Heuristical top-k: fast estimation of centralities in complex networks

Centrality metrics have proven to be of a major interest when analyzing the structure of networks. Given modern-day network sizes, fast algorithms for estimating these metrics are needed. This paper proposes a computation framework (named Filter-Compute-Extract) that returns an estimate of the top-k most important nodes in a given network. We show that considerable savings in computation time can be achieved by first filtering the input network based on correlations between cheap and more costly centrality metrics. Running the costly metric on the smaller resulting filtered network yields significant gains in computation time. We examine the complexity improvement due to this heuristic for classic centrality measures, as well as experimental results on well-studied public networks.     

OTIS网络的支配集问题算法研究

图的最小支配集问题和最小连通支配集问题在网络与并行分布式计算中有重要应用,计算上它们都属于NP难问题。OTIS网络是一类可以任意图为因子网络的复合网络,它能继承因子网络的良好特性,因而成为可扩展性、模块化、容错性的大规模并行计算机系统的体系结构形式之一。研究如何构建OTIS网络的较小支配集和连通支配集。基于OTIS网络构图规则,分别根据因子网络的支配集算法和连通支配集算法得到了求解OTIS网络的支配集算法和连通支配集算法。从理论上分析了这些算法的性能,并通过实例进行了验证。     

Cellular automaton-based algorithms for the dispersion of mobile wireless sensor networks

Due to the advent of sensor technology and its applications, mobile wireless sensor networks (MWSNs) have gained a significant amount of research interest. In a typical MWSN, sensors can move within the network. We develop a set of probabilistic and deterministic cellular automaton (CA)-based algorithms for motion planning problems in MWSNs. First, we consider a scenario where a group of sensors are deployed and they need to disperse in order to maximise the area covered by the network. In this variant of the problem we do not explicitly consider that the sensors should maintain the connectivity of the network while they move. Second, we consider a scenario where the sensors are initially randomly distributed and they need to disperse autonomously to both maximise the coverage of the network and maintain its connectivity. We carry out extensive simulations of both deterministic and randomised variants of the algorithms. For the first variant of the problem we compare our algorithms with one previous algorithm and find that our algorithm yields better network coverage than the earlier algorithm. We also find that probabilistic algorithms have better overall performance for the second variant. CA algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.     

Real-time data dissemination in mobile peer-to-peer networks

Mobile peer-to-peer networks have found many uses such as streaming of audio and video data. There are circumstances, such as emergency situations and disaster recovery, when real-time delivery is a fundamental requirement. The problem is challenging due to the limited network capacity, the variable transmission rates and the unpredictability with respect to the network conditions in the mobile peer-to-peer network.In this paper we address the problem of real-time data dissemination of multimedia streams in mobile peer-to-peer networks. Four routing algorithms are proposed based on a packet's deadline, priority or a combination of these metrics. They are simulated under different setups in a mobile peer-to-peer network with Bluetooth connectivity and nodes broadcasting audio and video streams using different priorities. We compare the performance of the algorithms using a number of metrics. Detailed experimental results are presented. Based on these results, propositions on the usage of the algorithms and the design of network requirements are presented.     

A testbed for evaluating network construction algorithms from GPS traces

Developing algorithms which construct street/pedestrian networks from crowd-sourced GPS traces has been an ongoing research since the outbreak of inexpensive GPS receivers on mobile devices. Although, the proposed algorithms are evaluated by their developers, the evaluation results cannot be used to compare their accuracy because: (a) different algorithms target different types of networks, some designed for complicated networks while others for simple ones, (b) GPS traces, used in different studies, are not the same, some of them are more accurate and denser than others, and (c) the constructed networks are evaluated either qualitatively or with different quantitative metrics. Lack of a comprehensive testbed for evaluating network construction algorithms has made it difficult for authors, reviewers, and readers to monitor the effectiveness of such algorithms. This study establishes a testbed for evaluating network construction algorithms containing three components: (a) street and pedestrian networks with different densities and complexities as the baseline, (b) collections of GPS traces with different accuracies and sampling rates to be used by algorithms to construct those networks, and (c) three quantitative metrics to indicate the completeness, precision, and topology correctness of the constructed network, in addition to the algorithm's time complexity, conventionally used to indicate its time performance. This testbed not only paves the way for comparing network construction algorithms but also allows researchers to focus on their algorithms rather than collecting data for testing it or looking for ways to describe its accuracy.     

