Modelling and analysis of pipelined circuit switching in interconnection networks with bursty traffic and hot-spot destinations

Pipelined circuit switching (PCS) that combines the advantages of both circuit switching and wormhole switching is an efficient method for passing messages in interconnection networks. Analytical modelling is a cost-effective tool and plays an important role in achieving a clear understanding of the network performance. However, most of the existing models for PCS are unable to capture the realistic nature of message behaviours generated by real-world applications, which have a significant impact on the design and performance of communication networks. This paper presents a new analytical model for PCS in interconnection networks in the presence of bursty and correlated message arrivals coupled with hot-spot destinations, which can capture the bursty message arrival process and non-uniform distribution of message destinations. Such a traffic pattern has been found in many practical communication environments. The accuracy of the proposed analytical model is validated through extensive simulation experiments. The model is then applied to investigate the effects of the bursty message arrivals and hot-spot destinations on the performance of interconnection networks with PCS.     

基于兴趣匹配的机会社会网络消息分发机制

机会社会网络(opportunistic social networks)能够利用节点移动创造的相遇机会,在缺乏持续端到端连接的网络中,为用户提供稳定的消息分发途径,但在消息分发效率以及用户体验方面存在不足.为提高消息分发系统的性能、改善网络用户体验,提出一种基于节点兴趣匹配的机会社会网络分发机制.通过引入混合结构的机会社会网络分发系统解决网络拓扑信息获取不全与节点计算能力不足的问题;从节点行为规律与兴趣爱好2方面对网络进行分析,并提出一种用于复杂关系数据分析的联合聚类方法;针对用户需求,设计消息属性与节点兴趣匹配优先的消息分发策略.仿真结果表明,该机制能够在投递率、投递时延、缓存占用率等方面提升网络性能,且具有较高的分发效率、覆盖率与兴趣匹配度.     

Police message diffusion on Twitter: analysing the reach of social media communications

Social media are becoming increasingly important for communication between government organisations and citizens. Although research on this issue is expanding, the structure of these new communication patterns is still poorly understood. This study contributes to our understanding of these new communication patterns by developing an explanatory model of message diffusion on social media. Messages from 964 Dutch police force Twitter accounts are analysed using trace data drawn from the Twitter^? API to explain why certain police tweets are forwarded and others are not. Based on an iterative human calibration procedure, message topics were automatically coded based on customised lexicons. A principal component analysis of message characteristics generated four distinct patterns of use in (in)personal communication and new/versus reproduced content. Message characteristics were combined with user characteristics in a multilevel logistic general linear model. Our main results show that URLs or use of informal communication increases chances of message forwarding. In addition, contextual factors such as user characteristics impact diffusion probability. Recommendations are discussed for further research into authorship styles and their implications for social media message diffusion. For the police and other government practitioners, a list of recommendation about how to reach a larger number of citizens through social media communications is presented.     

A directional data dissemination protocol for vehicular environments

This paper presents a simple and robust dissemination protocol that efficiently deals with data dissemination in both dense and sparse vehicular networks. Our goal is to address highway scenarios where vehicles equipped with sensors detect an event, e.g., a hazard and broadcast an event message to a specific direction of interest. In order to deal with broadcast communication under diverse network densities, we design a dissemination protocol in such a way that: (i) it prevents the so-called broadcast storm problem in dense networks by employing an optimized broadcast suppression technique; and (ii) it efficiently deals with disconnected networks by relying on the store-carry-forward communication model. The novelty of the protocol lies in its simplicity and robustness. Simplicity is achieved by only considering two states (i.e., cluster tail and non-tail) for vehicles. Furthermore, vehicles in both directions help disseminating messages in a seamlessly manner, without resorting to different operation modes for each direction. Robustness is achieved by assigning message delivery responsibility to multiple vehicles in sparse networks. Our simulation results show that our protocol achieves higher delivery ratio and higher robustness when compared with DV-CAST under diverse road scenarios.     

SMSAD: a framework for spam message and spam account detection

Multimedia Tools and Applications - Short message communication media, such as mobile and microblogging social networks, have become attractive platforms for spammers to disseminate unsolicited...     

