复杂网络统计力学的差分方程方法

通过分析几种估计增长网络度分布方法的缺点,提出估计度分布的差分方程方法,不仅避免了复杂网络分析中将离散问题连续化带来的逻辑矛盾,也避免了网络稳态度分布存在性的假设.利用这个方法给出Poisson增长择优连接网络的度分布公式,借助Poisson过程理论和Gamma 分布的性质严格证明Poisson增长择优连接网络是无标度网络.     

群体行为的异质节点复杂网络传播

当遭遇突发的公共社会安全事件时,具有负面影响的应激行为可能迅速在社会范围内传播扩散,形成群体行为.虽然一些复杂网络的传染模型能够对此进行刻画,但更为符合实际的是行为群体根据一些特性可能划分为不同的亚群体,为此将建立异质节点SIS复杂网络模型.此后,依据亚群体的有效传播率与度分布无关、正相关和负相关三种情形,分别研究了群体行为在异质节点的小世界网络传播特性,及异质节点的无标度网络传播特性.无论是异质节点的小世界网络模型还是异质节点的无标度网络模型,平均场动力学分析和计算机模拟结果显示,当亚群体的传播率与度分布呈正相关时,群体行为的传播会出现放大相应;反之,当亚群体的传播率与度分布成负相关时,群体行为的传播会出现抑制效应.但以上的两种效应在离散性更强的无标度网络上更为明显.     

BA无标度网络中的SIR模型

网络化是现代世界的一个重要特征,不仅包括互联网,还包括航空网,人际关系网,而它们都是很典型的无标度网络.在BA无标度网中,结合经典的SIR模型,建立了新的SIR模型,并对模型进行了研究,得到了BA无标度网络对传染病与计算机病毒传播具有脆弱性的结论.     

标度指数不大于2的无标度网络的若干性质

将标度指数不大于2的无标度网络称为亚标度网络.通过引入度秩函数研究了亚标度网络的最大度、平均度以及拓扑结构的非均匀性,通过与标度指数大于2的无标度网络对比,揭示了亚标度网络若干特殊性质.     

惯例复制与知识转移:作用机制的权变条件研究

针对惯例复制过程中与外部情景难以匹配的复制困境,分析了常规惯例复制和柔性惯例复制对知识转移的单独以及联合作用的关系悖论,对比分析作用机制在网络闭合性和环境不确定性约束下的权变性影响。以研发密集型企业为对象,运用多元回归分析进行实证检验。结果表明:网络闭合性和环境不确定性是惯例复制对知识转移的单独以及联合作用的权变条件;网络闭合性强化了常规惯例复制机制的促进作用,但限制了柔性惯例复制机制的效能发挥;环境不确定性削弱了常规惯例复制机制的促进作用,对柔性惯例复制机制的作用效果不显著;网络闭合性低或环境不确定性低时,两种惯例复制机制的联合作用相互补充,而网络闭合性高或环境不确定性高时,两种惯例复制机制相互替代。研究结论有助于揭示惯例复制的权变作用,对提升技术创新网络的知识转移效率具有重要意义。     

BA模型的数学基础——纪念李国平院士吴新谋教授诞辰100周年

该文基于马氏链的概念和技巧, 给出了BA无标度网络模型稳态度分布存在性的严格证明, 并且从数学上重新推导了度分布的精确解析表达式. 此处所用的方法具有一定的普适性, 适用于更一般的无标度型复杂网络模型.     

两个网络间的相互同步

本文研究了两个耦合网络的相互同步,利用线性化方法,我们给出了两个具有相同拓扑结构的网络实现同步的定理,最后用数值例子来验证得到的理论结果。     

基于组织进入、退出和合作的技术创新网络演化模型

技术创新网络是企业、大学、研究机构等相关组织基于共同的技术创新目标而建立起来的一种网络组织形式,像其他生命体一样存在着萌芽、成长、成熟和衰退的生命周期.网络的演化与网络成员的行为密切相关,在分析组织进入网络、增加合作和退出网络机制的基础上构建了技术创新网络演化模型,研究不同演化阶段的特征.理论推导和仿真结果表明:技术创新网络整个生命周期中保持着无标度特性;组织进入网络、增加合作和退出网络的速度决定了技术创新网络的生命周期以及其所处生命周期的阶段.     

