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Yahoo! Answers is an online platform where users can post questions and answer other users’ questions. Our previous work studied the online social network (OSN) of Yahoo! Answers by analyzing information from the profiles (including fans, contacts, and interests) of top contributors and their related users. Rather than using the static profile information from the top-contributor-centered dataset, in this paper, we particularly analyze the actual questioning and answering (Q/A) behaviors of normal users. We build a Q/A network that unidirectionally connects each asker to his/her answerers. We analyze the structural characteristics of the Q/A network, user Q/A activities, and knowledge base of all users. In addition to the observations similar to our previous study, which indicates that the OSN of Yahoo! Answers can reflect user Q/A activities to a certain extent, we additionally observe that: 1) a large portion of users only ask questions without answering others’ questions; 2) users are active in more knowledge categories than those indicated in their profiles; and 3) the knowledge categories of the top-contributor-related users cannot represent those of normal users. Finally, we analyze the characteristics of questions and answers in different knowledge categories. This paper not only provides an understanding of actual Q/A activities of users but also showcases the aspects of Q/A activities that the OSN of Yahoo! Answers can and cannot accurately reflect. Based on the insights gained from this paper, we propose a few methods to help improve the quality of service of Yahoo! Answers. [ABSTRACT FROM PUBLISHER]

Traditional web-based Question and Answer (Q&A) websites cannot easily solve non-factual questions to match askers’ preference. Recent research efforts begin to study social-based Q&A systems that rely on an asker's social friends to provide answers. However, this method cannot find answerers for a question not belonging to the asker's interests. To solve this problem, we propose a distributed Q&A system incorporating both social community intelligence and global collective intelligence, named as iASK. iASK improves the response latency and answer quality in both the social domain and global domain. It uses a neural network based friend ranking method to identify answerer candidates by considering social closeness and Q&A activities. To efficiently identify answerers in the global user base, iASK builds a virtual server tree that embeds the hierarchical structure of interests, and also maps users to the tree based on user interests. To accurately locate the cooperative experts, iASK has a fine-grained reputation system to evaluate user reputation based on their cooperativeness and expertise, and uses a reputation based reward strategy to encourage users to be cooperative. To further improve the performance of iASK, we propose a weak tie assisted social based potential answerer location algorithm and an interest coefficient based uncategorized question forwarding algorithm. To further improve the response quality and cooperativeness, we propose a reputation based reward strategy that motivates users to answer questions from unknown users. Experimental results from large-scale trace-driven simulation and real-world daily usages of the iASK prototype show the superior performance of iASK. It achieves high answer quality with 24 percent higher accuracy, short response latency with 53 percent less delay and effective cooperative incentives with 16 percent more answers compared to other social-based Q&A systems. The results also show the effectiveness of the enhancement algorithms in improving the performance of iASK. [ABSTRACT FROM AUTHOR]

In mobile opportunistic social networks (MOSNs), mobile devices carried by people communicate with each other directly when they meet for proximity-based MOSN services (e.g., file sharing) without the support of infrastructures. In current methods, when nodes meet, they simply communicate with their real IDs, which leads to privacy and security concerns. Anonymizing real IDs among neighbor nodes solves such concerns. However, this prevents nodes from collecting real ID-based encountering information, which is needed to support MOSN services. Therefore, in this paper, we propose FaceChange that can support both anonymizing real IDs among neighbor nodes and collecting real ID-based encountering information. For node anonymity, two encountering nodes communicate anonymously. Only when the two nodes disconnect with each other, each node forwards an encrypted encountering evidence to the encountered node to enable encountering information collection. A set of novel schemes are designed to ensure the confidentiality and uniqueness of encountering evidences. FaceChange also supports fine-grained control over what information is shared with the encountered node based on attribute similarity (i.e., trust), which is calculated without disclosing attributes. Advanced extensions for sharing real IDs between mutually trusted nodes and more efficient encountering evidence collection are also proposed. Extensive analysis and experiments show the effectiveness of FaceChange on protecting node privacy and meanwhile supporting the encountering information collection in MOSNs. Implementation on smartphones also demonstrates its energy efficiency. [ABSTRACT FROM AUTHOR]

