Efficient Multi-Pair Active Friending in Online Social Networks

The functional form of acceptance probabilities in Monte Carlo algorithms bears a resemblance to the distance functions which are specifically defined to be bracketed by the unit interval. This observation led us to seek the average distance between any two points on the unit interval and this by analogy resulted in a suggestion of an upper and a lower bound of 1/2 and 1/3, respectively, for the acceptance ratio or the average acceptance probability in Monte Carlo computer simulations.

Many business applications rely on their historical data to predict their business future.The marketing products process is one of the core processes for the business.Customer needs give a useful piece of information that helps to market the appropriate products at the appropriate time.Moreover, services are considered recently as products.The development of education and health services is depending on historical data.For the more, reducing online social media networks problems and crimes need a significant source of information.Data analysts need to use an efficient classification algorithm to predict the future of such businesses.However, dealing with a huge quantity of data requires great time to process.Data mining involves many useful techniques that are used to predict statistical data in a variety of business applications.The classification technique is one of the most widely used with a variety of algorithms.In this paper, various classification algorithms are revised in terms of accuracy in different areas of data mining applications.A comprehensive analysis is made after delegated reading of 20 papers in the literature.This paper aims to help data analysts to choose the most suitable classification algorithm for different business applications including business in general, online social media networks, agriculture, health, and education.Results show FFBPN is the most accurate algorithm in the business domain.The Random Forest algorithm is the most accurate in classifying online social networks (OSN) activities.Naïve Bayes algorithm is the most accurate to classify agriculture datasets.OneR is the most accurate algorithm to classify instances within the health domain.The C4.5 Decision Tree algorithm is the most accurate to classify students' records to predict degree completion time.

Being rational or aggressive? A revisit to Dunbar׳s number in online social networks

Event stream dissemination dominates the workloads in large-scale Online Social Network (OSN) systems. Based on the de facto per-user view data storage, event stream dissemination raises a large amount of inter-server traffics due to the complex interconnection among OSN users. The state-of-the-art schemes mainly explore the structure features of social graphs to reduce the inter-server messages for event stream dissemination. Different sub-graph structures are exploited for achieving the approximated optimal assignment. However, such schemes incur high costs of computation or communication. In this work, we follow a different design philosophy by using a game theoretic approach, which decomposes the high complex graph computation problem into individuals' rational strategy selection of each node. Specifically, we propose a novel social piggyback game to achieve a more efficient solution. We mathematically prove the existing of the Nash Equilibrium of the social piggyback game. Moreover, we propose an efficient best response dynamic algorithm to achieve the Nash Equilibrium, which quickly converges in a small number of iterations for large-scale OSNs. We further show that the communication cost of this design achieves a 1.5-approximation of the theoretical social optimal. We conduct comprehensive experiments to evaluate the performance of this design using large-scale real-world traces from popular OSN systems. Results show that the social piggyback game achieves a significant 302× improvement in system efficiency compared to existing schemes.

In recent years, the evolution of online social networks has become an important research topic in online social network analysis. An important approach to this problem is to detect community evolution events so as to understand the evolution of the whole network. Considering the huge amount of data in large social networks, an efficient and scalable community evolution detection algorithm is necessary. In this paper, we focus on community evolution events detection in dynamic social networks. First, we divide the Facebook and DBLP data set into a series of snapshots and apply the Louvain algorithm to find the communities in each snapshot. Then, we propose a light weight evolution events detection algorithm to find the community evolution patterns between adjacent snapshots, which statistically show the evolution trend of the entire network. Simulation results show that our algorithm can effectively detect the community evolution events in online social networks.

NASA researchers, working under the Aviation System Capacity Program and in conjunction with the FAA Free Flight Program Office, have developed a set of decision support tools to assist terminal area air traffic controllers with control of arrival and departure traffic. Two of these tools, the Final Approach Spacing Tool (FAST) and the Expedite Departure Path (EDP) tool, provide air traffic controllers with heading, speed and altitude advisories to assist in spacing aircraft. This paper describes the conflict prediction and resolution algorithm shared by aFAST and EDP to produce conflict-free aircraft trajectories with realistic conflict resolution maneuvers. The process is accomplished in three stages: prediction, classification and resolution. A conflict prediction scheme is documented which incorporates all applicable FAA separation requirements, including automatic detection of separation during transition from staggered to simultaneous parallel approach operations. A method of classifying predicted conflicts with a limited set of criteria is detailed. Finally, a knowledge-based conflict resolution process is presented which allows for resolution of predicted conflicts in a manner consistent with controller practice: including prioritization of resolution tactics and mixture of multiple degrees of freedom to achieve separation. The scheme has been employed in both closed-loop simulations to determine solution stability and controller-in-the-loop simulations to begin development of the resolution tactics knowledge base.