一类生物复制网络的极限性质

本文考虑生物网络中一类复制模型度分布的极限性质.利用组合知识和概率论中鞅方法,讨论了度分布的大数定律和中心极限定理,为生物学家研究生物内部系统机制提供理论基础.     

探讨几类具有完全图核心的网络模型的优美性

在复杂网络研究中,人们需要建立网络模型,无标度图就是这样的一种网络模型.我们发现具有完全图核心的网络模型可以演变成无标度图.具有完全图核心的几种网络模型的优美性得到研究.     

Integration of topological measures for eliminating non-specific interactions in protein interaction networks

High-throughput protein interaction assays aim to provide a comprehensive list of interactions that govern the biological processes in a cell. These large-scale sets of interactions, represented as protein–protein interaction networks, are often analyzed by computational methods for detailed biological interpretation. However, as a result of the tradeoff between speed and accuracy, the interactions reported by high-throughput techniques occasionally include non-specific (i.e., false-positive) interactions. Unfortunately, many computational methods are sensitive to noise in protein interaction networks; and therefore they are not able to make biologically accurate inferences.In this article, we propose a novel technique based on integration of topological measures for removing non-specific interactions in a large-scale protein–protein interaction network. After transforming a given protein interaction network using line graph transformation, we compute clustering coefficient and betweenness centrality measures for all the edges in the network. Motivated by the modular organization of specific protein interactions in a cell, we remove edges with low clustering coefficient and high betweenness centrality values. We also utilize confidence estimates that are provided by probabilistic interaction prediction techniques. We validate our proposed method by comparing the results of a molecular complex detection algorithm (MCODE) to a ground truth set of known Saccharomyces cerevisiae complexes in the MIPS complex catalogue database. Our results show that, by removing false-positive interactions in the S. cerevisiae network, we can significantly increase the biological accuracy of the complexes reported by MCODE.     

组织特异性蛋白质复合体的识别

本文研究了组织特异性蛋白质复合体的识别问题.利用蛋白质相互作用网络数据以及组织特异性基因表达数据构建组织特异性蛋白网络,利用多种代表性聚类算法对该网络进行聚类,并利用非负矩阵分解对聚类结果进行合并聚类,得到了组织特异性蛋白质复合体.结果表明,聚类效果得到明显提升,并且能识别出组织特异性蛋白质复合体.     

Link Prediction for Partially Observed Networks

Link prediction is one of the fundamental problems in network analysis. In many applications, notably in genetics, a partially observed network may not contain any negative examples, that is, edges known for certain to be absent, which creates a difficulty for existing supervised learning approaches. We develop a new method that treats the observed network as a sample of the true network with different sampling rates for positive (true edges) and negative (absent edges) examples. We obtain a relative ranking of potential links by their probabilities, using information on network topology as well as node covariates if available. The method relies on the intuitive assumption that if two pairs of nodes are similar, the probabilities of these pairs forming an edge are also similar. Empirically, the method performs well under many settings, including when the observed network is sparse. We apply the method to a protein–protein interaction network and a school friendship network.     

Monte Carlo Algorithms for Identifying Densely Connected Subgraphs

The problem of finding densely connected subgraphs in a network has attracted a lot of recent interest. Such subgraphs are sometimes referred to as communities in social networks or molecular modules in protein networks. In this article, we propose two Monte Carlo optimization algorithms for identifying the densest subgraphs with a fixed size or with size in a given range. The new algorithms combine the idea of simulated annealing and efficient moves for the Markov chain, and both algorithms are shown to converge to the set of optimal states (densest subgraphs) with probability 1. When applied to a yeast protein interaction network and a stock market graph, the algorithms identify interesting new densely connected subgraphs. Supplementary materials for the article are available online.     

