Strengthen nodal cooperation for data dissemination in mobile social networks

Data dissemination is a challenging issue in mobile social networks. It aims at increasing the overall delivery ratio and reducing the overall delivery delay. Most of the existing works assume all the users in a network are cooperative, i.e., the users are willing to carry the messages that they are not interested in while the nodes they meet maybe. In reality, the behaviors of each individual are naturally selfish, especially when the resources they have and they can access are limited. A data dissemination protocol cannot be pragmatic unless the selfishness is considered. This work proposes an incentive scheme to stimulate the users in a network to be more cooperative. Credits are the stimulus to encourage users to be more cooperative for data dissemination. We evaluate each node’s ability to fetch messages of a specific kind of interest and every single user can rent other nodes to help with obtaining the interested messages by paying credits. Extensive simulations on real traces are carried out to evaluate the proposed incentive scheme.     

基于用户行为的社交网络人格特质识别方法

社交网络已成为人们获取和发布信息的一个重要平台,也是黑客发起网络诈骗的主要场地。大多数黑客在发起网络诈骗之前,首先会判别目标用户的主要人格特点,然后根据主要人格特点制定与其接触的策略。因此,面向社交网络用户的人格特质识别方法的研究对提高用户识别社交网络诈骗能力具有重要意义。提出基于用户的人格特质识别方法。通过构建面向社交网络的人格特质词典提取用户发表或转发文本信息中能反映用户主要人格特质类型的观测值,采用5个具有不同参数值的隐半马尔可夫模型刻画用户在社交网络上发表或转发文本信息的行为过程。在人格特质识别阶段,通过计算每个用户在发表或转发文本信息过程中产生的观测序列相对于模型的平均对数似然概率,以识别用户所属的人格特质类型。在采集的新浪微博数据集上进行实验,结果表明,当假正率为10%时,该方法的总真正率为93.18%,能准确识别用户的人格特质类型。     

不同节点动态的复杂网络外部同步牵制控制

研究节点动态不同的两个复杂网络的外部同步问题。运用牵制控制方法,网络模型选取节点输出线性耦合模型,基于输出控制思想,设计结构简单的牵制控制器,对响应网络中的部分节点施加输出反馈控制,使得两个复杂动态网络达到外部同步,即实现响应网络与驱动网络的渐近同步。根据李雅普诺夫稳定性理论,推出相应的同步准则,得到控制器参数选择条件。仿真时,驱动网络和响应网络分别选取Lorenz系统和Lu系统,对全局耦合网络和最近邻耦合网络两个典型网络拓扑进行仿真,验证了所提方法的有效性。     

Discovering spammer communities in twitter

Online social networks have become immensely popular in recent years and have become the major sources for tracking the reverberation of events and news throughout the world. However, the diversity and popularity of online social networks attract malicious users to inject new forms of spam. Spamming is a malicious activity where a fake user spreads unsolicited messages in the form of bulk message, fraudulent review, malware/virus, hate speech, profanity, or advertising for marketing scam. In addition, it is found that spammers usually form a connected community of spam accounts and use them to spread spam to a large set of legitimate users. Consequently, it is highly desirable to detect such spammer communities existing in social networks. Even though a significant amount of work has been done in the field of detecting spam messages and accounts, not much research has been done in detecting spammer communities and hidden spam accounts. In this work, an unsupervised approach called SpamCom is proposed for detecting spammer communities in Twitter. We model the Twitter network as a multilayer social network and exploit the existence of overlapping community-based features of users represented in the form of Hypergraphs to identify spammers based on their structural behavior and URL characteristics. The use of community-based features, graph and URL characteristics of user accounts, and content similarity among users make our technique very robust and efficient.     

移动社会网络信息传播模型构建与top-k节点挖掘

在移动社会网络中挖掘出有影响力的top-k节点,对于移动运营商作出新产品或服务战略营销决策至关重要。针对移动社会网络的特点,提出一种充分考虑移动社会网络特点的信息传播模型以及基于该模型的top-k节点挖掘算法。实验证明,该方法能准确高效地定位移动社会网络中的活跃节点,这对于移动运营商作出营销决策起着至关重要的作用。     

Social network use and personality

Studies have shown a connection between the individual personality of the user and the way he or she behaves on line. Today many millions of people around the world are connected by being members of various Internet social networks. Ross et al. (2009) studied the connection between the personality of the individual users and their behavior on a social network. They based their study on the self-reports of users of Facebook, one of the most popular social networks, and measured five personality factors using the NEO-PI-R (Costa & McCrae, 1992) questionnaire. They found that while there was a connection between the personalities of surfers and their behavior on Facebook, it was not strong. This study is based on that of Ross et al. (2009), but in our study the self-reports of subjects, were replaced by more objective criteria, measurements of the user-information upload on Facebook. A strong connection was found between personality and Facebook behavior. Implications of the results are discussed.     

