Classifying multiclass relationships between ASes using graph convolutional network

Precisely understanding the business relationships between autonomous systems (ASes) is essential for studying the Internet structure. To date, many inference algorithms, which mainly focus on peer-to-peer (P2P) and provider-to-customer (P2C) binary classification, have been proposed to classify the AS relationships and have achieved excellent results. However, business-based sibling relationships and structure-based exchange relationships have become an increasingly nonnegligible part of the Internet market in recent years. Existing algorithms are often difficult to infer due to the high similarity of these relationships to P2P or P2C relationships. In this study, we focus on multiclassification of AS relationship for the first time. We first summarize the differences between AS relationships under the structural and attribute features, and the reasons why multiclass relationships are difficult to be inferred. We then introduce new features and propose a graph convolutional network (GCN) framework, AS-GCN, to solve this multiclassification problem under complex scenes. The proposed framework considers the global network structure and local link features concurrently. Experiments on real Internet topological data validate the effectiveness of our method, that is, AS-GCN. The proposed method achieves comparable results on the binary classification task and outperforms a series of baselines on the more difficult multiclassification task, with an overall metrics above 95%.     

Topological modelling of large networks

In a complex network, there is a strong interaction between the network's topology and its functionality. A good topological network model is a practical tool as it can be used to test 'what-if' scenarios and it can provide predictions of the network's evolution. Modelling the topology structure of a large network is a challenging task, since there is no agreement in the research community on which properties of the network a model should be based, or how to test its accuracy. Here we present recent results on how to model a large network, the autonomous system (AS)-Internet, using a growth model. Based on a nonlinear preferential growth model and the reproduction of the network's rich club, the model reproduces many of the topological characteristics of the AS-Internet. We also identify a recent method to visualize the network's topology. This visualization technique is simple and fast and can be used to understand the properties of a large complex network or as a first step to validate a network model.     

A BE-based shape optimization method enhanced by topological derivative for sound scattering problems

This paper presents a BE-based shape optimization method for acoustic problems in the context of shape design of sound insulating walls. In order to avoid the convergence to a local minimal solution around an initial shape, the topological derivative is introduced. Based on the global distribution of the topological derivative, new shape or topology are attained by nucleating small scattering bodies. Evaluation of the topological derivative is achieved within the framework of the boundary element analysis. To cope with the topological change due to the nucleation and cutting of thin branches, shape is updated by the aid of the level set approach during the optimization process. Through numerical examples, it is proved that the developed method can find a shape with good performance irrespective of its initial shape.     

Graph theory and stability analysis of protein complex interaction networks

Protein complexes play an essential role in many biological processes. Complexes can interact with other complexes to form protein complex interaction network (PCIN) that involves in important cellular processes. There are relatively few studies on examining the interaction topology among protein complexes; and little is known about the stability of PCIN under perturbations. We employed graph theoretical approach to reveal hidden properties and features of four species PCINs. Two main issues are addressed, (i) the global and local network topological properties, and (ii) the stability of the networks under 12 types of perturbations. According to the topological parameter classification, we identified some critical protein complexes and validated that the topological analysis approach could provide meaningful biological interpretations of the protein complex systems. Through the Kolmogorov–Smimov test, we showed that local topological parameters are good indicators to characterise the structure of PCINs. We further demonstrated the effectiveness of the current approach by performing the scalability and data normalization tests. To measure the robustness of PCINs, we proposed to consider eight topological‐based perturbations, which are specifically applicable in scenarios of targeted, sustained attacks. We found that the degree‐based, betweenness‐based and brokering‐coefficient‐based perturbations have the largest effect on network stability.Inspec keywords: graph theory, perturbation theory, proteins, molecular configurations, molecular biophysics, pattern clustering, pattern classification, cellular biophysics, biology computingOther keywords: stability analysis, protein complex interaction networks, biological processes, protein complex interaction network, cellular processes, interaction topology, graph theoretical approach, local network topological properties, topological parameter classification, Kolmogorov‐Smirnov test, network structures, data normalisation tests, topological‐based perturbations, highly clustered protein complexes, brokering‐coefficient‐based perturbations, betweenness‐based perturbations     

Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability

In the past decade, blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention. However, current blockchain systems face the problems of limited throughput, poor scalability, and high latency. Due to the failure of consensus algorithms in managing nodes’identities, blockchain technology is considered inappropriate for many applications, e.g., in IoT environments, because of poor scalability. This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking (ADR) protocol to improve blockchain scalability and throughput. The ADR protocol uses the directed acyclic graph ledger, where nodes are placed according to their ranking positions in the graph. It allows honest nodes to use the Direct Acyclic Graph (DAG) topology to write blocks and verify transactions instead of a chain of blocks. By using a three-step strategy, this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability. The first step involves the safe entry of nodes into the system by verifying their private and public keys. The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions. In the third step, a ranking algorithm is developed to separate the nodes created by attackers. After eliminating attacker nodes, the nodes are ranked according to their performance in the system, and true nodes are arranged in blocks in topological order. As a result, the ADR protocol is suitable for applications in the Internet of Things (IoT). We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes. Based on the simulation results, this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications (IOTA) and ByteBall.     

“竹”字的拓扑结构及其设计应用

目的用数学语言精确表达"竹"字发展过程中的形体结构,并提取其拓扑结构。方法利用拓扑学的严密性分析"竹"字的形体结构演变规律,并单纯剖分其形体结构,提取"竹"字在发展过程中保持不变的结构。结论讨论了"竹"字拓扑结构在造型设计中的应用,用拓扑学方法分析了象形字的形体演变过程,提取了其保持不变的结构,即拓扑结构,并对造型设计有一定的指导作用。     

Analyzing the Structure and Connectivity of Continent-Level Internet Topology

The research and analysis of Internet topology is hot in the field of network measurement, which have important applications in network security, traffic scheduling and many other fields. Most of the existing works are focused on the AS-level and router-level topology, but few works are about the IP-level topology. In fact, obtaining the topology of each continent and knowing how the topologies of the continents are connected to each other can help us understanding the Internet around the world more thoroughly. In this paper, we obtained data sets from RIPE, constructed and analyzed network topologies of all the continents. By analyzing the topological connections between continents, we found out that most of the junctions of inter-continent traces are located in a few countries.     

Topological modeling of 1-Pentagon carbon nanocones – topological efficiency and magic sizes

In this article topological modeling techniques have been applied to the study of one pentagon carbon nanocones (apical angle 19°) to derive important results about preferred sizes and chemical reactivity. This theoretical model looks to the nanocone just like a 3-connected graph and considers the topological efficiency (or topological roundness) of such a system as the long-range topological potential whose local minima correspond to magic sizes of the nanocone with high probability of formation. This study moreover shows that topology alone can determine a migration of the stable regions of the nanocone along the nanocone itself, leaving in such a way the apical pentagon in a topologically reactive status. This study expands and systematizes previous works on the same subject.     

Optimization of truss topology using tabu search

A design procedure for integrating topological considerations in the framework of structural optimization is presented. The proposed approach is capable of considering multiple load conditions, stress, displacement and local/global buckling constraints, and multiple objective functions in the problem formulation. Further, since the proposed method permits members to be added to or deleted from an existing topology and the topology is not defined by member areas, the difficulty of not being able to reach singular optima is also avoided. These objectives are accomplished using a discrete optimization procedure which uses 0–1 topological variables to optimize alternate designs. Since the topological variables are discrete in nature and the member cross-sections are assumed to be continuous, the topological optimization problem has mixed discrete-continuous variables. This non-linear programming problem is solved using a memory-based combinatorial optimization technique known as tabu search. Numerical results obtained using tabu search for single and multiobjective topological optimization of truss structures are presented. To model the multiple objective functions in the problem formulation, a cooperative game theoretic approach is used. The results indicate that the optimum topologies obtained using tabu search compare favourably, and in some instances, outperform the results obtained using the ground–structure approach. However, this improvement occurs at the expense of a significant increase in computational burden owing to the fact that the proposed approach necessitates that the geometry of each trial topology be optimized.     

