一种蛋白质复合体模块度函数及其识别算法

蛋白质复合体对于研究细胞活动具有重要意义.随着新的生物实验技术的不断出现,产生了大量的蛋白质相互作用网络.通过对蛋白质相互作用网络进行聚类识别蛋白质复合体是当前研究热点.然而,目前大多数蛋白质复合体识别算法的性能不够理想.为此,提出了蛋白质复合体模块度函数(PQ),并在此基础上提出了基于蛋白质复合体模块度函数的模块合并(based on protein complexes modularity function for merging modules,BMM)算法.BMM算法首先识别网络中一些稠密子图作为初始模块,然后依据PQ函数对这些初始模块进行合并,最终得到了质量较高的蛋白质复合体.将识别出的复合体分别与2种已知的蛋白质复合体数据集进行比对,结果表明BMM算法具有很好的识别性能.此外,与其他最新的识别算法相比,BMM算法的识别准确率较高.     

融合主题相似度权重的主题社区发现模型

社交网络结构错综复杂,主题社区是进行个性化推荐和商业推广的重要途径之一。然而,现有主题社区挖掘方法,要么仅基于链接关系和文本信息挖掘主题社区,要么在已划分社区的基础上挖掘主题,忽略了主题与社区的相互作用,导致社区内部话题相似度不高。因此,提出新的社区主题计算方法,进而建立一种融合主题相似度权重的主题社区发现模型（TSWTCD）。利用文本信息提取主题,计算节点间主题相似度作为链接权重,将链接权重作为模块度参数划分社区。最后,根据提出新的社区主题计算方法得到社区主题。基于真实数据集的实验结果表明,TSWTCD模型提升了挖掘主题社区的质量。     

Protein interaction networks: centrality,modularity, dynamics,and applications

In the post-genomic era, proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies. Especially the rapid accumulation of protein-protein interactions (PPIs) provides a foundation for constructing protein interaction networks (PINs), which can furnish a new perspective for understanding cellular organizations, processes, and functions at network level. In this paper, we present a comprehensive survey on three main characteristics of PINs: centrality, modularity, and dynamics. 1) Different centrality measures, which are used to calculate the importance of proteins, are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information; 2) Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced; 3) The dynamics of proteins, PPIs and sub-networks are discussed, respectively. Finally, the main applications of PINs in the complex diseases are reviewed, and the challenges and future research directions are also discussed.     

基于加权网络和局部适应度的蛋白质复合物识别算法

蛋白质复合物识别对分析蛋白质网络的结构特征和模块功能具有重要意义。通常在蛋白质网络中挖掘稠密子图或模块来识别其中的蛋白质复合物,限制了其应用范围和识别的准确性。针对该问题,提出了一种基于加权网络和局部适应度的蛋白质复合物识别算法,该算法综合稠密子图的密度指标和模块性定义了新的局部适应度函数,并基于边聚集系数构建加权的蛋白质网络,根据权值选择边,在加权蛋白质网络中将种子边不断聚类扩展,从而获取具有最大综合适应度的子图作为蛋白质复合物。在酵母蛋白质等多个实际网络中试验表明,该算法能够有效提升蛋白质复合物识别的准确性。     

动态社会网络的社团结构检测与分析

真实社会网络如邮件、科学合作、对等网络等均可以用图进行建模. 近年来, 基于图的社团挖掘吸引了人们越来越多的研究兴趣, 它不仅可以帮助识别网络的整体结构, 还可以发现社团演变的隐藏规律. 尽管使用静态图进行社团挖掘已经被广泛采用, 但基于动态图的研究还比较少. 通过使用时间序列, 对动态图上的社团挖掘包括社团检测与分析进行研究, 提出了一个新的动态社团结构检测模型, 并采用真实网络数据集进行了实验. 实验结果显示该模型在社团结构发现的有效性和效率性方面均有着良好的表现.     

Task-dependent evolution of modularity in neural networks

There exist many ideas and assumptions about the development and meaning of modularity in biological and technical neural systems. We empirically study the evolution of connectionist models in the context of modular problems. For this purpose, we define quantitative measures for the degree of modularity and monitor them during evolutionary processes under different constraints. It turns out that the modularity of the problem is reflected by the architecture of adapted systems, although learning can counterbalance some imperfection of the architecture. The demand for fast learning systems increases the selective pressure towards modularity.     

Connectionist simulation of quantification skills

The study of numerical abilities, and how they are acquired, is being used to explore the continuity between ontogenesis and environmental learning. One technique that proves useful in this exploration is the artificial simulation of numerical abilities with neural networks, using different learning paradigms to explore development. A neural network simulation of subitization, sometimes referred to as visual enumeration, and of counting, a recurrent operation, has been developed using the so-called multi-net architecture. Our numerical ability simulations use two or more neural networks combining supervised and unsupervised learning techniques to model subitization and counting. Subitization has been simulated using networks employing unsupervised self-organizing learning, the results of which agree with infant subitization experiments and are comparable with supervised neural network simulations of subitization reported in the literature. Counting has been simulated using a multi-net system of supervised static and recurrent backpropagation networks that learn their individual tasks within an unsupervised, competitive framework. The developmental profile of the counting simulation shows similarities to that of children learning to count and demonstrates how neural networks can learn how to be combined together in a process modelling development.     

基于多标度曲线的股市网络构造及其社区挖掘

针对股票市场的复杂网络建模问题,提出使用不同阶数下的标度曲线(多标度曲线),测度沪深300指标股之间的加权多重分形特征相似性,并据此构造网络,研究网络的拓扑性质。在此基础上采用快速 Newman, Girvan-Newman,Louvain等经典算法挖掘网络社区结构,利用最大模块度确定最优相似性门限值,通过投资组合MV模型验证方法的有效性。实验结果表明,多标度曲线网络具有无标度、小世界和富人俱乐部性质,使用不同算法挖掘其社区结构可得到最优的划分效果。基于该网络社区结构构造的投资组合可有效降低风险。     

基于模块度的社交网络分形维度计算方法

社交网络是由个体或组织以及它们之间的关系所组成的社会结构。利用社交网络的分形结构来解释和预测社交网络的行为是目前的一个研究热点。分形维度是对社交网络中分形结构的度量,为了更准确地对社交网络分形结构进行度量,提出了一种基于模块度的盒子覆盖算法来计算分形维度。该算法利用分形维度和模块度互斥的性质,基于模块度最小的原则来构建盒子,再对盒子进行计数来计算社交网络的分形维度。仿真实验表明：基于模块度的盒子覆盖法比传统的盒覆盖算法得到更为精确的分形维度。     

一个基于多维特征向量的复杂网络社团结构发现算法

作为复杂网络重要特性之一的社团结构在大量现实的大规模复杂系统研究中占有重要的一席地位.论文在研究现有的社团发现算法基础上,提出了一种基于多维特征向量的复杂网络社团结构发现算法,实验证明,该算法能够有效的发现复杂网络中的社团结构,对于进一步进行复杂网络上的信息挖掘具有重要的意义.