In large-scale Online Social Network (OSN) systems, event stream dissemination incurs costly inter-server communication due to the per-user view data storage. To solve the problem, existing schemes commonly leverage the social graph structures to save redundant inter- server traffics across social links. The state-of-the- art social piggyback scheme reduces the inter-server traffics by fully exploiting the proposed hub- structure, based on the observation of high cluster coefficient in OSNs. In order to find the best hub- structure, however, such a scheme needs to identify the global densest sub-graph by iteratively removing the node with the minimum weighted degree. Such a process causes a worst computation cost of O(n²), making the social piggyback scheme unscalable for real-world large-scale OSN graphs. In this work, we propose a novel scheme by exploiting the social community for event stream dissemination. We first detect the social communities in a social graph by using an efficient community detection algorithm based on distance dynamics. For each community, we then design a heuristics algorithm to fully leverage the hub- structure. The heuristics algorithm explores the hub- structure center on the node with maximum degree in each iteration. We collect large-scale datasets from DBLP and Facebook and conduct comprehensive experiments to evaluate our design. The results show that our design significantly reduces the communication overhead and computing time by 40.71% and 81.21% compared to existing schemes, respectively.

Online social networks (OSNs) have witnessed their prosperous developments in recent years. Based on the established relations among individuals in OSNs, ideas and opinions can be spread via a word-of-mouth effect. To exploit this effect, many companies have taken OSNs as the major advertising channels to promote their products, which has motivated substantial research on viral marketing. Among them, influence maximization, seed minimization, and profit maximization have been extensively studied in the past decades. These three problems are mainly studied in the non-adaptive setting in the literature, i.e., seed user selection phase does not involve any observations on influence propagation in reality. In this thesis, we extend profit maximization into a generalized version as target profit maximization and investigate these three problems in the adaptive setting, formulated as adaptive influence maximization, adaptive seed minimization, and adaptive target profit maximization respectively. Firstly, we study the adaptive influence maximization (IM) problem. Given a social network G and an integer k, the influence maximization problem asks for a seed set S of k nodes from G to maximize the expected number of nodes activated via an influence propagation. In the adaptive setting, the k seed nodes are selected in batches of equal size b, such that the i-th batch is identified after the actual influence results of the former i-1 batches are observed. We propose the first practical algorithm for the adaptive IM problem that could provide the worst-case approximation guarantee of 1-e^α(ε-1) with high probability, where ε∊(0, 1) is a user-specified parameter and α= 1-(1-1/b)^b is set by the batch size b. Our approach is based on a novel general framework AdaptGreedy which could be instantiated by any existing non-adaptive IM algorithms with expected approximation guarantee. In this regard, we design a novel non-adaptive IM algorithm called EPIC which not only provides strong expected approximation guarantee, but also presents superior performance compared with the existing IM algorithms. Meanwhile, we clarify some existing misunderstandings in recent work and shed light on further study of the adaptive IM problem. Subsequently, we consider the adaptive seed minimization (SM) problem. As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number η of users in a given social network G. In the adaptive setting, the seed nodes are selected in several batches, such that the choice of current batch may exploit information about the actual influence of the previous batches. We devise a novel algorithm, ASTI, which addresses the adaptive seed minimization problem in O (η(m+n)/ε^2 ln n) expected time and offers an approximation guarantee of (ln η+1)^2/((1-(1-1/b)^b)(1-1/e)(1-ε)) in expectation, where η is the targeted number of influenced nodes, b is size of each seed node batch, and ε∊(0, 1) is a user-specified parameter. To the best of our knowledge, ASTI is the first algorithm that provides such an approximation guarantee without incurring prohibitive computation overhead. Eventually, we explore the adaptive target profit maximization (TPM) problem. Given a social network G, the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed selection. The TPM problem, which generalizes the PM problem, aims to select a subset of seed nodes from a given target user set T to maximize the profit. In the adaptive setting, the seed users are selected through multiple batches, such that the selection of current batch exploits the knowledge of actual influence in the previous batches. To acquire an overall understanding, we study adaptive TPM under the oracle model and the noise model, to which we propose ADG and AddATP algorithms with strong theoretical guarantees respectively. In addition, we propose the idea of hybrid error to better handle the sampling errors under the noise model and design a novel algorithm HATP that boosts the efficiency of AddATP significantly.

