Graph structure analysis based on complex network

In this paper, we propose a novel method to characterize graph structures based on complex network model. First, we show that a structural graph can be modeled as a small-world complex network, and, then, Complex Network Characteristics (including topological and dynamic characteristics) Representation of a Graph (CNCRG) is obtained. Based on these characteristics, graph classification/clustering for objects viewed from different directions and characteristic views identification for single objects are investigated on one synthetic image dataset and two real image datasets. Our experimental results showed that CNCRG achieves better object classification/clustering performance and also provides well-structured view spaces based on multi-dimensional scaling (MDS) and principal component analysis (PCA) embedding methods for graphs extracted from 2D views of 3D objects.     

代谢网络结构与功能的复杂网络分析

细胞的新陈代谢过程可以抽象地描述成交互的复杂网络,越来越多的证据表明这种描述和分析有助于理解代谢网络的结构和功能.本文首先对高质量金黄色葡萄球菌(Staphylococcus aureus)代谢网络数据进行整理,构建了该网络中的所有代谢反应列表.随后用代谢物图(即节点表示代谢物,连线表示代谢反应)来表示这些反应,形成的...     

KDE based outlier detection on distributed data streams in multimedia network

Multimedia Tools and Applications - Multimedia networks hold the promise of facilitating large-scale, real-time data processing in complex environments. Their foreseeable applications will help...     

基于K-means聚类和数据场理论的复杂网络社团结构探寻

探寻社团结构是研究复杂网络结构与功能之间关系的基础.提出和分析了基于K-means聚类的社团探寻算法和基于数据场理论的社团探寻算法,并通过实验仿真验证了这两种算法的有效性.在仿真中发现并验证了社团内部比整个网络具有更加鲜明的小世界效应,这说明在网络控制中,在相同的耦合强度下,对社团的同步控制比对整个鲻网络的同步控制更容易实现.     

Knowle: A semantic link network based system for organizing large scale online news events

An explosive growth in the volume, velocity, and variety of the data available on the Internet has been witnessed recently. The data originated from multiple types of sources including mobile devices, sensors, individual archives, social networks, Internet of Things, enterprises, cameras, software logs, health data has led to one of the most challenging research issues of the big data era. In this paper, Knowle—an online news management system upon semantic link network model is introduced. Knowle is a news event centrality data management system. The core elements of Knowle are news events on the Web, which are linked by their semantic relations. Knowle is a hierarchical data system, which has three different layers including the bottom layer (concepts), the middle layer (resources), and the top layer (events). The basic blocks of the Knowle system—news collection, resources representation, semantic relations mining, semantic linking news events are given. Knowle does not require data providers to follow semantic standards such as RDF or OWL, which is a semantics-rich self-organized network. It reflects various semantic relations of concepts, news, and events. Moreover, in the case study, Knowle is used for organizing and mining health news, which shows the potential on forming the basis of designing and developing big data analytics based innovation framework in the health domain.     

Fine semantic mapping based on dense segmentation network

Intelligent Service Robotics - This paper proposes a fine semantic mapping method using dense segmentation network (DS-Net) to obtain good performance of semantic mapping fusion. First, the RGB...     

Hy-SN: Hyper-graph based semantic network

Knowledge representation is a critical basis for knowledge management. With considering a balance between machine- and human-understandable, we propose a new knowledge representation model by combining the semantic network and the hyper-graph. We refer to this model as ‘hyper-graph based semantic network’ (Hy-SN), which can represent more complex semantic relationships and have a more efficient data structure for storing knowledge in repositories. This paper also investigates the reasoning mechanisms of Hy-SN, which includes: the logical, analogical, and inductive reasoning. Implicit semantic relationships can be derived on the basis of these reasoning mechanisms. Three application cases based on Hy-SN are illustrated to validate its rationality and practicability.     

Multiple description coding network based on semantic segmentation

Multimedia Tools and Applications - Considering semantic information in the image compression can prominently improve the quality of synthesized image. In this paper, we propose a multiple...     