基于复杂网络理论的无线传感器网络特征度量分析

通过研究复杂网络的理论,介绍了复杂网络的几种主要特征度量,并以此分析了无线传感器网络的特性。列举了无线传感器网络的节点度分布、集聚系数、平均路径长度、网络联通性等特性。研究了节点数、通讯半径、连通率间的关系,由仿真的数据得出,增加节点不能完全保证网络的联通,只能增加联通的概率,而增加通讯半径则可以迅速地使网络联通。因此,在条件允许的情况下,选择通讯半径大的传感器比增加撒布节点更能保证网络的联通。最后给出了适合于无线传感器网络的介数的评价方法。     

Coverage and connectivity issues in wireless sensor networks: A survey

Sensing coverage and network connectivity are two of the most fundamental problems in wireless sensor networks. Finding an optimal node deployment strategy that would minimize cost, reduce computation and communication overhead, be resilient to node failures, and provide a high degree of coverage with network connectivity is extremely challenging. Coverage and connectivity together can be treated as a measure of quality of service in a sensor network; it tells us how well each point in the region is covered and how accurate is the information gathered by the nodes. Therefore, maximizing coverage as well as maintaining network connectivity using the resource constrained nodes is a non-trivial problem. In this survey article, we present and compare several state-of-the-art algorithms and techniques that aim to address this coverage–connectivity issue.     

Mobility-assisted minimum connected cover in a wireless sensor network

All properties of mobile wireless sensor networks (MWSNs) are inherited from static wireless sensor networks (WSNs) and meanwhile have their own uniqueness and node mobility. Sensor nodes in these networks monitor different regions of an area of interest and collectively present a global overview of monitored activities. Since failure of a sensor node leads to loss of connectivity, it may cause a partitioning of the network. Adding mobility to WSNs can significantly increase the capability of the WSN by making it resilient to failures, reactive to events, and able to support disparate missions with a common set of sensor nodes. In this paper, we propose a new algorithm based on the divide-and-conquer approach, in which the whole region is divided into sub-regions and in each sub-region the minimum connected sensor cover set is selected through energy-aware selection method. Also, we propose a new technique for mobility assisted minimum connected sensor cover considering the network energy. We provide performance metrics to analyze the performance of our approach and the simulation results clearly indicate the benefits of our new approach in terms of energy consumption, communication complexity, and number of active nodes over existing algorithms.     

A comparison of statistical relational learning and graph neural networks for aggregate graph queries

Statistical relational learning (SRL) and graph neural networks (GNNs) are two powerful approaches for learning and inference over graphs. Typically, they are evaluated in terms of simple metrics such as accuracy over individual node labels. Complex aggregate graph queries (AGQ) involving multiple nodes, edges, and labels are common in the graph mining community and are used to estimate important network properties such as social cohesion and influence. While graph mining algorithms support AGQs, they typically do not take into account uncertainty, or when they do, make simplifying assumptions and do not build full probabilistic models. In this paper, we examine the performance of SRL and GNNs on AGQs over graphs with partially observed node labels. We show that, not surprisingly, inferring the unobserved node labels as a first step and then evaluating the queries on the fully observed graph can lead to sub-optimal estimates, and that a better approach is to compute these queries as an expectation under the joint distribution. We propose a sampling framework to tractably compute the expected values of AGQs. Motivated by the analysis of subgroup cohesion in social networks, we propose a suite of AGQs that estimate the community structure in graphs. In our empirical evaluation, we show that by estimating these queries as an expectation, SRL-based approaches yield up to a 50-fold reduction in average error when compared to existing GNN-based approaches.

     

ROI-Driven Cyber Risk Mitigation Using Host Compliance and Network Configuration

Automated cyber security configuration synthesis is the holy grail of cyber risk management. The effectiveness of cyber security is highly dependent on the appropriate configuration hardening of heterogeneous, yet interdependent, network security devices, such as firewalls, intrusion detection systems, IPSec gateways, and proxies, to minimize cyber risk. However, determining cost-effective security configuration for risk mitigation is a complex decision-making process because it requires considering many different factors including end-hosts’ security weaknesses based on compliance checking, threat exposure due to network connectivity, potential impact/damage, service reachability requirements according to business polices, acceptable usability due to security hardness, and budgetary constraints. Although many automated techniques and tools have been proposed to scan end-host vulnerabilities and verify the policy compliance, existing approaches lack metrics and analytics to identify fine-grained network access control based on comprehensive risk analysis using both the hosts’ compliance reports and network connectivity. In this paper, we present new metrics and a formal framework for automatically assessing the global enterprise risk and determining the most cost-effective security configuration for risk mitigation considering both the end-host security compliance and network connectivity. Our proposed metrics measure the global enterprise risk based on the end-host vulnerabilities and configuration weaknesses, collected through compliance scanning reports, their inter-dependencies, and network reachability. We then use these metrics to automatically generate a set of host-based vulnerability fixes and network access control decisions that mitigates the global network risk to satisfy the desired Return on Investment of cyber security. We solve the problem of cyber risk mitigation based on advanced formal methods using Satisfiability Module Theories, which has shown scalability with large-size networks.     