基于Moran过程的竞争性舆情信息的网络传播博弈模型

新的媒体环境下,在享受不同形式在线社交网络提供便捷信息互动渠道的同时,需要加强对信息传播的管理与引导,防止其发展成为负面舆情信息肆意滋生的温床。结合传统演化博弈论与复杂网络理论,提出了竞争性舆情信息的网络传播博弈模型,并理论分析了正面舆情信息传播占优的收益条件及群体规模;随后,结合实际案例,通过仿真实验对比分析三种博弈环境下网络舆情的演化特征。三种博弈环境下,正面舆情信息传播策略占优条件均与收益有关,弱选择情境对收益参数的要求最为严格;强、弱选择情景下策略占优的条件还与群体规模有关,且强选择情景对群体规模的要求更为严格。     

Event detection over twitter social media streams

In recent years, microblogs have become an important source for reporting real-world events. A real-world occurrence reported in microblogs is also called a social event. Social events may hold critical materials that describe the situations during a crisis. In real applications, such as crisis management and decision making, monitoring the critical events over social streams will enable watch officers to analyze a whole situation that is a composite event, and make the right decision based on the detailed contexts such as what is happening, where an event is happening, and who are involved. Although there has been significant research effort on detecting a target event in social networks based on a single source, in crisis, we often want to analyze the composite events contributed by different social users. So far, the problem of integrating ambiguous views from different users is not well investigated. To address this issue, we propose a novel framework to detect composite social events over streams, which fully exploits the information of social data over multiple dimensions. Specifically, we first propose a graphical model called location-time constrained topic (LTT) to capture the content, time, and location of social messages. Using LTT, a social message is represented as a probability distribution over a set of topics by inference, and the similarity between two messages is measured by the distance between their distributions. Then, the events are identified by conducting efficient similarity joins over social media streams. To accelerate the similarity join, we also propose a variable dimensional extendible hash over social streams. We have conducted extensive experiments to prove the high effectiveness and efficiency of the proposed approach.     

The dynamic role of social media during Hurricane #Sandy: An introduction of the STREMII model to weather the storm of the crisis lifecycle

Social media has introduced a key ingredient, and a potentially unfamiliar variable, into the practice of crisis communication. In this paper, the authors critically assess the social media milestones related to Hurricane Sandy according to situational crisis communication theory (SCCT) (Coombs, 2007). Further, we discuss the crisis lifecycle of Hurricane Sandy with regard to the potential implementation of the STREMII model of social media crisis management, a proposed model originated through this research application. This original model develops from lessons and best practices discovered in historical and contemporary cases of social media crises and crisis management. The researchers acknowledge potential limitations and describe steps for further development of the model through research, all the while recognizing the powerful and paradoxical role of social media in the crisis management process. In reflection of Hurricane Sandy, further qualitative and quantitative examinations of crisis events are encouraged to evaluate the STREMII model continually in the dynamic social media climate and across the vast facets of crisis communication.     

大规模社交网络中高效的关键用户选取方法

针对大规模社交网络及其用户发布消息的历史数据,如何快速有效地选取具有较强信息传播能力的关键用户,提出了一种关键用户选取方法。首先,利用社交网络的结构信息,构建以用户为节点的有向图,利用用户发布消息的历史数据,基于Spark计算框架,定量计算由用户活跃度、转发交互度和信息量占比刻画的权重,从而构建社交网络的有向带权图模型;然后,借鉴PageRank算法,建立用户信息传播能力的度量机制,给出基于Spark的大规模社交网络中用户信息传播能力的计算方法;进而,给出基于Spark的d-距选取算法,通过多次迭代,使得所选取的不同关键用户的信息传播范围尽量少地重叠。建立在新浪微博数据上的实验结果表明,所提方法具有高效性、可行性和可扩展性,对于控制不良突发信息传播、社交网络舆情监控具有一定的支撑作用。     

Stepwise fair-share buffering for gossip-based peer-to-peer data dissemination

We consider buffer management in support of large-scale gossip-based peer-to-peer data dissemination protocols. Coupled with an efficient buffering mechanism, system-wide buffer usage can be optimized while providing reliability and scalability in such protocols. We propose a novel approach, stepwise fair-share buffering, that provides uniform load distribution and reduces the overall buffer usage where every peer has a partial view of the system. We report and discuss the comparative performance results with existing buffering approaches as well as random buffering which serves as a benchmark. We present separate evaluations of bufferer selection and gossip-based data dissemination. Reliability, content dissemination time, message delay, buffering delay, and minimum buffer requirements are considered as the key metrics investigated through simulations. The performance of our approach in the case of multiple senders, link failures with multiple bufferers, and scalability to larger networks are investigated. Several power-law and hierarchical overlay topologies are considered. Analytical bounds for reliability of dissemination are also provided.     