基于局部信息的复杂网络社团结构发现算法

发现网络中的社团结构有助于更好地理解网络结构和分析网络属性。通过定义边的聚类系数和基于局部信息的方法,提出了一种寻找复杂网络中社团结构的算法。该算法首先在网络的剩余节点中寻找度最大的节点,然后利用该节点的局部信息、边的聚类系数和凝聚的思想,得到复杂网络的社团结构。在两个典型网络上的测试结果表明了该方法的可行性。     

Automatically constructing course dependence graph based on association semantic link model

Course dependence graph of subject can provide an important reference model for the automatic arrangement for subject teaching plan, effective online subject learning and subject resource recommendation. Nevertheless, the challenges of the course dependence graph on the automatic construction and the maintenance of its objectivity seriously restrict its popularity. Hence, this paper proposes an approach utilizing association semantic link model for automatically constructing course dependence graph. The proposed approach employs construction of the semantic link of fragment course information resources and the association mining method to build course dependence graph. The main task of the approach can be roughly divided into the extraction of semantic key terms, the knowledge representation of course semantic and subject semantic and constructing course dependence graph. The advantages of the proposed approach are that it promotes the automation of constructing course dependence graph, defending its objectivity and getting the service of the course dependence graph smarter. The experiments show that the proposed approach has rationality and validity.     

Evocation: analyzing and propagating a semantic link based on free word association

Studies of lexical–semantic relations aim to understand the mechanism of semantic memory and the organization of the mental lexicon. However, standard paradigmatic relations such as “hypernym” and “hyponym” cannot capture connections among concepts from different parts of speech. WordNet, which organizes synsets (i.e., synonym sets) using these lexical–semantic relations, is rather sparse in its connectivity. According to WordNet statistics, the average number of outgoing/incoming arcs for the hypernym/hyponym relation per synset is 1.33. Evocation, defined as how much a concept (expressed by one or more words) brings to mind another, is proposed as a new directed and weighted measure for the semantic relatedness among concepts. Commonly applied semantic relations and relatedness measures do not seem to be fully compatible with data that reflect evocations among concepts. They are compatible but evocation captures MORE. This work aims to provide a reliable and extendable dataset of concepts evoked by, and evoking, other concepts to enrich WordNet, the existing semantic network. We propose the use of disambiguated free word association data (first responses to verbal stimuli) to infer and collect evocation ratings. WordNet aims to represent the organization of mental lexicon, and free word association which has been used by psycholinguists to explore semantic organization can contribute to the understanding. This work was carried out in two phases. In the first phase, it was confirmed that existing free word association norms can be converted into evocation data computationally. In the second phase, a two-stage association-annotation procedure of collecting evocation data from human judgment was compared to the state-of-the-art method, showing that introducing free association can greatly improve the quality of the evocation data generated. Evocation can be incorporated into WordNet as directed links with scales, and benefits various natural language processing applications.     

基于语义网技术的主题词自动标引

为了实现电子政务公文主题词语义意义上的自动标引，创建了电子政务领域的概念空间，包括使用主题词表本体形式描述基本的概念及其关系和使用语义网资源描述框架表示概念类别词及其关系，然后根据主题词表抽取出题名所包含的主题词，并消除歧义词，最后将所得到的主题词使用逻辑推理机RACER进行语义逻辑的推理，从语义的角度进行主题词标引，突破机械标引的局限。     

无线传感网络中基于链路质量的地理路由

传统的贪婪地理路由算法通常选择离目的节点最近的邻居节点作为下一跳节点,这种单纯寻求最小跳数的路由算法可能会选择质量较差的链路传输数据,导致包的投递率较低,数据传输的可靠性变差。针对这一问题,在总结现有基于链路质量的地理路由度量方法的基础上,提出了一种基于EWMA链路质量评估算法的地理路由协议。协议中节点利用周期广播的探测包评估与邻居节点之间的链路质量,并交换地理位置信息。转发节点选择离目的节点更近且链路质量更好的邻居作为下一跳节点,从而有效地减少数据分组的丢失,提高网络数据传输的可靠性。     