Effectively identifying the influential spreaders in large-scale social networks

With great theoretical and practical significance, the studies of information spreading on social media become one of the most exciting domains in many branches of sciences. How to control the spreading process is of particular interests, where the identification of the most influential nodes in larger-scale social networks is a crucial issue. Degree centrality is one of the simplest method which supposes that the node with more neighbours may be more influential. K-shell decomposition method partitions the networks into several shells based on the assumption that nodes in the same shell have similar influence and nodes in higher-level shells (e.g., central) are probably to infect more nodes. Degree centrality and k-shell decomposition are local methods which are efficient but less relevant. Global methods such as closeness and betweenness centralities are more exact but time-consuming. For effectively identifying the more influential spreaders in large-scale social networks, in this paper we proposed an algorithm framework to solve this dilemma by combining the local and global methods. All the nodes are graded by the local methods and then the periphery of the network is removed according to their central values. At last, the global methods are employed to find out which node is more influential. The experimental results show that our framework can be efficient and even more accurate than the global methods     

An Efficient Two-Phase Model for Computing Influential Nodes in Social Networks Using Social Actions

在社会网络的影响的测量在数据采矿社区收到了很多注意。影响最大化指发现尽量利用信息或产品采纳的有影响的用户的过程。在真实设置,在一个社会网络的一个用户的影响能被行动的集合建模(例如,份额,重新鸣叫,注释) 在其出版物以后由网络的另外的用户表现了。就我们的知识而言,在文学的所有建议模型同等地对待这些行动。然而,它是明显的一工具少些比一样的出版的份额影响的一份出版物相似。这建议每个行动有它影响的自己的水平(或重要性) 。在这份报纸,我们建议一个模型(叫的社会基于行动的影响最大化模型, SAIM ) 为在社会网络的影响最大化。在 SAIM,行动没在测量一个个人的影响力量同等地被考虑,并且它由二主要的步组成。在第一步,我们在社会网络计算每个个人的影响力量。这影响力量用 PageRank 从用户行动被计算。在这步的结束,我们得到每个节点被它的影响力量在标记的一个加权的社会网络。在 SAIM 的第二步,我们计算一个新概念说出 influence-BFS 树的使用的有影响的节点的一个最佳的集合。在大规模真实世界、合成的社会网络上进行的实验在计算揭示我们的模型 SAIM 的好表演,在可接受的时间规模,允许信息的最大的传播的有影响的节点的一个最小的集合。     

A high-performance algorithm for finding influential nodes in large-scale social networks

As one of the significant issues in social networks analysis, the influence maximization problem aims to fetch a minimal set of the most influential individuals in the network to maximize the number of influenced nodes under a diffusion model. Several approaches have been proposed to tackle this NP-hard problem. The traditional approaches failed to develop an efficient and effective solution due to the exponential growth of the size of social networks (due to massive computational overhead). In this paper, a three-stage framework based on the community detection approach is devised, namely LGFIM. In the first stage, the search space was controlled by partitioning the network into communities. Simultaneously, three heuristic methods were presented for modifying the community detection algorithm to extract the optimal communities: core nodes selection, capacity constraint on communities, and communities combination. These extracted communities were highly compatible with the information propagation mechanism. The next stages apply a scalable and robust algorithm at two different levels of the network: 1. Exploring the local scope of communities to select the most influential nodes of each community and construct the potential influential nodes set 2. Exploring the global scope of the network to select the target influential nodes among potential influential nodes set. Experimental results on various real datasets proved that LGFIM could achieve remarkable results compared with the state-of-the-art algorithms, especially acceptable influence spread, much better running time, and more applicable to massive social networks.

