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
针对不同应用场景的用户利用底层网络资源不充分的问题,提出一种利用网络切片技术对切片进行准入控制和资源分配联合算法(Joint Access Control and Resource Allocation Algorithm for Slicing,JACRAAS)。在第五代移动通信技术(5th Gen...针对不同应用场景的用户利用底层网络资源不充分的问题,提出一种利用网络切片技术对切片进行准入控制和资源分配联合算法(Joint Access Control and Resource Allocation Algorithm for Slicing,JACRAAS)。在第五代移动通信技术(5th Generation Mobile Communication Technology,5G)的演进(5G-Advanced,5G-A)标准下,通过最大化网络切片提供商(Network Slicing Provider,NSP)的收益,使用双深度Q网络算法对网络切片请求进行智能高效的准入控制和资源分配,并对重要经验优先回放,拒绝不满足条件的切片请求。同时,考虑网络拓扑对节点的影响,对重要节点优先排序,并进行节点映射和链路映射。仿真结果表明,所提算法与深度Q网络算法和Q学习算法相比,NSP收益成本比分别提高了9%和15%,资源利用率分别提升了10%和14%,所提算法可以显著提高底层资源的利用率。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.
文摘针对不同应用场景的用户利用底层网络资源不充分的问题,提出一种利用网络切片技术对切片进行准入控制和资源分配联合算法(Joint Access Control and Resource Allocation Algorithm for Slicing,JACRAAS)。在第五代移动通信技术(5th Generation Mobile Communication Technology,5G)的演进(5G-Advanced,5G-A)标准下,通过最大化网络切片提供商(Network Slicing Provider,NSP)的收益,使用双深度Q网络算法对网络切片请求进行智能高效的准入控制和资源分配,并对重要经验优先回放,拒绝不满足条件的切片请求。同时,考虑网络拓扑对节点的影响,对重要节点优先排序,并进行节点映射和链路映射。仿真结果表明,所提算法与深度Q网络算法和Q学习算法相比,NSP收益成本比分别提高了9%和15%,资源利用率分别提升了10%和14%,所提算法可以显著提高底层资源的利用率。
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。