Detecting siblings in image pairs

In everyday life, face similarity is an important kinship clue. Computer algorithms able to infer kinship from pairs of face images could be applied in forensics, image retrieval and annotation, and historical studies. So far, little work in this area has been presented, and only one study, using a small set of low quality images, tackles the problem of identifying siblings pairs. The purpose of our paper is to present a comprehensive investigation on this subject, aimed at understanding which are, on the average, the most relevant facial features, how effective can be computer algorithms for detecting siblings pairs, and if they can outperform human evaluation. To avoid problems due to low quality pictures and uncontrolled imaging conditions, as for the heterogeneous datasets collected for previous researches, we prepared a database of high quality pictures of sibling pairs, shot in controlled conditions and including frontal, profile, expressionless, and smiling faces. Then we constructed various classifiers of image pairs using different types of facial data, based on various geometric, textural, and holistic features. The classifiers were first tested separately, and then the most significant facial data, selected with a two stage feature selection algorithm were combined into a unique classifier. The discriminating ability of the automatic classifier combining features of different nature has been found to outperform that of a panel of human raters. We also show the good generalization capabilities of the algorithm by applying the classifier, in a cross-database experiment, to a low quality database of images collected from the Internet.     

利用概念格发现领域关键词间的语义关系

提出一种利用概念格发现领域关键词间的语义关系的方法,定义了关键词间两种语义关系:相似关系和蕴含关系;关键词根据其出现频率高低分为高频关键词、中频关键词,低频关键词,进而形成关键词的语义空间.为发现高层次主题间语义关系提供了条件,也为形成语义网络奠定了一定的基础.在上海某服装搜索引擎项目中取得了很好的效果,可以直接应用于搜索引擎中的相关搜索和关键词扩展.     

基于复杂网络理论的计算机网络拓扑研究

复杂网络理论是一门新兴学科。随着计算机网络的快速发展,网络结构日益复杂,使得传统的随机网络模型已很难对其拓扑特性作出客观的描述,因此,复杂网络理论为计算机网络拓扑的研究提供了一个新的视野和思路。文章对复杂网络理论作了介绍,综述了当前基于复杂网络理论计算机网络拓扑研究的状况并指出了其中存在的问题;强调将复杂网络理论应用到计算机网络拓扑研究当中的必要性。最后对复杂网络理论在未来计算机网络拓扑研究中的可能应用方向作了总结。     

基于结构熵权-多层次灰色关联的配电网规划方案综合决策

电网的复杂性和多目标性给配电网规划综合决策带来巨大困难,建立既能满足系统运行要求又能满足经济性要求的规划方案具有重大意义。从经济性、可靠性、适应性和社会影响性四个方面对配电网规划进行研究,并构建综合评价指标体系。利用结构熵权法确定权重,采用多层次灰色关联法对配电网规划方案进行综合评价。最后,通过分析某地区3种电网规划方案的评价结果,证明本文所建立的综合评价指标体系和方法在配电网规划方案优选中应用的有效性。     

基于模块度优化的蛋白质网络集团探测与分析

探测蛋白质相互作用网络中的功能模块对于理解生物系统的组织和功能具有重要的意义。目前,普遍的做法是将蛋白质相互作用网络表示成一个图,利用各种图聚类算法来挖掘功能模块。本文采用了基于模块度优化的图聚类算法来探测蛋白质相互作用网络中的集团,从具有2617个节点11855个相互作用的酵母蛋白相互作用网络中探测出68个集团。对于得到的集团,首先从拓扑结构的角度验证其的确是内部连接稠密的子图,然后分析了MIPS数据库中ComplexCat提供的已知的蛋白质复合体与这些集团的重叠情况,发现很多蛋白质复合体完全包含在某些集团中,最后使用超几何聚集分布的P值来分析一个集团对某个特定功能的富集程度,并根据最小的P值对应的功能来注释该集团的主要功能,发现集团中大部分的蛋白质具有相同的功能。研究结果表明,该方法探测的集团具有重要的生物学功能意义。     

Analysis of functional brain network based on electroencephalography and complex network

Microsystem Technologies - In this paper, electroencephalography data are used to establish a functional network connecting correlated human brain regions. Through analysis, it is found that the...