基于网络拓扑的生物网络关键节点识别研究进展

与生物实验方法相比,基于网络拓扑的生物信息学方法在关键节点识别上有独特优势.基于网络拓扑的关键节点识别主要依赖节点在生物网络中的拓扑特性,通过观察节点网络拓扑参数的大小、所处的路径或模块的结构及其动力特性,在一定程度上可以对其关键性进行推断.从节点的中心性测度、网络的拓扑参数及层次结构等几方面总结了生物网络及其节点的主要拓扑特征;比较了蛋白质网络、代谢网络及基因调控网络关键节点识别的主要方法;分析了节点拓扑参数计算、路径求解及模块的划分及识别算法;指出生物网络关键节点识别上存在识别率不高、不同研究结论的不一致甚至相互矛盾、现有算法对网络规模日益增长的不适应等问题,并提出解决问题的思路及进一步研究的方向.     

基于多尺度中心性算法的复杂网络节点影响力研究

复杂网络中重要节点的影响力度量是网络信息挖掘中的关键问题,传统的重要节点识别方法仅考虑单一因素影响,具有一定的局限性.提出了一种基于位置信息,拓扑结构和边重要性的多尺度中心性(Multi-Scale Centrality (MSC))的度量新方法.方法融合了多样性因子影响,在K-shell分解的基础上根据节点与其位于不同k核层的邻居间的关系构建外连边尺度衡量节点的位置信息,克服了同层节点重要性无法被区分的缺陷.又结合具有结构洞特性的节点相对其邻居节点的信息传播和控制优势,对节点的重要性更进一步地作区分.最后根据边的可替代性衡量边重要性,并依据边对其相连节点的重要性贡献构造多尺度MSC中心性算法模型.经与SIR疾病传播模型在真实网络模拟的结果进行对比,验证了本算法可行性和有效性.     

基于邻域信息的社区发现方法

考虑含有节点邻域信息的新模块度函数的社区发现方法和最优分组下标度参数的选择问题,通过谱松弛方法求解模块度函数的最大化问题,最终利用新算法快速求解,并通过真实网络数据验证算法能更好的发现社区.     

节点动态增长对复杂网络传输效率的影响研究

在真实的复杂网络中,网络节点会因为网络拓扑结构的变化而增减,进而导致网络节点间传输效率降低.针对这一问题,通过分析复杂网络节点的动态变化,提出网络节点增加的动态传输模型,并利用真实复杂网络的数据模拟仿真,研究网络节点变化对网络传输效率的影响.结果表明:网络的初始大小会随网络节点的动态增加而变化,其传输效率受节点动态增加的影响在最初阶段表现明显,随着节点的继续增加,网络传输效率会趋于平稳,表现出稳定的网络特性.在这个过程中,复杂网络每次新加入节点的个数和节点边维持了网络信息传输的信息量,强化了网络传的输性能,使得网络具有较好的总体控制能力和有效的节点连接方式.     

检测网络社团结构的基于最短路径的$k$-均值算法

我们考虑复杂网络社团结构的检测问题,即检测出那些具有高于平均密度的边所连接的节点的集合.本文我们利用模拟退火策略来极大化可表示为稳定效益函数的模量(modularity),并结合基于最短路径的$k$-均值迭代过程来对网络进行分区.该算法不仅能检测出社团,而且能够识别出在最短路径度量下,该社团中位于中心位置的节点.社团的最优数目可以在无需任何关于网络结构的先验信息下自动确定.对人工生成网络和真实世界中的网络的成功应用表明了算法的有效性.     

面向可靠性的网络级联失效分析

本文从可靠性角度定义了影响级联失效过程的关键指标,探讨网络不同类型节点在失效传播过程中的作用及其对可靠性的影响。通过节点聚合描述不同节点的失效传递,以及节点失效时的网络拓扑结构变化特征,从而构建网络级联失效模型,然后确定网络的关键失效路径。最后通过案例分析,发现交通网络在经过聚合变化后稳定性更强,流通性也有提高,验证了该模型的有效性。     

M-G-P型复杂网络中有寿命节点的进入退出机制

现实中复杂网络结构复杂,形式多样,处在高度动态变化的过程.为了更好地理解真实网络的演化,基于复杂网络的特性进行分析,建立了Poissotn连续时间增长节点具有寿命的M-G-P型复杂网络模型,模型中包括:新节点加入、节点老化和老节点退出等,基于齐次马尔可夫链对模型的度分布进行计算,得出M-G-P型网络的度分布符合幂律分布,模型和BA模型一样能产生指数γ=3的无标度网络,验证了导致无标度网络度分布特征起关键性作用的是链接的偏好特性.     

