Large-scale access scheduling in wireless mesh networks using social centrality

Wireless mesh networking is an economic and convenient way to provide last mile Internet access through ad hoc peer-to-peer communication links. However, without systematic network configuration and channel resource management, these networks suffer from scalability, performance degradation and service disruption issues due to overwhelming co-channel interference, unscrupulous channel utilization and inherent network mobility. The IEEE 802.11 DCF and EDCA mechanisms based on CSMA/CA are the most widely used random channel access mechanisms, but unfortunately these cannot effectively eliminate hidden terminal and exposed terminal problems in multi-hop scenarios. Social network analysis techniques proposed for economic and social studies have recently been shown to be a successful approach for characterizing information propagation in multi-hop wireless networks. We propose a set of efficient resource allocation algorithms and channel access scheduling protocols based on Latin squares and social centrality metrics for wireless mesh networks (WMNs) with multi-radio multi-channel (MRMC) communication capabilities, called LaSo$LaSo$, which can coexist with IEEE 802.11 DCF and be effectively applied in large scale WMNs. Based on interference information provided by the interference graph, LaSo$LaSo$ uses nodal degree centrality to form cliques for intra-cluster communication, and betweenness centrality to choose bridge nodes to form cliques for inter-cluster communication in WMNs, and then applies Latin squares to map the clique-based clustering structure to radios and channels for wireless communication purposes. Afterwards, LaSo$LaSo$ again applies Latin squares to schedule the channel access amongst nodes within each cluster in a collision-free manner. We evaluate LaSo$LaSo$ using simulations, and results show that LaSo$LaSo$ achieves much better performance than existing IEEE 802.11 standards and other multi-channel access control protocols.     

Visual analysis of social networks in space and time using smartphone logs

We designed and applied interactive visualisation techniques for investigating how social networks are embedded in time and space, using data collected from smartphone logs. Our interest in spatial aspects of social networks is that they may reveal associations between participants missed by simply making contact through smartphone devices. Four linked and co-ordinated views of spatial, temporal, individual and social network aspects of the data, along with demographic and attitudinal variables, helped add context to the behaviours we observed. Using these techniques, we were able to characterise spatial and temporal aspects of participants’ social networks and suggest explanations for some of them. This provides some validation of our techniques.Unexpected deficiencies in the data that became apparent prompted us to evaluate the dataset in more detail. Contrary to what we expected, we found significant gaps in participant records, particularly in terms of location, a poorly connected sample of participants and asymmetries in reciprocal call logs. Although the data captured are of high quality, deficiencies such as these remain and are likely to have a significant impact on interpretations relating to spatial aspects of the social network. We argue that appropriately-designed interactive visualisation techniques–afforded by our flexible prototyping approach–are effective in identifying and characterising data inconsistencies. Such deficiencies are likely to exist in other similar datasets, and although the visual approaches we discuss for identifying data problems may not be scalable, the categories of problems we identify may be used to inform attempts to systematically account for errors in larger smartphone datasets.     

移动社交网络中基于网络中心度的链路预测方法*

移动社交网络中的链路预测是指通过已知的网络节点以及移动社交网络结构等信息预测网络中尚未产生连边的两个节点之间产生链接的可能性。基于网络中心度的思想,提出一种适用于移动社交网络的链路预测算法。在该算法中,根据节点网络中心度和共同邻居数来计算两个节点的相似性指标,两个节点的共同邻居数越多、共同邻居的网络中心度越高,则两个节点的相似度越高。另外,由于移动社交网络的动态性特征,还将考虑时间因素对预测结果的影响。将该方法与其他4种常用的链路预测方法进行比较,实验结果显示所提方法要优于其他方法。     

Flocking based evolutionary computation strategy for measuring centrality of online social networks

Centrality in social network is one of the major research topics in social network analysis. Even though there are more than half a dozen methods to find centrality of a node, each of these methods has some drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This new computation technique makes use of parameters that are more realistic and practical in online social networks. The interactions between nodes play a significant role in determining the centrality of node. The new method has been calculated both empirically as well as experimentally. The new method is tested, verified and validated for different sets of random networks and benchmark datasets. The method has been correlated with other state of the art centrality measures. The new centrality measure is found to be realistic and suits well with online social networks. The proposed method can be used in applications such as finding the most prestigious node and for discovering the node which can influence maximum number of users in an online social network. FBCS centrality has higher Kendall’s tau correlation when compared with other state of the art centrality methods. The robustness of the FBCS centrality is found to be better than other centrality measures.     