Node searching in delay tolerant networks is of great importance for different applications, in which a locator node finds a target node in person. In the previous distributed node searching method, a locator traces the target along its movement path from its most frequently visited location. For this purpose, nodes leave traces during their movements and also store their long-term movement patterns in their frequently visited locations (i.e., preferred locations). However, such tracing leads to a long delay and high overhead on the locator by long-distance moving. Our trace data study confirms these problems and provides the foundation of our design of a new node searching method, called target-oriented method (TSearch). By leveraging social network properties, TSearch aims to enable a locator to directly move toward the target. Nodes create encounter records (ERs) indicating the locations and times of their encounters and make the ERs easily accessible by locators through message exchanges or a hierarchical structure. In node searching, a locator follows the target’s latest ER, the latest ERs of its friends (i.e., frequently meeting nodes), its preferred locations, and the target’s possible locations deduced from additional information for node searching. Extensive trace-driven and real-world experiments show that TSearch achieves significantly higher success rate and lower delay in node searching compared with previous methods. [ABSTRACT FROM PUBLISHER]

Real-world applications, such as peer-to-peer (P2P) networks, e-commerce and social networks, usually employ reputation systems to provide guidance in selecting trustworthy node for high system reliability and security. A reputation system computes and publishes reputation score for each node based on a collection of opinions from others about the node. However, collusion behaviors impair the effectiveness of reputation systems in trustworthy node selection. Though many reputation calculation methods have been proposed to mitigate collusion's influence, little effort has been devoted to specifically tackling collusion. Based on the important collusion behavior characteristics in reputation evaluation and influence on reputation values, we propose a basic collusion detection method to specifically detect suspicious collusion behaviors in pairs. We further optimize the method by reducing the computing overhead. We also propose two pre-processing methods to firstly identify partial reputation raters of a node that are more likely to be colluders before applying the collusion detection method on them, thus reducing the collusion detection overhead. Extensive experimental results show that our proposed methods can significantly enhance the capability of existing reputation systems to detect collusion with low overhead. Also, the pre-processing methods are effective in reducing the collusion detection overhead without affecting the collusion detection accuracy. [ABSTRACT FROM AUTHOR]

Distributed file searching in delay tolerant networks formed by mobile devices can potentially support various useful applications. In such networks, nodes often present certain social network properties of their holders in terms of contents (i.e., interests) and contacts. However, current methods in DTNs only consider either content or contact for file searching or dissemination, which limits the file sharing efficiency. In this paper, we first analyze real traces to confirm the importance and necessity of considering both content and contact in file search. We then propose Cont2, a social-aware file search method that exploits both node contents and contact patterns. First, considering people with common interests tend to share files and gather together, Cont2 virtually groups common-interest nodes into a community to direct file search. Second, considering human mobility follows a certain pattern, Cont2 exploits nodes' contact frequencies with a community to expedite file searching. To further improve the searching efficiency, Cont2 also integrates sub-communities and parallel forwarding as optional components for file searching. Trace-driven experiments on the GENI testbed and NS-2 simulator show that Cont2 can effectively improve the search efficiency compared to current methods. [ABSTRACT FROM AUTHOR]

Current reputation systems for peer-to-peer (P2P) file sharing networks suffer from high overhead on reputation querying. Also, purely relying on a threshold to detect malicious nodes may make a high-reputed node be reluctant to further increase its reputation in these reputation systems. On the other side, the social network concept of ”friendship foster cooperation” can be utilized to alleviate the high overhead in reputation systems. However, the limited number of friends limits the availability of file resources in these approaches. To overcome the drawbacks, we propose a social network based reputation system, namely SocialTrust, that synergistically leverages the social network connections and traditional credit based reputation system to provide efficient reputation management for P2P file sharing. In SocialTrust, each node favors friends for service transactions, which are resulted from both real life acquaintances and online partnerships established between high-reputed and frequently-interacted nodes. When no friends are available for a request, a node chooses the server with the highest reputation. The benefits of friendship and partnership on file sharing and cost saving encourage nodes to be continuously cooperative. Further, SocialTrust considers the number of friends/partners and the reputation of a node in reputation rewarding/punishment in order to realize accurate reputation evaluation. SocialTrust can also prevent certain attacks such as deny of service and collusion. Extensive trace-driven simulation demonstrates the effectiveness of SocialTrust. [ABSTRACT FROM AUTHOR]

