On structural properties of scale-free networks with finite size

Since many large real networks tend to present scale-free degree distribution, this paper investigates the structural properties of scale-free networks with finite size. Beginning with a comprehensive analysis of the degree distribution consisting of the concentration trend, dispersion and inequality, this paper then focuses on the discussion of heterogeneity and hub nodes of scale-free networks. The findings will help to improve our understanding of the structure and function of real networks.     

Identifying all-around nodes for spreading dynamics in complex networks

Identifying the most influential nodes in complex networks provides a strong basis for understanding spreading dynamics and ensuring more efficient spread of information. Due to the heterogeneous degree distribution, we observe that current centrality measures are correlated in their results of nodes ranking. This paper introduces the concept of all-around nodes, which act like all-around players with good performance in combined metrics. Then, an all-around distance is presented for quantifying the influence of nodes. The experimental results of susceptible-infectious-recovered (SIR) dynamics suggest that the proposed all-around distance can act as a more accurate, stable indicator of influential nodes.     

Examining Supervised Machine Learning Methods for Integer Link Weight Prediction Using Node Metadata

With the goal of understanding if the information contained in node metadata can help in the task of link weight prediction, we investigate herein whether incorporating it as a similarity feature (referred to as metadata similarity) between end nodes of a link improves the prediction accuracy of common supervised machine learning methods. In contrast with previous works, instead of normalizing the link weights, we treat them as count variables representing the number of interactions between end nodes, as this is a natural representation for many datasets in the literature. In this preliminary study, we find no significant evidence that metadata similarity improved the prediction accuracy of the four empirical datasets studied. To further explore the role of node metadata in weight prediction, we synthesized weights to analyze the extreme case where the weights depend solely on the metadata of the end nodes, while encoding different relationships between them using logical operators in the generation process. Under these conditions, the random forest method performed significantly better than other methods in 99.07% of cases, though the prediction accuracy was significantly degraded for the methods analyzed in comparison to the experiments with the original weights.     

Scalably Using Node Attributes and Graph Structure for Node Classification

The task of node classification concerns a network where nodes are associated with labels, but labels are known only for some of the nodes. The task consists of inferring the unknown labels given the known node labels, the structure of the network, and other known node attributes. Common node classification approaches are based on the assumption that adjacent nodes have similar attributes and, therefore, that a node’s label can be predicted from the labels of its neighbors. While such an assumption is often valid (e.g., for political affiliation in social networks), it may not hold in some cases. In fact, nodes that share the same label may be adjacent but differ in their attributes, or may not be adjacent but have similar attributes. In this work, we present JANE (Jointly using Attributes and Node Embeddings), a novel and principled approach to node classification that flexibly adapts to a range of settings wherein unknown labels may be predicted from known labels of adjacent nodes in the network, other node attributes, or both. Our experiments on synthetic data highlight the limitations of benchmark algorithms and the versatility of JANE. Further, our experiments on seven real datasets of sizes ranging from 2.5K to 1.5M nodes and edge homophily ranging from 0.86 to 0.29 show that JANE scales well to large networks while also demonstrating an up to 20% improvement in accuracy compared to strong baseline algorithms.     

基于FDLs的边缘汇聚节点的性能研究

随着全光接入网的不断发展,接入速率不断提高,对于边缘节点发送来的高速数据业务也必须进行流量梳理。为了适应未来全光高速网络接入的需求,基于光纤延迟线(Fiber delay lines,FDLs)结构,构造了一种简单高效的全光包交换网络边缘汇聚节点。并针对光线延迟线结构,提出一种新的主动缓存队列管理模型来分析节点性能,得到了与以往研究不同的结果。同时在对节点性能分析的基础上,提出一种新的FDLs的排列方式,在缓存深度相同的情况下,大大提高了FDLs的效率,实现了对到来的具有自相似性的数据业务的合理调配。     

改进的泡泡布点方法研究

泡泡布点方法能够在复杂区域内生成高质量的节点集，但是其计算效率仍有待提高，为此本文做了两方面的改进。一是种子填充算法思想应用于节点的初始布置中，从而生成数目合适的节点集，省去了原布点方法中的节点增删过程，有效地节约模拟时间；二是简化了泡泡运动模拟的控制方程，使得运动模拟更加简单。数值算例表明，改进的泡泡布点方法生成的网格平均质量均高于0.94,且计算效率相比原布点方法提高90%以上。因此，改进的泡泡布点方法是一种高质量、高效率且适宜求解大规模问题的节点布置方法。