Parallel discovery of network motifs

Many natural structures can be naturally represented by complex networks. Discovering network motifs, which are overrepresented patterns of inter-connections, is a computationally hard task related to graph isomorphism. Sequential methods are hindered by an exponential execution time growth when we increase the size of motifs and networks. In this article we study the opportunities for parallelism in existing methods and propose new parallel strategies that adapt and extend one of the most efficient serial methods known from the Fanmod tool. We propose both a master–worker strategy and one with distributed control, in which we employ a randomized receiver initiated methodology capable of providing dynamic load balancing during the whole computation process. Our strategies are capable of dealing both with exact and approximate network motif discovery. We implement and apply our algorithms to a set of representative networks and examine their scalability up to 128 processing cores. We obtain almost linear speedups, showcasing the efficiency of our proposed approach and are able to reach motif sizes that were not previously achievable using conventional serial algorithms.     

A complex network approach to text summarization

Automatic summarization of texts is now crucial for several information retrieval tasks owing to the huge amount of information available in digital media, which has increased the demand for simple, language-independent extractive summarization strategies. In this paper, we employ concepts and metrics of complex networks to select sentences for an extractive summary. The graph or network representing one piece of text consists of nodes corresponding to sentences, while edges connect sentences that share common meaningful nouns. Because various metrics could be used, we developed a set of 14 summarizers, generically referred to as CN-Summ, employing network concepts such as node degree, length of shortest paths, d-rings and k-cores. An additional summarizer was created which selects the highest ranked sentences in the 14 systems, as in a voting system. When applied to a corpus of Brazilian Portuguese texts, some CN-Summ versions performed better than summarizers that do not employ deep linguistic knowledge, with results comparable to state-of-the-art summarizers based on expensive linguistic resources. The use of complex networks to represent texts appears therefore as suitable for automatic summarization, consistent with the belief that the metrics of such networks may capture important text features.     

DACCER: Distributed Assessment of the Closeness CEntrality Ranking in complex networks

We propose a method for the Distributed Assessment of the Closeness CEntrality Ranking (DACCER) in complex networks. DACCER computes centrality based only on localized information restricted to a limited neighborhood around each node, thus not requiring full knowledge of the network topology. We indicate that the node centrality ranking computed by DACCER is highly correlated with the node ranking based on the traditional closeness centrality, which requires high computational costs and full knowledge of the network topology by the entity responsible for calculating the centrality. This outcome is quite useful given the vast potential applicability of closeness centrality, which is seldom applied to large-scale networks due to its high computational costs. Results indicate that DACCER is simple, yet efficient, in assessing node centrality while allowing a distributed implementation that contributes to its performance. This also contributes to the practical applicability of DACCER to the analysis of large complex networks, as indicated in our experimental evaluation using both synthetically generated networks and real-world network traces of different kinds and scales.     

Analyzing User-Perceived Dependability and Performance Characteristics of Voting Algorithms for Managing Replicated Data

User-perceived dependability and performance metrics are very different from conventional ones in that the dependability and performance properties must be assessed from the perspective of users accessing the system. In this paper, we develop techniques based on stochastic Petri nets (SPN) to analyze user-perceived dependability and performance properties of quorum-based algorithms for managing replicated data. A feature of the techniques developed in the paper is that no assumption is made regarding the interconnection topology, the number of replicas, or the quorum definition used by the replicated system, thus making it applicable to a wide class of quorum-based algorithms. We illustrate this technique by comparing conventional and user-perceived metrics in majority voting algorithms. Our analysis shows that when the user-perceiveness is taken into consideration, the effect of increasing the network connectivity and number of replicas on the availability and dependability properties perceived by users is very different from that under conventional metrics. Thus, unlike conventional metrics, user-perceived metrics allow a tradeoff to be exploited between the hardware invested, i.e., higher network connectivity and number of replicas, and the performance and dependability properties perceived by users.     