Computer recognition of Jia Gu Wen characters

Jia Gu Wen characters, which have the features of drawings, were the most ancient graphlike Chinese characters used about 4,000 years ago. Thus, the currently available methods for Chinese character recognition are not applicable. This article proposes a two-level classification recognition method. The characters to be recognized are first abstracted to be a kind of graph, upon which the first-level classification is exercised based on their topological properties. Then the definition for generalized strokes is given. The second-level classification is exercised based on the features of these strokes. Experimental results demonstrate a recognition rate of about 94%. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol 10, 363–367, 1999     

顶点划分映射算法判定运动链同构

研究机构创新设计中运动链同构判定问题. 依据图论和机构拓扑学原理,提出子块、平方和度、子块关联度等概念;利用拓扑图顶点间连接关系,构造顶点分类集合;提出一种将所有顶点一一划分并两两映射的算法,实现同构识别. 实验结果证明该算法比现有算法效率高. 将算法设计基础理论应用于机构拓扑学,为该领域研究提供新思路     

Long-range topological correlations of real polycrystalline grains in two dimensions

The topological and geometric properties of real polycrystalline grains in two dimensions are investigated on the basis of a large dataset (14,810 grains). The distribution of grain edges, the grain topology–size relationship and the short- and long-range topological correlations between neighboring grains are characterized quantitatively. The results show a strong short-range topological correlation between a center grain and its first nearest neighbors, and a trivial long-range topological correlation beyond the first nearest neighbors (on average). Both the short- and long-range relations are well described by a generalized Aboav–Weaire law reported recently. Meanwhile, it is the perimeter law, rather than the Lewis law, that describes appropriately the relationship between the topology and geometry of metallurgical grains.     

Influence of randomness in topology,geometry and material properties on the mechanical response of elastic central-force networks

We show that topological features have strong influence on the mechanical response of random elastic networks. In the simple case of a planar central force network under uni-axial compression, we vary first randomly topological characteristics under the condition that the numbers of nodes and edges in the network are fixed. Subsequently, we vary simultaneously topology and geometry and topology and local stiffness, respectively. It turns out that the characteristics related to topology determine the major trend in the mechanical behavior while randomness in geometry and local stiffness plays a minor role.     

Adaptive moving mesh level set method for structure topology optimization

The level set method is a promising approach to provide flexibility in dealing with topological changes during structural optimization. Normally, the level set surface, which depicts a structure's topology by a level contour set of a continuous scalar function embedded in space, is interpolated on a fixed mesh. The accuracy of the boundary positions is therefore largely dependent on the mesh density, a characteristic of any Eulerian expression when using a fixed mesh. This article combines the adaptive moving mesh method with a level set structure topology optimization method. The finite element mesh automatically maintains a high nodal density around the structural boundaries of the material domain, whereas the mesh topology remains unchanged. Numerical experiments demonstrate the effect of the combination of a Lagrangian expression for a moving mesh and a Eulerian expression for capturing the moving boundaries.     

无线传感器网络几类拓扑控制及其抗毁性应用简述

无线传感器网络的拓扑结构随着网络中节点的增加、减少和移动实时变化,为保证网络的连通性和覆盖性不被影响,拓扑控制技术所要解决的问题正是传感器节点如何更好地自组织构建全局网络拓扑．本文首先概述了四类拓扑控制算法的理论基础及算法步骤．然后,对提高网络抗毁性的两类拓扑演化算法进行了详细叙述,即无标度网络生长与构建$k$连通网络,分别构建了基于节点位置偏好的移动网络拓扑模型和基于$k$连通的节点调度优化模型．最后,分别从移动节点的引入、折中控制算法的探索、复杂网络理论的应用和传统算法与智能算法的结合这四方面对拓扑控制算法的前景进行了阐述．     

Robust shape and topology optimization considering geometric uncertainties with stochastic level set perturbation