Introducing a procedure for developing a novel centrality measure (Sociability Centrality) for social networks using TOPSIS method and genetic algorithm

Centrality is one of the most important fields of social network research. To date, some centrality measures based on topological features of nodes in social networks have been proposed in which the importance of nodes is investigated from a certain point of view. Such measures are one dimensional and thus not feasible for measuring sociological features of nodes. Given that the main basis of Social Network Analysis (SNA) is related to social issues and interactions, a novel procedure is hereby proposed for developing a new centrality measure, named Sociability Centrality, based on the TOPSIS method and Genetic Algorithm (GA). This new centrality is not only based on topological features of nodes, but also a representation of their psychological and sociological features that is calculable for large size networks (e.g. online social networks) and has high correlation with the nodes' social skill questionnaire scores. Finally, efficiency of the proposed procedure for developing sociability centrality was tested via implementation on the Abrar Dataset. Our results show that this centrality measure outperforms its existing counterparts in terms of representing the social skills of nodes in a social network.     

Designing business logistics networks using model-based reasoning and heuristic-based searching

This article describes an intelligent system called LogNet that provides advice on how to design business logistics networks. It implements its capabilities by utilizing model-based reasoning techniques. In addition, it utilizes heuristic-based searching to guide an end-user toward more effective network designs.

The network design problem studied in this article addresses the warehouse facility location problem: how many warehouses are needed in a network and to which customer markets should they be assigned? LogNet enables end-users to incrementally create and test a logistics network design by using a graphical user interface. LogNet employs model-based reasoning procedures that analyze the structure of the current network design in order to offer recommendations on how to consolidate or decentralize a network. The overall goal of LogNet is to provide a flexible user interface that is capable of supporting this design task.     

Designing business logistics networks using model-based reasoning and heuristic-based searching

This article describes an intelligent system called LogNet that provides advice on how to design business logistics networks. It implements its capabilities by utilizing model-based reasoning techniques. In addition, it utilizes heuristic-based searching to guide an end-user toward more effective network designs.The network design problem studied in this article addresses the warehouse facility location problem: how many warehouses are needed in a network and to which customer markets should they be assigned? LogNet enables end-users to incrementally create and test a logistics network design by using a graphical user interface. LogNet employs model-based reasoning procedures that analyze the structure of the current network design in order to offer recommendations on how to consolidate or decentralize a network. The overall goal of LogNet is to provide a flexible user interface that is capable of supporting this design task.     

Visual reasoning instructional software system

Visual reasoning is an essential skill for an engineer to possess, particularly as computer-aided design tools become more prevalent. In this paper, we describe an innovative interactive multimedia application that provides a student with the tools to build a strong foundation in visual reasoning. The system, called the Visual Reasoning Tutor, exploits the missing view problem as a mechanism to develop the visual reasoning abilities of students. The Visual Reasoning Tutor provides a student with interactive, graphical operations to construct 3-D geometric objects, varying levels of intelligent critiques throughout the solution process, and a graphical user interface which supports multimedia capabilities aimed to enhance the learning process. This paper presents an overview of the system components, a complete sample exercise, and testing results of several prototypes that have been used in the classroom.     

基于MCMC技术的社会网络搜索

实验和理论表明社会网络中存在着短路径,即人们可以在较少的步数内找到目标。研究了社会网络中的贪婪搜索现象,给出了将长程连接图嵌入底层一维和二维网格的马尔科夫链蒙特卡罗方法。该方法更符合现实情况,坐标体系只是用于网上距离的计算。将算法用于模拟数据（根据一维和二维理想模型产生的图）和真实的社会网络数据均有很好的查询效率,查询成功率高,成功查询平均步长短。     

车载自组织网络动态中心性分析

针对车载自组织网络（VANET）拓扑结构的动态性特征,基于车辆换道功能的智能驾驶移动模型,应用VanetMobiSim仿真软件详细研究车载自组织网络拓扑结构的动态中心性。构建VANET时序网络模型,建立基于衰落因子和信息存储转发指数的动态中心性评价方法,该方法不仅能够描述当前网络拓扑与历史网络拓扑之间的联系,而且能够刻画VANET中信息的存储转发机制;最后,通过仿真实验分析了VANET动态中心性。结果表明虽然VANET拓扑结构的动态中心性随着衰落因子和信息存储转发指数的变化而变化,但重要节点整体的排名基本保持相对稳定的状态。该结论有助于更好地确定信息传播的中继节点,实现信息的成功投递,而且为VANET拓扑结构的抗毁性提供指导。     

Personality assessment using multiple online social networks

Personality plays an important role in various aspects of our daily life. It is being used in many application scenarios such as i) personalized marketing and advertisement of commercial products, ii) designing personalized ambient environments, iii) personalized avatars in virtual world, and iv) by psychologists to treat various mental and personality disorders. Traditional methods of personality assessment require a long questionnaire to be completed, which is time consuming. On the other hand, several works have been published that seek to acquire various personality traits by analyzing Internet usage statistics. Researchers have used Facebook, Twitter, YouTube, and various other websites to collect usage statistics. However, we are still far from a successful outcome. This paper uses a range of divergent features of Facebook and LinkedIn social networks, both separately and collectively, in order to achieve better results. In this work, the big five personality trait model is used to analyze the five traits: openness to experience, conscientiousness, extroversion, agreeableness, and neuroticism. The experimental results show that the accuracy of personality detection improves with the use of complementary features of multiple social networks (Facebook and LinkedIn, in our case) for openness, conscientiousness, agreeableness, and neuroticism. However, for extroversion we found that the use of only LinkedIn features provides better results than the use of only Facebook features or both Facebook and LinkedIn features.     

