间接互惠与合作演化的若干问题研究进展

2005年Science杂志指出"合作行为如何进化"是21世纪最关键的25个科学问题之一.间接互惠如何促进合作演化的研究已吸引了包括经济学家、社会学家和演化生物学家等众多学者的关注.这是由于：人类社会道德的形成、社会化分工、语言的出现、人类大脑的进化等都和间接互惠密不可分;随着经济全球化和网络时代的到来,依赖声望和信誉的陌生个体间的交易日益频繁,局部信息条件下个体的信任被利用的"道德风险"逐渐增大.本文所关注的间接互惠是以声望为核心的"下游互惠",具体而言,个体通过帮助他人建立自己在群体中的好声望,从而期待未来获得他人的帮助.可见,声望是"下游互惠"发挥作用的关键.声望的建立引发了两方面的研究：1）如何评价个体声望的好与坏,焦点是何种声望评估准则能够促进合作的演化;2）个体的声望如何在群体中快速、准确、广泛地传播,使得陌生个体间能够获得彼此的声望信息,其中八卦这种声望传播方式成为间接互惠的研究热点之一.基于声望的间接互惠研究前景广阔,未来可能的研究方向主要有复杂网络上的间接互惠、声望传播系统的鲁棒性、声望共享系统的建立和间接互惠在P2P网络中的应用.     

一个有效的动态网络节点影响力模型

网络节点影响力度量对社会网络研究具有重要的价值,静态网络的影响力度量是目前研究的主要问题。然后社会网络的结构经常会随着时间变化,呈现出动态网络。静态网络节点影响力度量模型虽然可以对动态网络不同时间点上的快照进行度量,然后这种机制很难刻画动态网络节点影响力的变化过程。本文将动态网络建模为不同时间点网络的叠加快照,然后构建了动态网络边权重衰减和节点影响力衰减机制,基于衰减机制提出了动态网络节点影响力模型,模型可以应用于加权或无权动态网络节点影响力度量。为了客观衡量本文模型的性能,在一个模拟网络和三个真实网络进行了不同实验。在模拟网络上,将结果与人工标注的结果计算肯德尔系数,针对三个真实网络则进行了不同角度的影响力效果分析。实验结果表明本文模型不仅可以较好的刻画动态网络节点影响力的变化过程,还可以准确度量动态网络节点影响力。     

A regularized graph layout framework for dynamic network visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling and dynamic graph Laplacian layout. We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.     

Stochastic actor-oriented modeling for studying homophily and social influence in OSS projects

Open Source Software projects are communities in which people “learn the ropes” from each other. The social and technical activities of developers evolve together, and as they link to each other they get organized in a network of changing socio-technical connections. Traces of those activities, or behaviors, are typically visible to all, in project repositories and through communication between them. Thus, in principle it may be possible to study those traces to tell which of the observable socio-technical behaviors of developers in these projects are responsible for the forming of persistent links between them. It may also be possible to tell the extent to which links participate in the spread of potential behavioral influences. Since OSS projects change in both social and technical activity over time, static approaches, that either ignore time or simplify it to a few slices, are frequently inadequate to study these networks. On the other hand, ad-hoc dynamic approaches are often only loosely supported by theory and can yield misleading findings. Here we adapt the stochastic actor-oriented models from social network analysis. These models enable the study of the interplay between behavior, influence and network architecture, for dynamic networks, in a statistically sound way. We apply the stochastic actor-oriented models in case studies of two Apache Software Foundation projects, and study code ownership and developer productivity as behaviors. For those, we find evidence of significant social selection effects (homophily) in both projects, but in different directions. However, we find no evidence for the spread (social influence) of either code ownership or developer productivity behaviors through the networks.     

基于随机模型检验的社交网络隐私保护研究

针对现有社交网络所提供静态隐私策略的隐私设置不够灵活且难以定量验证问题,提出一种动态隐私保护框架,将社交网络建模为离散时间马尔科夫链模型,通过设置触发条件实现用户动态隐私规约并将其转化为概率计算树逻辑公式,同时结合随机模型检验和运行时验证中的参数化与监控技术,保护社交网络发生随机故障情况下的用户动态隐私信息.在Dias...     