     

Efficient techniques for time-constrained information dissemination using location-based social networks

Social networks have undergone an explosive growth in recent years. They constitute a central part of users׳ everyday lives as they are used as major tools for the spread of information, ideas and notifications among the members of the network. In this work we investigate the use of location-based social networks as a medium of emergency notification, for efficient dissemination of emergency information among members of the social network under time constraints. Our objective is the following: given a location-based social network comprising a number of mobile users, the social relationships among the users, the set of recipients, and the corresponding timeliness requirements, our goal is to select an appropriate subset of users so that the spread of information is maximized, time constraints are satisfied and costs are considered. We propose LATITuDE, our system that investigates the interactions among the members of the social network to infer their social relationships, and develop scalable dissemination mechanisms that select the most efficient set of users to initiate the dissemination process in order to maximize the information reach among the appropriate receivers within a time window. Our detailed experimental results illustrate that our approach is practical, effectively addresses the problem of informing the appropriate set of users within a deadline when an emergency event occurs, uses a small number of messages, and consistently outperforms its competitors.     

Personality and social characteristics of Facebook non-users and frequent users

Facebook is currently the largest social networking website with an estimated one billion of monthly active users in 2012. While most of the prior research has explored characteristics of Facebook users, less is known about the characteristics of individuals who do not use Facebook. The current study examined personality and social factors that might influence the decision to use Facebook and explored differences between Facebook non-users and frequent users. Online questionnaires examining levels of trust and self-disclosure, number of intimate friendships, peer usage of Facebook and scores on overt and covert narcissism were used for the purpose of the study. The results showed that non-users and frequent users differed on several social and personality characteristics. Facebook non-users had lower tendency to self-disclose, fewer peers participating in the social network and higher covert narcissistic traits. Frequent Facebook users scored higher on overt narcissism and reported more intimate friendships than non-users, indicating that close friendships might actually extend to social networks and contribute to a feeling of closeness and intimacy between friends in both an online and offline context.     

AntP2PR: An ant intelligence inspired routing scheme for Peer-to-Peer networks

Unstructured Peer-to-Peer networks consist of an infrastructure-less overlay on top of another network. Most of them use distributed algorithms for all operations, such as resource discovery or connectivity control. Research has shown that a considerable amount of the generated traffic is due to signaling messages. Furthermore, another challenge when implementing a Peer-to-Peer network is avoiding free riders, i.e. users trying to profit from the network without sharing their resources. In this paper a new approach to routing packets in such networks is presented using ant intelligence. Success messages are used as agents and the biological procedure of pheromone trails is used for forwarding new packets used in resource discovery. These agents carry an amount of pheromone which will be added to a pheromone table representing routes to other peers. This approach enables the network to adjust to the dynamic nature of Peer-to-Peer networks where new nodes connect and disconnect continuously. Peers that are free riding will be ultimately isolated from the rest of the network by limiting the number of messages directed to them. The authors have simulated an unstructured Peer-to-Peer network, such as Gnutella, that uses this method and the results are very promising. The amount of traffic used solely for resource discovery is greatly reduced enabling the users to use more bandwidth for transferring content.     

Identifying influential nodes in weighted complex networks using an improved WVoteRank approach

Influence maximization is an important research problem in the field of network science because of its business value. It requires the strategic selection of seed nodes called “influential nodes,” such that information originating from these nodes can reach numerous nodes in the network. Many real-world networks, such as transportation, communication, and social networks, are weighted networks. Influence maximization in a weighted network is more challenging compared to that in an unweighted network. Many methods, such as weighted degree rank, weighted h-index, weighted betweenness, and weighted VoteRank techniques, have been used to order the nodes based on their spreading capabilities in weighted networks. The VoteRank method is a popular method for finding influential nodes in an unweighted network using the idea of a voting scheme. Recently, the WVoteRank method was proposed to find the seed nodes; it extends the idea of the VoteRank method by considering the edge weights. This method considers only 1-hop neighbors to calculate the voting score of every node. In this study, we propose an improved WVoteRank method based on an extended neighborhood concept, which takes the 1-hop neighbors as well as 2-hop neighbors into account for the voting process to decide influential nodes in a weighted network. We also extend our proposed approach to unweighted networks. We compare the performance of the proposed improved WVoteRank method against the popular centrality measures, weighted degree, weighted closeness, weighted betweenness, weighted h-index, and weighted VoteRank on several real-life and synthetic datasets of diverse sizes and properties. We utilize the widely used stochastic susceptible–infected–recovered information diffusion model to calculate the infection scale, the final infected scale as a function of time, and the average distance between spreaders. The simulation results reveal that the proposed method, improved WVoteRank, considerably outperforms the other methods described above, including the recent WVoteRank.