An airborne conflict resolution approach is presented that is capable of providing flight plans forecast to be conflict-free with both area and traffic hazards. This approach is capable of meeting constraints on the flight plan such as required times of arrival (RTA) at a fix. The conflict resolution algorithm is based upon a genetic algorithm, and can thus seek conflict-free flight plans meeting broader flight planning objectives such as minimum time, fuel or total cost. The method has been applied to conflicts occurring 6 to 25 minutes in the future in climb, cruise and descent phases of flight. The conflict resolution approach separates the detection, trajectory generation and flight rules function from the resolution algorithm. The method is capable of supporting pilot-constructed resolutions, cooperative and non-cooperative maneuvers, and also providing conflict resolution on trajectories forecast by an onboard FMC.

Stochastic optimal control for aircraft conflict resolution under wind uncertainty

Building on the authors' prior studies that investigate uses and perceptions of online social networks, this study critically explores the emerging social networking culture. In doing so, the research seeks to identify possible constructs that can be used to predict social networking behavior that may then be tested in a future study. The study relies on multiple user perspectives, drawing its participants from international students at two universities, one in Australia and one in the United States. Throughout this process, the utility of using the lens of national culture versus using other lenses is also examined. While the qualitative data suggests somewhat divergent approaches to social networking in different countries, a number of common themes were also identified. Two themes which appeared across national boundaries were changes in use over time and privacy and trust.

Influence maximization is one of the most fundamental problems in social network analysis due to its significant impact on viral marketing and targeted advertisements. Considerable amount of works has extensively studied to analyze the influence in social networks, but most of the existing works unfortunately neglected the fact that the location information in the cyber-physical world could also play an important role in the influence prorogation. Furthermore, even though a few works consider a little location information to enhance the influence maximization, they do not discuss any privacy issue of the model and expose user's location information directly to the public. This paper considers the problem of influence maximization in both GPS-enabled cyber-physical and online social networks with privacy reservation. We propose one novel model merging both GPS data of cyber-physical network and relationship data of online social network together in a unified framework, then we provide an efficient algorithm to solve the influence maximization problem in the framework. Besides the influence maximization problem, our framework could also support other applications involving both cyber-physical and online social networks. Further more, to protect the sensitive location and link information, we also provide corresponding techniques to protect the privacy during the whole influence maximization process. Empirical studies of real life datasets demonstrate the power of engaging location information to influence maximization, and suggest that our resolution outperforming most existing alternative algorithms with the protection of location privacy.

Topic discussion is a significant phenomenon on the online social network, which would attract the rising attention in the near future. In this paper, we predict the tendency of topic discussion on the online social networks using a dynamic probability model. We analyze the process of topic discussion, and give the formulation of it. Three main factors (individual interest, group behavior, and time lapse) are analyzed and quantized, based on which, we propose a dynamic probability model to predict the user's behavior, i.e. attending the topic discussion or not, and then obtain the number of the attending users. Most of the parameters of the model can be calculated by ML estimate methods, and the rest 3 parameters are set by human experience. Experiment shows that our model could predict the tendency of topic discussion accurately. Also, we simulate different sets of the three experience parameters and study the selection of suitable experience parameters.

We use a large quota-sampled online survey and data on Facebook connections among survey respondents in six successor states of former Yugoslavia to demonstrate that, even more than two decades after the violence had ended, online social connections in this region are substantially related to people’s war experiences of combat, victimhood, and forced migration, as well as to their views of the wars’ causes, conduct, and consequences. What is particularly important, the sizes of the effects of these war-related factors on respondents’ online social networks are substantively large and comparable to those of gender, ethnicity, education, or political ideology. Our findings are an important contribution to the understanding of the deeply pervasive and long-lasting effects of wars on societies. They also highlight the enduring relevance of wartime violence in postwar social networks that is likely to affect efforts at enduring conflict resolution and reconciliation.

ABSTRACTWith online social networking having revolutionized the way in which individuals communicate and interact with each other, there is heightened research interest in the dynamics of social networks. This article seeks to contribute to this stream of research by addressing the key question of the impact of major life events, such as getting married or graduating from college, on social network evolution. Consistent with prior studies on the evolution of individuals’ social networks, we specifically focus on two key attributes of an individual's network: indegree of ties and relational embeddedness. By longitudinally analyzing the network activities of a large‐scale online social network, we find that the indegree of ties increased significantly following a major life event, and that this impact was stronger for more active users in the network. Interestingly, we also find that the broadcast of major life events served to revive dormant ties as reflected by a decrease in embeddedness following a life event. We also found that one‐time life events such as weddings had a greater impact than recurring life events such as birthdays on the evolution of a user's social network. From a research perspective, our study contributes to existing research by focusing on a user's communication network as opposed to friendship network and by emphasizing how exogenous life events add a different dimension to user communication patterns. The importance of life events on social network evolution has important implications for practice as well by providing insights to advertisers as well as to social networking sites.