Incremental K-clique clustering in dynamic social networks

Clustering entities into dense parts is an important issue in social network analysis. Real social networks usually evolve over time and it remains a problem to efficiently cluster dynamic social networks. In this paper, a dynamic social network is modeled as an initial graph with an infinite change stream, called change stream model, which naturally eliminates the parameter setting problem of snapshot graph model. Based on the change stream model, the incremental version of a well known k-clique clustering problem is studied and incremental k-clique clustering algorithms are proposed based on local DFS (depth first search) forest updating technique. It is theoretically proved that the proposed algorithms outperform corresponding static ones and incremental spectral clustering algorithm in terms of time complexity. The practical performances of our algorithms are extensively evaluated and compared with the baseline algorithms on ENRON and DBLP datasets. Experimental results show that incremental k-clique clustering algorithms are much more efficient than corresponding static ones, and have no accumulating errors that incremental spectral clustering algorithm has and can capture the evolving details of the clusters that snapshot graph model based algorithms miss.     

动态网络模式挖掘方法及其应用

静态复杂网络研究在揭示社会网络、信息网络和生物网络的形成和演化机制方面取得了重要成果,其方法和结果对系统生物学产生了重要影响.但现实世界中,很多网络是随时间发生变化的,即动态网络.以动态网络为对象,对动态网络的拓扑特性分析、动态网络相关的各种模式挖掘模型和方法进行了综述、比较和分析.特别地,将动态网络模式分析方法应用于生物网络和社会网络,分析了生物网络相关的动态功能模块和模式演化问题、科学家合作网络和社交网络的动态模式.最后指出了动态网络的模式挖掘方法及其在动态生物网络和社会网络研究中存在的问题和挑战,并对未来的研究方向进行了分析.     

A new dynamic hierarchical reputation evaluation scheme for hybrid wireless mesh networks

A hybrid wireless mesh network has the advantage of a flexible infrastructure but is vulnerable to attacks. Traditional reputation schemes are common approaches to address security, but they are not suitable for direct application to hybrid wireless mesh networks because the node cooperation and hierarchical construction are not considered. In this paper, we propose a new dynamic hierarchical reputation evaluation scheme (DHRES) to provide security for hybrid wireless mesh networks. In this scheme, the virtual cluster structure is built to introduce the reputation relation, including the related nodes’ roles and functions. Moreover, the reputation evaluation mechanism is based on the correlation between nodes such that the reputation information of the nodes is updated according to their different roles. Simulation results show that, compared with traditional reputation models, the proposed scheme can more accurately reflect the security status of nodes, particularly in cases where the malicious nodes are mesh clients.     

动态社区发现方法研究综述

随着社交媒体多样性的增加,实时分析社交网络的需求不断增大,动态社区发现的研究受到了广泛的关注。已有的社区发现综述多是侧重静态社区发现,以及相关方法的探讨,无法进行网络演化分析,此外社区的实体数据往往具有交叉更替性和时序性,因此对动态社区发现的研究现状进行分析和综述。首先,基于复杂网络的研究背景,提出了通用的动态社区发现研究框架;接着,形式化表示动态社区发现的相关定义,并从网络层面和节点层面对动态社区演化进行详细分析;然后,根据架构和技术的不同,对动态社区发现方法进行归纳分类,并结合常用数据集和评价指标对经典静态社区发现算法进行定性和定量分析;最后,介绍了社区发现的典型应用场景,探讨了当前动态社区发现研究面临的主要挑战,针对性地提出了相关解决方案,为动态社区发现研究领域勾画出较为清晰和全面的研究方向。     

Asymptotic behaviours of a class of threshold models for collective action in social networks

A class of dynamic threshold models is proposed for describing the upset of collective actions in social networks. The agents of the network have to decide whether to undertake certain action or not. They make their decision by comparing the activity level of their neighbours with a time-varying threshold, evolving according to a time-invariant opinion dynamic model. Key features of the model are a parameter representing the degree of self-confidence of the agents and the mechanism adopted by the agents to evaluate the activity level of their neighbours. The case in which a radical agent, initially eager to undertake the action, interacts with a group of ordinary agents, is considered. The main contribution of the paper is the complete characterisation of the asymptotic behaviours of the network, for three different graph topologies. The asymptotic activity patterns are determined as a function of the self-confidence parameter and of the initial threshold of the ordinary agents. Numerical validation on a real ego network shows that the theoretical results obtained for simple graph structures provide useful insights on the network behaviour in more complex settings.     