Physarum computing and topology optimisation

The plasmodium stage of the giant single-celled amoeboid organism Physarum polycephalum, or true slime mould, dynamically adapts its body plan in response to environmental stimuli, including nutrient location, temperature gradients, light exposure, and predatory threats. The construction and adaptation of the plasmodial transport network has been shown to be an efficient trade-off between minimising network distance and maximising connectivity. Optimisation of topology is also a desired trait in manufacturing and engineering, whereby the topology of an initially simple component is modified to minimise one or more properties (for example minimising the amount of substrate reduces the weight and material cost of the component) whilst maintaining other desired material properties within the component (for example strength under specific load points and conditions). Biologically inspired approaches to certain specific engineering challenges are well documented and successful. In this paper we explore at a more general level whether a cross-pollination can occur between Physarum computing and Topology Optimisation. Using an interdisciplinary modelling approach we explore whether the evolution of transport networks in a multi-agent model of Physarum has any similarities to networks formed by Topology Optimisation. We find that the Topology Optimisation method generates networks which are similar to the model slime mould networks. The Topology Optimisation networks correspond to higher regions of the Toussaint hierarchy of proximity graphs (i.e. more edges) whereas the model slime mould networks exhibit greater minimisation of network length. Since it is possible to adjust network connectivity in the multi-agent model via nutrient concentration we speculate that similar parametric adjustment may be possible to alter the connectivity of Topology Optimisation networks.     

无线传感器网络适应QoS机制的网络模型

基于无线传感器网络服务质量(QoS)的特点,提出了一个独立于具体算法的、在点和边上都可加权的有向图网络模型,并用数学定义描述了QoS参数间的关系及服从的函数约束,使用该网络模型分析QoS路由的数学约束集并提出QoS路由选择策略。     

A Framework for Optimizing End-to-End Connectivity Degree in Mobile Ad-Hoc Networks

Mobile ad-hoc networks are networks spontaneously deployed from a set of mobile devices without requiring any fixed infrastructures. The increasing interest in this technology raises new research challenges towards providing them a management framework. Network users expect a service level as close as in regular fixed networks. A primary expectation is the capability to communicate (end-to-end) with the other network users. We present in this paper a framework for monitoring and optimizing this capability in mobile ad-hoc networks. Our normalized metric, called end-to-end connectivity degree, characterizes the number of nodes, that a node can reach in the entire network. We describe a management architecture to monitor this metric together with the network density, and illustrate how routing configuration can be performed to optimize it.     

Fusing functional connectivity with network nodal information for sparse network pattern learning of functional brain networks

Sparse learning methods have been powerful tools for learning compact representations of functional brain networks consisting of a set of brain network nodes and a connectivity matrix measuring functional coherence between the nodes. However, these tools typically focus on the functional connectivity measures alone, ignoring the brain network nodal information that is complementary to the functional connectivity measures for comprehensively characterizing the functional brain networks. In order to provide a comprehensive delineation of the functional brain networks, we develop a new data fusion method for heterogeneous data, aiming at learning sparse network patterns to characterize both the functional connectivity measures and their complementary network nodal information within a unified framework. Experimental results have demonstrated that our method outperforms the best alternative method under comparison in terms of accuracy on simulated data as well as both reproducibility and prediction performance of brain age on real resting state functional magnetic resonance imaging data.     

Routing performance enhancement in hierarchical torus network by link-selection algorithm

A hierarchical torus network (HTN) is a 2D-torus network of multiple basic modules, in which the basic modules are 3D-torus networks that are hierarchically interconnected for higher-level networks. The static network performance of the HTN and its dynamic communication performance using the popular dimension-order routing algorithm have already been evaluated and shown to be superior to the performance of other conventional and hierarchical interconnection networks. In this paper, we propose a link-selection algorithm for efficient use of physical links of the HTN, while keeping the link-selection algorithm as simple as the dimension-order routing algorithm. We also prove that the proposed algorithm for the HTN is deadlock-free using three virtual channels. We evaluate the dynamic communication performance of an HTN using dimension-order routing and link-selection algorithms under various traffic patterns. We find that the dynamic communication performance of an HTN using the link-selection algorithm is better than when the dimension-order routing algorithm is used.     

基于在线社交网络的JPS跳跃并行顶点采样方法

针对现有在线社交网络（OSNs）采样方法无法有效地应用于低连通性的社交网络，且采集的样本顶点平均度严重偏离原始社交网络、顶点过度采样等问题，本文基于蒙特卡罗随机游走（MHRW）采样方法，引入双重跳跃策略、并行机制和顶点缓存区，提出一种跳跃无偏并行顶点（JPS）采样方法。将在线社交网络数据集建模为包含顶点和边的社交图进行模拟采样，利用Python/Matplotlib绘图库绘制采集的样本顶点属性图。实验结果表明,该采样方法更有效地应用于不同连通强度的社交图，提高了采样过程中的顶点更新率，降低了样本顶点的平均度偏差且能够更快速地收敛。