强关系型社交媒体平台不实信息传播模型研究

社交媒体是当前人们获取信息的重要渠道,但是社交媒体中不实信息大量传播造成社会危害的问题一直困扰着管理者。为了减轻不实信息造成的不良社会影响,对不实信息的传播规律进行研究。划分出强关系型社交媒体,通过Multi-Agent建模的方法,引入用户个体异质性和决策行为不确定性,建立不实信息传播模型。用控制变量法,在多种网络中进行仿真模拟。研究表明：不实信息传播的初始节点不完全能够决定不实信息的传播范围;不实信息传播过程的缓解期长短与网络的随机性有关;用户观点共鸣度分布会对不实信息传播产生影响。     

基于官方信息控制的多层网络谣言传播模型

官方信息在现实生活和虚拟网络中的扩散对谣言传播具有双重影响。本文建立多层网络传播模型研究跨境电商新政策错误解读这一谣言传播问题,模型上层为虚拟网络传播层(虚拟层),下层为现实社会传播层(现实层),个体根据各自社交网络和行为习惯接收官方信息来控制多层网络谣言传播。文章根据平均场方法分析该模型的演化过程和传播阈值,采用无标度网络进行仿真验证了理论分析传播动力学行为的正确性,结果表明,官方信息对虚拟层和社会层谣言的控制都具有积极作用,官方信息的掌握程度对社会谣言传播有显著性影响,且当官方信息的网络传播达到一定规模时,能够有效约束现实社会个体行为,进而达到虚拟层和现实层对谣言控制的同向促进作用。     

Redundant data transmission in control/estimation over lossy networks

In lossy networks the probability of successful communication can be significantly increased by transmitting multiple copies of a same message through independent channels. In this paper we show that communication protocols that exploit this by dynamically assigning the number of transmitted copies of the same data can significantly improve the control performance in a networked control system with only a modest increase in the total number of transmissions. We develop techniques to design communication protocols that exploit the transmission of multiple packets while seeking a balance between stability/estimation performance and communication rate. An average cost optimality criterion is employed to obtain a number of optimal protocols applicable to networks with different computational capabilities. We also discuss stability results under network contention when multiple nodes utilize these protocols.     

Optimal total exchange in Cayley graphs

Consider an interconnection network and the following situation: Every node needs to send a different message to every other node. This is the total exchange or all-to-all personalized communication problem, one of a number of information dissemination problems known as collective communications. Under the assumption that a node can send and receive only one message at each step (single-port model), it is seen that the minimum time required to solve the problem is governed by the status (or total distance) of the nodes in the network. We present a time-optimal solution for any Cayley network. Rings, hypercubes, cube-connected cycles, and butterflies are some well-known Cayley networks which can take advantage of our method. The solution is based on a class of algorithms which we call node-invariant algorithms and which behave uniformly across the network     

Navigation your way: from spontaneous independent exploration to dynamic social journeys

In this article, we describe a novel approach to pedestrian navigation using bearing-based haptic feedback. People are guided in the general direction of their destination via a minimal directional cue, but additional exploration is stimulated by varying feedback based on the potential for taking alternative routes. This extreme navigation method removes the complexities of maps and direction following, concentrating on allowing pedestrians to actively explore their surroundings, rather than offering perfect, but passive, turn-by-turn guidance. We simulate and build two mobile prototypes to examine the possible benefits of this approach, then further extend its impact by considering how social media might be incorporated to provide a real-time, dynamically evolving map of physical locations. The successful use of our mobile prototypes is demonstrated in a realistic field trial, and we discuss the results and interesting participant behaviours that were recorded, validating the predictions from their earlier simulation. We continue by simulating the use of publicly posted status updates and pictures as a proxy for location mapping, showing how these methods can produce comparable navigation results to real-world field trials, highlighting their potential as tools for real-world social journeys.     

机会移动社交网络中基于群组构造的数据分发机制

机会移动社交网络(opportunistic mobile social networks, OMSNs)是一种利用节点的相遇机会进行端到端无线数据传输的网络.随着人们使用移动智能终端数量的剧增,为建立泛在的数据传输基础设施提供了机会,因此研究机会移动社交网络的数据传输机制具有重要意义.为了提高机会移动社交网络的数据传输性能,提出了一种基于群组构造的数据分发机制(data dissemination mechanism based on group structure, DDMGS).首先,基于用户的行为属性,即节点重要性、兴趣相似度和通信关系紧密度,设计关系度量模型.其次,依据不同的行为属性关系构成的网络拓扑特征设计群组构造算法：基于位置关系的拓扑结构具有周期稳定性,基于兴趣关系的拓扑结构具有长期稳定性,而基于通信关系的拓扑结构具有动态性.为进一步提高数据分发性能和网络的整体性能,还设计了节点缓冲区管理机制,引入了合作博弈理论加强节点之间的合作能力,规避节点的自私行为.仿真验证表明DDMGS与直接投递路由、先知路由以及Simbet路由和Epidemic路由相比具有较好的性能,提高了消息传输成功率,减少了平均跳数,该算法是可行的.     