Reconstructing social interactions using an unreliable wireless sensor network

In the very active field of complex networks, research advances have largely been stimulated by the availability of empirical data and the increase in computational power needed for their analysis. These works have led to the identification of similarities in the structures of such networks arising in very different fields, and to the development of a body of knowledge, tools and methods for their study.While many interesting questions remain open on the subject of static networks, challenging issues arise from the study of dynamic networks. In particular, the measurement, analysis and modeling of social interactions are first class concerns.In this article, we address the challenges of capturing physical proximity and social interaction by means of a wireless network. In particular, as a concrete case study, we exhibit the deployment of a wireless sensor network applied to the measurement of health care workers’ exposure to tuberculosis-infected patients in a service unit of the Bichat-Claude Bernard hospital in Paris, France. This network has continuously monitored the presence of all HCWs in all rooms of the service during a three month period.We both describe the measurement system that was deployed and some early analysis on the measured data. We highlight the bias introduced by the measurement system reliability and provide a reconstruction method which not only leads to a significantly more coherent and realistic dataset but also evidences phenomena a priori hidden in the raw data. By this analysis, we suggest that a processing step is required prior to any adequate exploitation of data gathered thanks to a non-fully reliable measurement architecture.     

Peri-Watchdog: Hunting for hidden botnets in the periphery of online social networks

In order to evade detection of ever-improving defense techniques, modern botnet masters are constantly looking for new communication platforms for delivering C&C (Command and Control) information. Attracting their attention is the emergence of online social networks such as Twitter, as the information dissemination mechanism provided by these networks can naturally be exploited for spreading botnet C&C information, and the enormous amount of normal communications co-existing in these networks makes it a daunting task to tease out botnet C&C messages.Against this backdrop, we explore graph-theoretic techniques that aid effective monitoring of potential botnet activities in large open online social networks. Our work is based on extensive analysis of a Twitter dataset that contains more than 40 million users and 1.4 billion following relationships, and mine patterns from the Twitter network structure that can be leveraged for improving efficiency of botnet monitoring. Our analysis reveals that the static Twitter topology contains a small-sized core sugraph, after removing which, the Twitter network breaks down into small connected components, each of which can be handily monitored for potential botnet activities. Based on this observation, we propose a method called Peri-Watchdog, which computes the core of a large online social network and derives the set of nodes that are likely to pass botnet C&C information in the periphery of online social network. We analyze the time complexity of Peri-Watchdog under its normal operations. We further apply Peri-Watchdog on the Twitter graph injected with synthetic botnet structures and investigate the effectiveness of Peri-Watchdog in detecting potential C&C information from these botnets.To verify whether patterns observed from the static Twitter graph are common to other online social networks, we analyze another online social network dataset, BrightKite, which contains evolution of social graphs formed by its users in half a year. We show not only that there exists a similarly relatively small core in the BrightKite network, but also this core remains stable over the course of BrightKite evolution. We also find that to accommodate the dynamic growth of BrightKite, the core has to be updated about every 18 days under a constrained monitoring capacity.     

SFFS-PC-NN optimized by genetic algorithm for dynamic prediction of financial distress with longitudinal data streams

Recently, research of financial distress prediction has become increasingly urgent. However, existing static models for financial distress prediction are not able to adapt to the situation that the sample data flows constantly with the lapse of time. Financial distress prediction with static models does not meet the demand of the dynamic nature of business operations. This article explores the theoretical and empirical research of dynamic modeling on financial distress prediction with longitudinal data streams from the view of individual enterprise. Based on enterprise’s longitudinal data streams, dynamic financial distress prediction model is constructed by integrating financial indicator selection by using sequential floating forward selection method, dynamic evaluation of enterprise’s financial situation by using principal component analysis at each longitudinal time point, and dynamic prediction of financial distress by using back-propagation neural network optimized by genetic algorithm. This model’s ex-ante prediction efficiently combines its ex-post evaluation. In empirical study, three listed companies’ half-year longitudinal data streams are used as the sample set. Results of dynamic financial distress prediction show that the longitudinal and dynamic model of enterprise’s financial distress prediction is more effective and feasible than static model.     