Circadian synchrony in networks of protein rhythm driven neurons

The interaction between gene activation and cellular activity has recently emerged as a critical aspect of brain behavior, but the dynamics of networks incorporating these interactions are poorly understood. An interesting phenomena arises when the genetic activation oscillates endogenously and a network of such cells synchronize to a coherent rhythm, such as is the case with the suprachiasmatic nucleus. To explain this synchronization, we propose a model in which a mRNA/protein expression cycle drives neurons electrical activity, and synaptic activation shifts the phase of the protein rhythm. Using lattice networks, we demonstrate that these interactions are sufficient to generate coherent oscillation. © 2006 Wiley Periodicals, Inc. Complexity 12: 67–72, 2006     

复杂疾病的分子网络模型研究

复杂疾病是危害人类健康的主要杀手.不同于单基因缺陷性遗传病,复杂疾病的发生发展与多个基因之间、基因与环境之间的相互作用有关,致病机理复杂,其早期诊断及治疗困难是21世纪生物医学研究的重大挑战之一.随着生物知识的不断积累和多层次"组学"数据的井喷式涌现,复杂疾病研究迎来了新的"组学革命",研究模式从以往的只关注某个分子扩展到对分子之间相互形成的生物分子网络的系统分析.作为系统生物学核心概念,生物分子网络系统整合大量生物知识和高通量生物数据,是研究复杂疾病的强有力工具.本文以分子网络为主线,以数学建模为工具来研究复杂疾病,针对复杂疾病关系和复杂疾病的发生发展机制等复杂疾病研究的关键热点问题,分析和集成高通量多层次组学数据,构建并求解生物分子网络的数学模型,在若干复杂疾病相关系统生物学问题中取得有生物学意义的结果.本文提出若干生物网络建模、分析及应用的方法并提供若干应用软件,为从系统层面理解复杂疾病提供重要参考;同时,网络模型在若干实例中的应用得到若干有生物学意义的结论,为揭示复杂疾病机理、推动疾病治疗与预防起到了一定的作用.     

Topological fixed points in Boolean networks

We introduce the notion of a topological fixed point in Boolean Networks: a fixed point of Boolean network F is said to be topologic if it is a fixed point of every Boolean network with the same interaction graph as the one of F. Then, we characterize the number of topological fixed points of a Boolean network according to the structure of its interaction graph.     

内生网络环境下2-步邻域内的局部策略互动及其仿真

考察内生网络环境下局中人与2-步邻域内的邻居进行的局部协同对策,较为完整地给出了均衡网络的结构特性,以及费用参数和互动半径对于均衡结构的影响. 基于 NetLogo仿真系统,编制了局部互动仿真模拟实验程序. 仿真结果显示,网络生成的动态进程对于网络均衡结果存在很大影响. 结果对于解决社会和经济领域中的互动问题可提供策略性指导.     

Directed chain stochastic differential equations

We propose a particle system of diffusion processes coupled through a chain-like network structure described by an infinite-dimensional, nonlinear stochastic differential equation of McKean–Vlasov type. It has both (i) a local chain interaction and (ii) a mean-field interaction. It can be approximated by a limit of finite particle systems, as the number of particles goes to infinity. Due to the local chain interaction, propagation of chaos does not necessarily hold. Furthermore, we exhibit a dichotomy of presence or absence of mean-field interaction, and we discuss the problem of detecting its presence from the observation of a single component process.     

乳腺癌易感基因蛋白质网络的构建与研究

本文从复杂网络理论出发,在分析原有乳腺癌易感基因数据的基础上,综合统计分析易感基因彼此之间的关联与乳腺癌疾病之间的关系,并以此构建乳腺癌致病基因蛋白质网络．通过计算和研究网络度,聚类系数等指标发现,此网络具有高度聚集性,即少数核心节点控制着整个网络结构的稳定性．这将为进一步研究和发现乳腺癌致病基因提供新的理论依据和方法．