     

基于簇中心预测的Ad Hoc网络位置更新算法

本文根据许多实际应用场合下网络节点所表现出来的群组性移动特征,提出了一种基于簇中心预测的位置更新算法.它通过计算和估计簇中心的移动特性,并在此基础上预测各个节点的位置.只有当预测位置与实际位置的偏差超过一定范围时,才产生新的位置更新消息,由此可以极大地减少所需传送的位置更新消息.仿真结果表明,该算法下的位置消息大大少于常规的基于距离的算法.     

Maximizing influence under influence loss constraint in social networks

Influence maximization is a fundamental research problem in social networks. Viral marketing, one of its applications, aims to select a small set of users to adopt a product, so that the word-of-mouth effect can subsequently trigger a large cascade of further adoption in social networks. The problem of influence maximization is to select a set of K nodes from a social network so that the spread of influence is maximized over the network. Previous research on mining top-K influential nodes assumes that all of the selected K nodes can propagate the influence as expected. However, some of the selected nodes may not function well in practice, which leads to influence loss of top-K nodes. In this paper, we study an alternative influence maximization problem which is naturally motivated by the reliability constraint of nodes in social networks. We aim to find top-K influential nodes given a threshold of influence loss due to the failure of a subset of R(<K) nodes. To solve the new type of influence maximization problem, we propose an approach based on constrained simulated annealing and further improve its performance through efficiently estimating the influence loss. We provide experimental results over multiple real-world social networks in support. This research will further support practical applications of social networks in various domains particularly where reliability would be a main concern in a system deployment.     

Ranking influential nodes in complex networks based on local and global structures

Applied Intelligence - Identifying influential nodes in complex networks is an open and challenging issue. Many measures have been proposed to evaluate the influence of nodes and improve the...     

基于社区划分的影响力最大化算法

影响力最大化问题是社会网络中的重要研究方向,其主要目的是获取社会网络中最有影响力的用户使通过这些用户获得影响传播范围的最大化。随着大数据时代的来临,传统的贪心算法因为复杂度高而不能有效解决大规模社会网络下影响力最大化的时间问题。提出一种基于社区划分的影响力最大化算法,利用影响概率将大规模社会网络分成较小的社区模块,并考虑社区边界节点之间的联系,从而最大程度缩小因社区划分造成的社区间的孤立。为进一步提高算法效率,在每个社区中以影响路径作为影响评估单元,同时对每个社区并行处理以便更高效地获取有影响力的节点。通过仿真实验验证了算法的可行性和高效性,其可以较好地适应大规模社会网络环境。     

A location-aware multicasting protocol for Bluetooth Location Networks

Bluetooth Location Network (BLN) is a Bluetooth radio network that is composed of some mobile Bluetooth devices and static Bluetooth units, and is established at the system initialization to form a spontaneous network topology. In a BLN, a multicast service is defined as the periodical delivering of messages from a Service Server to a set of mobile devices which are the multicast members predefined by the Service Server. Several multicast protocols have been proposed for the Ad-Hoc networks, but they create an inefficient multicast tree for the BLN due to the existing differences in the radio characteristics between Ad-Hoc and Bluetooth radio networks. The present paper analyzes these differences and proposes a novel multicasting protocol for constructing an efficient multicast tree in a BLN. The proposed protocol constructs a multicast tree with good features which include the shortest path, a higher degree of path sharing, and fewer forwarding nodes. Simulation results reveal that the proposed multicast protocol outperforms the existing multicast protocols in the BLN.     

Models of Influence in Online Social Networks

Online social networks gained their popularity from relationships users can build with each other. These social ties play an important role in asserting users' behaviors in a social network. For example, a user might purchase a product that his friend recently bought. Such phenomenon is called social influence, which is used to study users' behavior when the action of one user can affect the behavior of his neighbors in a social network. Social influence is increasingly investigated nowadays as it can help spreading messages widely, particularly in the context of marketing, to rapidly promote products and services based on social friends' behavior in the network. This wide interest in social influence raises the need to develop models to evaluate the rate of social influence. In this paper, we discuss metrics used to measure influence probabilities. Then, we reveal means to maximize social influence by identifying and using the most influential users in a social network. Along with these contributions, we also survey existing social influence models, and classify them into an original categorization framework. Then, based on our proposed metrics, we show the results of an experimental evaluation to compare the influence power of some of the surveyed salient models used to maximize social influence.