轴辐式枢纽网络系统的关键枢纽设施识别问题研究

作为轴辐式枢纽网络关键因素的节点,尤其是起到中转作用的枢纽节点是网络稳定运行的重要环节。当这些节点被中断时,将对整个网络产生严重的影响。最直接的表现方式即是网络运行成本的急剧上升。因此本文研究如何识别对网络成本具有决定性影响的关键节点。首先,提出枢纽功能性中断问题和模型,并通过禁忌搜索算法进行求解。最后通过中国航空实例验证模型和算法在实际应用中的有效性。结果显示模型和算法能够有效识别出中国航空网络较重要的关键城市以及相对影响较弱的城市。可以为资源有限情况下,中国航空网络中各城市防御设施的合理分级和部署,为重点保护城市的鉴别提供依据和帮助。     

基于Erdos1合著网络和论文引用网络的复杂网络节点特征研究

首先,研究了Erdos1合著网络的特征属性,一方面使用节点的度、介数、接近中心性来描述Erdos1合著网络节点重要性,另一方面使用特征向量中心性和本文提出的高阶度参数来描述Erdos1合著网络节点影响力;然后,分别用逼近理想解的排序算法（TOPSIS算法）和主成份分析（PCA）对节点重要性和影响力排序;最后,利用修改的网页排名算法（PageRank算法）讨论了网络科学原创性论文中最具影响力的论文。     

Bellman最优原理在复杂加权网络中的应用研究

针对现有算法及软件计算复杂加权网络介数的局限性,应用Bellman最优原理于复杂加权网络介数计算中,并针对复杂网络动态演化,节点众多,重点,节点间无边连接等特点作了相应修改.依算法实例计算出了复杂加权网络的最短路径长、最短路径和介数,最后经验证算法具有较快的运行速度和较准确的结果.     

Modeling centrality measures in social network analysis using bi-criteria network flow optimization problems

Centrality measures play an important role in the field of network analysis. In the particular case of social networks, the flow represents the way in which information passes through the network nodes. Freeman et al. (1991) were the first authors to relate centrality measures to network flow optimization problems in terms of betweenness, closeness, and the influence of one node over another one. Such measures are single dimensional and, in general, they amalgamate several heterogeneous dimensions into a single one, which is not suitable for dealing with most real-world problems. In this paper we extend the betweenness centrality measure (or concept) to take into account explicitly several dimensions (criteria). A new closeness centrality measure is defined to deal not only with the maximum flow between every ordered pair of nodes, but also with the cost associated with communications. We shall show how the classical measures can be enhanced when the problem is modeled as a bi-criteria network flow optimization problem.     

Analysis of complex network performance and heuristic node removal strategies

Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks’ diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers’ network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.     

Leverage centrality analysis of infrastructure networks

Leverage centrality is a novel centrality measure proposed to identify the critical nodes that are highly influential within the network. Leverage centrality considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its direct neighbors rely on that node for information. In this paper, leverage centralities of the nodes of infrastructure networks are computed and critical nodes within the network are identified.     

Discovery Through Gossip

We study randomized gossip‐based processes in dynamic networks that are motivated by information discovery in large‐scale distributed networks such as peer‐to‐peer and social networks. A well‐studied problem in peer‐to‐peer networks is resource discovery, where the goal for nodes (hosts with IP addresses) is to discover the IP addresses of all other hosts. Also, some of the recent work on self‐stabilization algorithms for P2P/overlay networks proceed via discovery of the complete network. In social networks, nodes (people) discover new nodes through exchanging contacts with their neighbors (friends). In both cases the discovery of new nodes changes the underlying network — new edges are added to the network — and the process continues in the changed network. Rigorously analyzing such dynamic (stochastic) processes in a continuously changing topology remains a challenging problem with obvious applications. This paper studies and analyzes two natural gossip‐based discovery processes. In the push discovery or triangulation process, each node repeatedly chooses two random neighbors and connects them (i.e., “pushes” their mutual information to each other). In the pull discovery process or the two‐hop walk, each node repeatedly requests or “pulls” a random contact from a random neighbor and connects itself to this two‐hop neighbor. Both processes are lightweight in the sense that the amortized work done per node is constant per round, local, and naturally robust due to the inherent randomized nature of gossip. Our main result is an almost‐tight analysis of the time taken for these two randomized processes to converge. We show that in any undirected n‐node graph both processes take rounds to connect every node to all other nodes with high probability, whereas is a lower bound. We also study the two‐hop walk in directed graphs, and show that it takes time with high probability, and that the worst‐case bound is tight for arbitrary directed graphs, whereas Ω(n²) is a lower bound for strongly connected directed graphs. A key technical challenge that we overcome in our work is the analysis of a randomized process that itself results in a constantly changing network leading to complicated dependencies in every round. We discuss implications of our results and their analysis to discovery problems in P2P networks as well as to evolution in social networks. © 2016 Wiley Periodicals, Inc. Random Struct. Alg., 48, 565–587, 2016     