Planning for tourism routes using social networks

Traveling recommendation systems have become very popular applications for organizing and planning tourist trips. Among other challenges, these applications are faced with the task of maintaining updated information about popular tourist destinations, as well as providing useful tourist guides that meet the users preferences. In this work we present the PlanTour, a system that creates personalized tourist plans using the human-generated information gathered from the minube¹ traveling social network. The system follows an automated planning approach to generate a multiple-day plan with the most relevant points of interest of the city/region being visited. Particularly, the system collects information of users and points of interest from minube, groups these points with clustering techniques to split the problem into per-day sub-problems. Then, it uses an off-the-shelf domain-independent automated planner that finds good quality tourist plans. Unlike other tourist recommender systems, the PlanTour planner is able to organize relevant points of interest taking into account user’s expected drives, and user scores from a real social network. The paper also highlights how to use human provided recommendations to guide the search for solutions of combinatorial tasks. The resulting intelligent system opens new possibilities of combining human-generated knowledge with efficient automated techniques when solving hard computational tasks. From an engineering perspective we advocate for the use of declarative representations of problem solving tasks that have been shown to improve modeling and maintenance of intelligent systems.     

Uncertain reasoning about agents' beliefs and reasoning

Reasoning about mental states and processes is important in varioussubareas of the legal domain. A trial lawyer might need to reason andthe beliefs, reasoning and other mental states and processes of membersof a jury; a police officer might need to reason about the conjecturedbeliefs and reasoning of perpetrators; a judge may need to consider adefendant's mental states and processes for the purposes of sentencing;and so on. Further, the mental states in question may themselves beabout the mental states and processes of other people. Therefore, if AIsystems are to assist with reasoning tasks in law, they may need to beable to reason about mental states and processes. Such reasoning isriddled with uncertainty, and this is true in particular in the legaldomain. The article discusses how various different types ofuncertainty arise, and shows how they greatly complicate the task ofreasoning about mental states and processes. The article concentrates onthe special case of states of belief and processes of reasoning, andsketches an implemented, prototype computer program (ATT-Meta) thatcopes with the various types of uncertainty in reasoning about beliefsand reasoning. In particular, the article outlines the system'sfacilities for handling conflict between different lines of argument,especially when these lie within the reasoning of different people. Thesystem's approach is illustrated by application to a real-life muggingexample.     

Visual analysis of large heterogeneous social networks by semantic and structural abstraction

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies     

Arnetminer: expertise oriented search using social networks

Expertise Oriented Search (EOS) aims at providing comprehensive expertise analysis on data from distributed sources. It is useful in many application domains, for example, finding experts on a given topic, detecting the confliction of interest between researchers, and assigning reviewers to proposals. In this paper, we present the design and implementation of our expertise oriented search system, Arnetminer (). Arnetminer has gathered and integrated information about a half-million computer science researchers from the Web, including their profiles and publications. Moreover, Arnetminer constructs a social network among these researchers through their co-authorship, and utilizes this network information as well as the individual profiles to facilitate expertise oriented search tasks. In particular, the co-authorship information is used both in ranking the expertise of individual researchers for a given topic and in searching for associations between researchers. We have conducted initial experiments on Arnetminer. Our results demonstrate that the proposed relevancy propagation expert finding method outperforms the method that only uses person local information, and the proposed two-stage association search on a large-scale social network is order of magnitude faster than the baseline method.     

Genetic clustering of social networks using random walks

In the era of globalization, traditional theories and models of social systems are shifting their focus from isolation and independence to networks and connectedness. Analyzing these new complex social models is a growing, and computationally demanding area of research. In this study, we investigate the integration of genetic algorithms (GAs) with a random-walk-based distance measure to find subgroups in social networks. We test our approach by synthetically generating realistic social network data sets. Our clustering experiments using random-walk-based distances reveal exceptionally accurate results compared with the experiments using Euclidean distances.     

模糊推理的统计敏感性分析

在设计模糊逻辑系统时，如何实现其对输入噪声的鲁棒性是一个首要的问题，相应地如何很好地分析其对输入噪声的鲁棒性（也称敏感性分析）也就成了一个重要问题．使用统计的方法，对常见的模糊推理方法进行了敏感性分析．首先以均值与方差为基础，提出了2个模糊集的ε-统计相等的概念；随后导出了常见的模糊推理方法的统计敏感性，这包括链接模糊推理与多规则模糊推理．与前人相关工作不同的是，更着重于模糊推理的方差分析，这一方法从数理统计的角度来看能更好地揭示模糊推理本质的敏感性．     

MEBC: social network immunization via motif-based edge-betweenness centrality

Knowledge and Information Systems - Immunization of social networks has attracted increasing attention over the last decade. Various algorithms have been proposed based on the topological structure...