When geometric uncertainties arising from manufacturing errors are comparable with the characteristic length or the product responses are sensitive to such uncertainties, the products of deterministic design cannot perform robustly. This paper presents a new level set‐based framework for robust shape and topology optimization against geometric uncertainties. We first propose a stochastic level set perturbation model of uncertain topology/shape to characterize manufacturing errors in conjunction with Karhunen–Loève (K–L) expansion. We then utilize polynomial chaos expansion to implement the stochastic response analysis. In this context, the mathematical formulation of the considered robust shape and topology optimization problem is developed, and the adjoint‐variable shape sensitivity scheme is derived. An advantage of this method is that relatively large shape variations and even topological changes can be accounted for with desired accuracy and efficiency. Numerical examples are given to demonstrate the validity of the present formulation and numerical techniques. In particular, this method is justified by the observations in minimum compliance problems, where slender bars vanish when the manufacturing errors become comparable with the characteristic length of the structures. Copyright © 2016 John Wiley & Sons, Ltd.     

Robust compliance topology optimization based on the topological derivative concept

In this paper, we present an approach for robust compliance topology optimization under volume constraint. The compliance is evaluated considering a point‐wise worst‐case scenario. Analogously to sequential optimization and reliability assessment, the resulting robust optimization problem can be decoupled into a deterministic topology optimization step and a reliability analysis step. This procedure allows us to use topology optimization algorithms already developed with only small modifications. Here, the deterministic topology optimization problem is addressed with an efficient algorithm based on the topological derivative concept and a level‐set domain representation method. The reliability analysis step is handled as in the performance measure approach. Several numerical examples are presented showing the effectiveness of the proposed approach. Copyright © 2015 John Wiley & Sons, Ltd.     

A level set–based method for stress-constrained multimaterial topology optimization of minimizing a global measure of stress

In multimaterial topology optimization of minimizing a global measure of stress, the maximum stresses in different materials may not satisfy the strength design requirements simultaneously if stress constraints for different materials are not considered. In this paper, a level set–based method is presented to handle the stress-constrained multimaterial topology optimization of minimizing a global stress measure. Specifically, a multimaterial level set model is adopted to describe the structural topology, and a stress interpolation scheme is introduced for stress evaluation. Then, a stress penalty-based topology optimization model is presented. Meanwhile, an adaptive adjusting scheme of the stress penalty factor is employed to improve the control of the local stress level. To solve the stress-constrained multimaterial topology optimization problem minimizing the global measure of stress, the parametric level set method is employed, and the sensitivity analysis is carried out. Numerical examples are provided to demonstrate the effectiveness of the presented method. Results indicate that multimaterial structures with optimized global stress can be gained, and stress constraints for different materials can be satisfied simultaneously.     

中国包装产业大数据知识图谱应用系统的设计

目的针对目前包装产业存在的产业链长、数据大而散、包装领域知识不全面等问题,设计一个中国包装产业大数据知识图谱应用系统。方法从行业高度定义涵盖包装领域全生态的分类体系,结合人工智能中知识图谱的最新技术,对政府信息、工商信息、行业信息、学术论文、全球包装专利等互联网上各类数据进行自动采集汇聚,抽取出知识信息,融合成一个涵盖资讯、政策、会议、标准、论文、专利、企业、产品、高校、机构和专家等十几类信息的包装知识图谱知识库。结果系统主要功能包括数据采集、知识图谱和终端应用,实现了包装产业大数据的图谱探索、产业链图、数据报告和关联搜索。结论该系统使用方便,可从多视图、多维度获取包装产业相关数据,提升行业的数字化和信息化水平,加速中国包装行业的智能化产业升级,促进包装产业逐步向智能、绿色、集约、创新方向发展。     

Topological characterization of the Ge-Sb-Te phase diagram

Topological images of the Ge-Sb-Te phase diagram are constructed on the basis of two versions of the phase relations between the low-temperature forms of Ge_1-δTe in the Ge-Te system. The images have the form of flow diagrams and graphs, with labeled nodes representing the phases existing in the system. The graph edges represent two-phase mixtures and are labeled by the numbers of the phase reactions in which these mixtures appear and disappear. The graphs help to visualize the topology of the phase diagram in a compact form and are convenient for topological characterization and identification of phase reactions at invariant points.