单圈图生成的凯莱图UG_n在PMC模型和MM~*模型下的1好邻诊断度

多处理系统的诊断度是一个重要的研究课题.一种新的系统故障诊断方法称为g好邻诊断度,它是限制每个无故障点至少包含g个无故障的邻点.单圈图生成的凯莱图UG_n作为一种极好的互联网络拓扑结构有许多好的性质.现证明了当n≥4时,单圈图生成的凯莱图UG_n在PMC模型下的1好邻诊断度是2n-1;当n≥5时,UG_n在MM~*模型下的1好邻诊断度是2n-1.     

构建最小k 重控制集的概率算法

点集D ⊆ V (G) 称为图G 的k 重控制集, 如果D 满足V (G) - D 中任意结点在D 中至少有k 个邻居. 在无线网络中, 最小k 重控制集(MkDS) 用以构建健壮的虚拟骨干网. 构建虚拟骨干网是无线网络中最基本也是最重要的问题. 在本文中, 我们提出一种快速的分布式概率算法来构建k重控制集. 我们构建的k 重控制集的期望大小不超过最优解的O(k²) 倍. 算法的运行时间复杂度为O((Δ logΔ+log log n)n),其中Δ = max{|D(p)|}, D(p) 是以p 为中心半径为1 的圆盘中的结点, 最大值的比较范围是给定集合中所有的p 点.     

A Linear Programming-based Evolutionary Algorithm for the Minimum Power Broadcast Problem in Wireless Sensor Networks

A matheuristic approach, where concepts from linear programming are integrated into an evolutionary algorithm, is proposed. It is tested on a problem arising in wireless sensor networks: a topology with minimum total power expenditure, that connects a source node to all the other nodes of the network, has to be identified. Experimental results are presented.     

广播网络的最佳源点集

广播是研究通信网络的某个成员的消息如何尽快地传递给所有其它成员的消息传递问题,有两类常见的通信模式,一类是shouting模式,即在一个单位时间内,一个顶点能够和它的户斤有邻点通信;另一类是whispering模式,即在一个单位时间以内,一个顶点最多只能和它的一个邻点通信,通信网络通常用图来描述,最初贮存消息的网络成员称为源点。     

Topology control in the mobile ad hoc networks in order to intensify energy conservation

Although studied for years, due to their dynamic nature, research in the field of mobile ad hoc networks (MANETs) has remained a vast area of interest. Since once distributed, there will be less to no plausibility of recharge, energy conservation has become one of the pressing concerns regarding this particular type of network. In fact, one of the main obligations of designers is to make efficient use of these scarce resources. There has been tremendous work done in different layers of protocol stack in order to intensify energy conservation. To date, numerous topology control algorithms have been proposed, however, only a few have used meta-heuristics such as genetic algorithms, neural networks and/or learning automata to overcome this issue. On the other hand, since nodes are mobile and thus in a different spatial position, as time varies, we can expect that by regulating time intervals between topology controls, one may prolong the network’s lifetime. The main initiative of this paper is to intensify energy conservation in a mobile ad hoc network by using weighted and learning automata based algorithms. The learning automata, regulates time intervals between which the topology controls are done. The represented learning automata based algorithm uses its learning ability to find appropriate time-intervals so that the nodes would regulate the energy needed in order to exchange the information to their neighbors, accordingly. Moreover, at first we have represented two weighted based algorithms which extend two prominent protocols, namely K-Neigh and LMST. Then these algorithms are combined with a learning based algorithm which regulates time intervals between which the topology controls are done. In comparison with approaches that are based on periodic topology controls, proposed approach shows enhanced results. On the other hand, considering the learning ability of the learning automata based algorithms, composition of the aforementioned algorithms has been proven to be enhanced, in the respect of energy consumed per data transmitted, over those compared with.