Interest spaces: A unified interest-based dissemination framework for opportunistic networks

Embedded ubiquitous computing systems (EUCS) promise to grow remarkably in the near future. For such systems, new communication technologies are constructed to cope with the increasing functional and temporal demans. Because of the ubiquitousness of mobile devices nowadays, for example, traditional publish/subscribe is no longer an adequate model for data dissemination in mobile networks. Since any node can publish content at any time, the network can get congested easily, so a dissemination paradigm where mobile nodes contribute with a fraction of their resources is needed, through the use of opportunistic networks. Furthermore, a suitable organization for data dissemination in mobile networks should be centered around interests. Thus, we propose a unified interest-based dissemination framework for opportunistic networks entitled Interest Spaces, which simplifies dissemination by just allowing applications to mark data items with certain tags, letting the framework handle the caching, routing, forwarding, and disseminating. Similarly, applications that need to subscribe to channels simply have to specify the tags they are interested in, and the framework does the rest. However, the Interest Spaces framework also allows applications to have more control over the dissemination process if they wish, by specifying various criteria and dissemination rules. In this article, we present the architecture and components of Interest Spaces. We focus on data aggregation at the context layer, showing through simulations the benefits it brings in opportunistic dissemination. We also present a real-life use case for Interest Spaces through Chatty, an opportunistic chat application.     

一种基于模糊相似关系的局部社区发现方法

近几年,在线社交媒体发展飞速,出现了大规模社会网络.传统的基于网络全局结构的社区发现方法难以有效地处理这些大网络.局部社区发现作为一种无需知道网络的全局结构、仅通过分析给定节点的周围节点之间的关系即可找出给定节点所在社区的方法,在社会网络大数据分析中具有重要的应用意义.针对真实世界网络结构中个体间的相似关系是模糊的或不确定性的,提出了一种基于模糊相似关系的局部社区发现方法.首先,采用模糊关系来描述两个节点之间的相似关系,以节点对的相似度作为该模糊关系的隶属函数;然后证明了该关系是一种模糊相似关系,将局部社区定义为给定节点关于模糊相似关系的等价类,进而采用最大连通子图算法求得给定节点所在的社区.分别在仿真网络和真实网络上进行了实验,实验结果表明,该算法能够有效地揭示出给定节点所在的局部社区,相比其他算法,具有更高的F-score.     

基于在线社交网络的动态消息传播模型

传统传播模型较难描述在线社交网络中的复杂活跃模式以及节点间的拓扑差异,并且其接触式的传播者退化方式也与现实不符。针对理论模型模拟与现实消息传播的不符,提出一个基于在线社交网络的动态消息传播模型D-SIR。该模型考虑了在线社交网络中影响消息传播的一些实际因素,引入基于传播延迟的退化方式使传播者自发地退化成免疫者,动态指定节点的权威度和免疫力以适应非均质网络,并考虑接收增强信号效应以及外部社会加强效果。在采集的新浪微博真实传播网络数据中,通过参数变化的传播仿真实验验证了D-SIR模型可以有效反映在线社交网络的现实传播情形,并且较传统模型更具灵活性及可扩展性。     

One Social Movement,Two Social Media Sites: A Comparative Study of Public Discourses

Social media have become central places where public discourses are generated, sustained, and circulated around public events. So far, much research has examined large-scale dissemination patterns of prominent statements, opinions, and slogans circulated on social media, such as the analysis of keywords and hashtags on Twitter regarding a political event. However, little attention has been paid to understanding how local socio-cultural-political conditions influence the formation and development of public discourses on social media. To explore this question, we analyzed public discourses about Hong Kong’s Umbrella Movement on two distinct social media sites, Facebook and Weibo, the largest micro-blogging service in China. Facebook topped Hong Kong citizens’ usage of social media sites, while Weibo’s primary user base is mainland Chinese. The social movement and these two social media sites provide a unique opportunity to explore the commonalities and differences between social media discourses generated by two different cultures. Using grounded theory and discourse analysis, we reveal how people on two sites reasoned about the many incidents of the movement and developed sometimes similar but other times strikingly different discourses. We trace the links between different discourses and the socio-cultural-political conditions of Hong Kong and mainland China. We discuss how this study may contribute deeper understandings of public discourses on social media to the CSCW literature.