The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient appro...The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.展开更多
Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always ...Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.展开更多
在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol...在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。展开更多
链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考...链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.展开更多
通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数...通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数据。计算每个节点的重要性,利用节点的重要性和时间序列数据,通过特定的算法,获取每个节点的预测值,从而实现无特征网络链路的预测。实验结果表明,该方法预测评估结果的AUC值较高,能够更准确地预测网络中的链路连接。展开更多
文摘The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.
基金the Ministry of National Education,Turkey for financially supporting the first author’s PhD study at Newcastle University,UK.
文摘Node-link visual representation is a widely used tool that allows decision-makers to see details about a network through the appropriate choice of visual metaphor.However,existing visualization methods are not always effective and efficient in representing bivariate graph-based data.This study proposes a novel node-link visual model–visual entropy(Vizent)graph–to effectively represent both primary and secondary values,such as uncertainty,on the edges simultaneously.We performed two user studies to demonstrate the efficiency and effectiveness of our approach in the context of static nodelink diagrams.In the first experiment,we evaluated the performance of the Vizent design to determine if it performed equally well or better than existing alternatives in terms of response time and accuracy.Three static visual encodings that use two visual cues were selected from the literature for comparison:Width-Lightness,Saturation-Transparency,and Numerical values.We compared the Vizent design to the selected visual encodings on various graphs ranging in complexity from 5 to 25 edges for three different tasks.The participants achieved higher accuracy of their responses using Vizent and Numerical values;however,both Width-Lightness and Saturation-Transparency did not show equal performance for all tasks.Our results suggest that increasing graph size has no impact on Vizent in terms of response time and accuracy.The performance of the Vizent graph was then compared to the Numerical values visualization.The Wilcoxon signed-rank test revealed that mean response time in seconds was significantly less when the Vizent graphs were presented,while no significant difference in accuracy was found.The results from the experiments are encouraging and we believe justify using the Vizent graph as a good alternative to traditional methods for representing bivariate data in the context of node-link diagrams.
文摘在无线传感器(Wireless Sensors Networks,WSN)中,由于节点能量有限,可能导致节点过早死亡,引起网络结构发生变化,链路稳定性变差。针对该问题文中提出了一种基于链路预测和能量感知的机会路由协议ELPOR(Opportunistic Routing Protocol Based on Link Prediction and Energy Sensing,ELPOR).该协议综合考虑节点能量和各节点之间链路连接的概率,从潜在的候选转发集中选择一个中继节点,以实现能量的高效利用和数据的可靠传输。仿真结果表明,该协议能够有效均衡网络能耗、提高吞吐量和延长网络生存周期。
文摘链路预测是通过已知的网络拓扑和节点属性挖掘未来时刻节点潜在关系的重要手段,是预测缺失链路和识别虚假链路的有效方法,在研究社会网络结构演化中具有现实意义.传统的链路预测方法基于节点信息或路径信息相似性进行预测,然而,前者考虑指标单一导致预测精度受限,后者由于计算复杂度过高不适合在规模较大网络中应用.通过对网络拓扑结构的分析,本文提出一种基于节点交互度(interacting degree of nodes,IDN)的社会网络链路预测方法.该方法首先根据网络中节点间的路径特征,引入了节点效率的概念,从而提高对于没有公共邻居节点之间链路预测的准确性;为了进一步挖掘节点间共同邻居的相关属性,借助分析节点间共同邻居的拓扑结构,该方法还创新性地整合了路径特征和局部信息,提出了社会网络节点交互度的定义,准确刻画出节点间的相似度,从而增强网络链路的预测能力;最后,本文借助6个真实网络数据集对IDN方法进行验证,实验结果表明,相比于目前的主流算法,本文提出的方法在AUC和Precision两个评价指标上均表现出更优的预测性能,预测结果平均分别提升22%和54%.因此节点交互度的提出在链路预测方面具有很高的可行性和有效性.
文摘通信系统的无特征网络链路中,由于忽略了节点的特征属性,导致预测评估结果的曲线下面积(Area Under Curve,AUC)值较低。针对上述现象,提出融合节点重要性的通信系统链路预测方法。提取无特征网络中节点的局部特征,构建节点的时间序列数据。计算每个节点的重要性,利用节点的重要性和时间序列数据,通过特定的算法,获取每个节点的预测值,从而实现无特征网络链路的预测。实验结果表明,该方法预测评估结果的AUC值较高,能够更准确地预测网络中的链路连接。