Protecting shared information in networks: A network security game with strategic attacks

A digital security breach, by which confidential information is leaked, does not only affect the agent whose system is infiltrated but is also detrimental to other agents socially connected to the infiltrated system. Although it has been argued that these externalities create incentives to underinvest in security, this presumption is challenged by the possibility of strategic adversaries that attack the least protected agents. In this paper we study a new model of security games in which agents share tokens of sensitive information in a network of contacts. The agents have the opportunity to invest in security to protect against an attack that can be either strategically or randomly targeted. We show that, in the presence of random attack, underinvestments always prevail at the Nash equilibrium in comparison with the social optimum. Instead, when the attack is strategic, either underinvestments or overinvestments are possible, depending on the network topology and on the characteristics of the process of the spreading of information. Actually, agents invest more in security than socially optimal when dependencies among agents are low (which can happen because the information network is sparsely connected or because the probability that information tokens are shared is small). These overinvestments pass on to underinvestments when information sharing is more likely (and therefore, when the risk brought by the attack is higher). In order to keep our analysis tractable, some of our results on strategic attacks make an assumption of homogeneity in the network, namely, that the network is vertex‐transitive. We complement these results with an analysis on star graphs (which are nonhomogeneous), which confirms that the essential lines of our findings can remain valid on general networks.     

A hierarchical overlay with cluster-based reputation tree for dynamic peer-to-peer systems

Traditional peer-to-peer technologies and systems assume that people operate with desktop computers in fixed broadband networks. When people with modern mobile devices now access Internet and Web services much in the manner they used to on desktop computers, the classical peer-to-peer overlay models can be vulnerable in wireless and mobile networks. This paper proposes a hierarchical overlay architecture based on partially central and semi-structured overlay models for the deployment of peer-to-peer systems in dynamic network environments. To keep up system scalability and efficacy, this architecture design exploits peer locality and network proximity, and contends with several problems of peer churn, peer mobility, search redundancy and traffic overhead that become much stickier in dynamic network environments. This design also integrates the reputation notion to mitigate the free-riding problem in peer-to-peer systems. According to a special cluster-based reputation tree, the hierarchical overlay is adjustable to moderate unfair or imbalanced resource utilization over the system. Furthermore, the cluster hierarchy is resilient to any points of failure at peer clusters in the overlay topology. Therefore, the effort of this study achieves an efficient and robust overlay architecture in dynamic network environments. Simulation results show that the proposed architecture is not only scalable to peer population, but also sustainable to peer- and network-initiated dynamics and influences in peer-to-peer systems.     

A comparative study of collaboration-based reputation models for social recommender systems

Today, people increasingly leverage their online social networks to discover meaningful and relevant information, products and services. Thus, the ability to identify reputable online contacts with whom to interact has become ever more important. In this work we describe a generic approach to modeling user and item reputation in social recommender systems. In particular, we show how the various interactions between producers and consumers of content can be used to create so-called collaboration graphs, from which the reputation of users and items can be derived. We analyze the performance of our reputation models in the context of the HeyStaks social search platform, which is designed to complement mainstream search engines by recommending relevant pages to users based on the past experiences of search communities. By incorporating reputation into the existing HeyStaks recommendation framework, we demonstrate that the relevance of HeyStaks recommendations can be significantly improved based on data recorded during a live-user trial of the system.     

Summarizing dynamic Social Tagging Systems

Social tagging is a popular method that allows users of social networks to share annotation in the form of keywords, called tags, assigned to resources. Social tagging addresses information overload by easing the task of locating interesting entities in a social network. Nevertheless, users can still be overwhelmed by too many tags posted at each moment. A process is needed that offers an accurate overview of the representative entities and their relationships with each other, while dealing with the dynamics of social tagging and of tags’ semantics. We propose a method for the automated summarization of an evolving multi-modal social network, focusing on the entities that stay representative over time for some subnetwork in the social tagging system. We report on experiments with real data from the Bibsonomy social tagging system, where we compare our dynamic approach with a static one.