In current peer-to-peer (P2P) live streaming systems, nodes in a channel form a P2P overlay for video sharing. To watch a new channel, a node depends on the centralized server to join in the overlay of the channel. In today's live streaming applications, the increase in the number of channels triggers users' desire of watching multiple channels successively or simultaneously. However, the support of such watching modes in current applications is no better than joining in different channel overlays successively or simultaneously through the centralized server, which if widely used, poses a heavy burden on the server. In order to achieve higher efficiency and scalability, we propose a Social-network-Aided efficient liVe strEaming system (SAVE). SAVE regards users' channel switching or multichannel watching as interactions between channels. By collecting the information of channel interactions, nodes' interests, and watching times, SAVE forms nodes in multiple channels with frequent interactions into an overlay, constructs bridges between overlays of channels with less frequent interactions, and enables nodes to identify friends sharing similar interests and watching times. Thus, a node can connect to a new channel while staying in its current overlay, using bridges or relying on its friends, reducing the need to contact the centralized server. We further propose the channel-closeness-based chunk-pushing strategy and capacity-based chunk provider selection strategy to enhance the system performance. Extensive experimental results from the PeerSim simulator and PlanetLab verify that SAVE outperforms other systems in system efficiency and server load reduction, as well as the effectiveness of the two proposed strategies. [ABSTRACT FROM PUBLISHER]

Advances in ubiquitous sensing, computing and wireless communication technologies are leading to the development of cyber-physical systems (CPS), which promise to revolutionize the way we interact with the physical world. CPS applications, such as healthcare monitoring, may involve many users and objects scattered over a wide area. One critical function of CPS is object search in the physical world through the cyber sphere that enables interaction between the cyber and physical spheres. Some of the previously proposed physical object search engines use RFID tracking, and others collect the information of object locations into a hierarchical centralized server. The difficulty of widely deploying RFID devices, the centralized search, and the need for periodical location information collection prevent CPS from achieving higher scalability and efficiency. To deal with this problem, we propose a Social-aware distributed Cyber-Physical human-centric Search engine (SCPS) that leverages the social network formed by wireless device users for object search. Without requiring periodical location information collection, SCPS locates objects held by users based on the routine user movement pattern. Moreover, using a social-aware Bayesian network, it can accurately predict the users’ locations even at the occurrence of exceptional (i.e., non-routine) events (e.g., raining) that break user movement pattern. Thus, SCPS is more advantageous than all previous social network based works which assume that user behaviors always follow a certain pattern. Further, SCPS conducts the search in a fully distributed manner by relying on a distributed hash table (DHT) structure. As a result, SCPS achieves high scalability, efficiency and location accuracy. Extensive real-trace driven simulation results show the superior performance of SCPS compared to other representative search methods including a hierarchical centralized method, a decentralized method, and two social network based methods. The results also show the effectiveness of different components of SCPS. [ABSTRACT FROM AUTHOR]

The explosive growth of unsolicited e-mails has prompted the development of numerous spam filter techniques. Bayesian spam filters are superior to static keyword-based spam filters in that they can continuously evolve to tackle new spam by learning keywords in new spam emails. However, Bayesian spam filters are easily poisoned by clever spammers who avoid spam keywords and add many innocuous words in their emails. Also, Bayesian spam filters need a significant amount of time to adapt to a new spam based on user feedback. Moreover, few current spam filters exploit social networks to assist in spam detection. In order to develop an accurate and user-friendly spam filter, we propose a SOcial network Aided Personalized and effective spam filter (SOAP) in this paper. In SOAP, each node connects to its social friends; i.e., nodes form a distributed overlay by directly using social network links as overlay links. Each node uses SOAP to collect information and check spam autonomously in a distributed manner. Unlike previous spam filters that focus on parsing keywords (e.g., Bayesian filters) or building blacklists, SOAP exploits the social relationships among email correspondents and their (dis)interests to detect spam adaptively and automatically. In each node, SOAP integrates four components into the basic Bayesian filter: social closeness-based spam filtering, social interest-based spam filtering, adaptive trust management, and friend notification. We have evaluated the performance of SOAP using simulation based on trace data from Facebook. We also have implemented a SOAP prototype for real-world experiments. Experimental results show that SOAP can greatly improve the performance of Bayesian spam filters in terms of accuracy, attack-resilience, and efficiency of spam detection. The performance of the Bayesian spam filter is SOAP’s lower bound. [ABSTRACT FROM AUTHOR]

