性
    • US20120278261A1
    • 2012-11-01
    • US13096220
    • 2011-04-28
    • Ching-Yung LinHanghang TongJimeng SunSpyridon PapadimitriouU Kang
    • Ching-Yung LinHanghang TongJimeng SunSpyridon PapadimitriouU Kang
    • G06F17/30G06F15/18
    • G06N5/003
    • Computer-implemented methods, systems, and articles of manufacture for determining the importance of a data item. A method includes: (a) receiving a node graph; (b) approximating a number of neighbor nodes of a node; and (c) calculating a average shortest path length of the node to the remaining nodes using the approximation step, where this calculation demonstrates the importance of a data item represented by the node. Another method includes: (a) receiving a node graph; (b) building a decomposed line graph of the node graph; (c) calculating stationary probabilities of incident edges of a node graph node in the decomposed line graph, and (d) calculating a summation of the stationary probabilities of the incident edges associated with the node, where the summation demonstrates the importance of a data item represented by the node. Both methods have at least one step carried out using a computer device.
    • 用于确定数据项的重要性的计算机实现的方法,系统和制造。 一种方法包括:(a)接收节点图; (b)近似一个节点的邻居节点数; 和(c)使用近似步骤计算节点与剩余节点的平均最短路径长度,其中该计算表明由节点表示的数据项的重要性。 另一种方法包括:(a)接收节点图; (b)构建节点图的分解线图; (c)计算分解线图中节点图形节点的入射边缘的固定概率,以及(d)计算与节点相关联的入射边缘的固定概率的总和,其中求和表示数据项的重要性 由节点表示。 两种方法都使用计算机设备进行至少一个步骤。