Previous delay-tolerant network (DTN) routing algorithms exploit either past encounter records or social network properties to derive a node's probability of delivering packets to their destinations. However, they only have a local view of the network, which limits the routing efficiency. Also, when two nodes meet, they have to exchange the delivery abilities to the destinations of all packets in the two nodes, which incurs high resource consumption. In this paper, we propose SMART, which utilizes a distributed social map for lightweight routing in delay-tolerant networks. In SMART, each node builds its own social map consisting of nodes it has met and their frequently encountered nodes in a distributed manner. Based on both encountering frequency and social closeness of the two linked nodes in the social map, we decide the weight of each link to reflect the packet delivery ability between the two nodes. The social map enables more accurate forwarder selection through a broader view. Moreover, nodes exchange much less information for social map update, which reduces resource consumption. Trace-driven experiments and tests on the GENI ORBIT testbed demonstrate the high efficiency of SMART in comparison to previous algorithms. [ABSTRACT FROM PUBLISHER]

Current peer-to-peer (P2P) file sharing methods in mobile ad hoc networks (MANETs) can be classified into three groups: flooding-based, advertisement-based, and social contact-based. The first two groups of methods can easily have high overhead and low scalability. They are mainly developed for connected MANETs, in which end-to-end connectivity among nodes is ensured. The third group of methods adapts to the opportunistic nature of disconnected MANETs but fails to consider the social interests (i.e., contents) of mobile nodes, which can be exploited to improve the file searching efficiency. In this paper, we propose a P2P content-based file sharing system, namely SPOON, for disconnected MANETs. The system uses an interest extraction algorithm to derive a node's interests from its files for content-based file searching. For efficient file searching, SPOON groups common-interest nodes that frequently meet with each other as communities. It takes advantage of node mobility by designating stable nodes, which have the most frequent contact with community members, as community coordinators for intracommunity searching, and highly mobile nodes that visit other communities frequently as community ambassadors for intercommunity searching. An interest-oriented file searching scheme is proposed for high file searching efficiency. Additional strategies for file prefetching, querying-completion, and loop-prevention, and node churn consideration are discussed to further enhance the file searching efficiency. We first tested our system on the GENI Orbit testbed with a real trace and then conducted event-driven experiment with two real traces and NS2 simulation with simulated disconnected and connected MANET scenarios. The test results show that our system significantly lowers transmission cost and improves file searching success rate compared to current methods. [ABSTRACT FROM PUBLISHER]

In peer-to-peer networks (P2Ps), many autonomous peers without preexisting trust relationships share resources with each other. Due to their open environment, the P2Ps usually employ reputation systems to provide guidance in selecting trustworthy resource providers for high reliability and security; however, node collusion impairs the effectiveness of reputation systems in trustworthy node selection. Although some reputation systems have certain mechanisms to counter collusion, the effectiveness of the mechanisms is not sufficiently high. In this paper, we leverage social networks to enhance the capability of reputation systems in combating collusion. We first analyzed real trace of the reputation system in the Overstock online auction platform, which incorporates a social network. The analysis reveals the impact of the social network on user purchasing and reputation rating patterns. We, thus, identified suspicious collusion behavior patterns and propose a social network-based mechanism, SocialTrust, to counter collusion. SocialTrust adaptively adjusts the weight of ratings based on the social distance and interest relationship between peers. Experiment results show that SocialTrust can significantly strengthen the capability of current reputation systems in combating collusion. [ABSTRACT FROM AUTHOR]