Parallelization of game theoretic centrality algorithms

Understanding the importance of links in transmitting information in a network can provide ways to hinder or postpone ongoing dynamical phenomena like the spreading of epidemic or the diffusion of information. In this work, we propose a new measure based on stochastic diffusion processes, the transmission centrality, that captures the importance of links by estimating the average number of nodes to whom they transfer information during a global spreading diffusion process. We propose a simple algorithmic solution to compute transmission centrality and to approximate it in very large networks at low computational cost. Finally we apply transmission centrality in the identification of weak ties in three large empirical social networks, showing that this metric outperforms other centrality measures in identifying links that drive spreading processes in a social network.

In recent years, online social networks have become a very popular and effective forum for information exchange. These large, highly interconnected networks span the globe and have the ability to disseminate information in a fraction of the time it would take other communication networks. Given the myriad ways in which online social networks can be used, creating accurate, predictive models for the spread of information across them is very valuable. With that, modeling processes on large networks is a difficult task. It is computationally expensive, and usually prohibitive, to model a process on the entirety of a very large network. Given these complexities, creating smaller network graphs that are characteristically similar to the original networks graphs enable researchers to run models that are otherwise not feasible. This project aims to create prototypic networks and model the spread of information across them using traditional and network-based epidemiological models to better understand how information spreads across an online social network. More specifically, the focus will be on the spread of the news of a scientific discovery, i.e., the Higgs-Boson particle, on Twitter.

Finding a number of nodes that are able to maximize the spread of influence through the social network and are called influence maximization has numerous applications in marketing. One such application is to find influential members for promoting a product across a large network. Even though numerous algorithms have been proposed, challenges such as scalability, time constraints, and low accuracy have motivated the researchers for better solutions. Some of the newly proposed algorithms are scalable, but fail to provide adequate accuracy. On the other hand, some greedy algorithms provide a good level of accuracy but are very time consuming for large networks. In this paper, an algorithm is proposed called FAIMCS that can quickly find influential nodes across large networks with high accuracy. FAIMCS, reduces computational overhead considerably by eliminating major portions of the social network graph which have little influence. FAIMCS uses community detection algorithm to determine each community's quota of influential nodes based on the structure of that community. Finally, it obtains influential nodes from the candidate nodes. Experiment results show FAIMCS is faster than current algorithms and provides a high level of accuracy for large social networks.

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.

An Online Support Vector Machine Algorithm for Dynamic Social Network Monitoring

As the online social network (OSN) develops, the social media data can be easily obtained, which leads to a large scale social network data. In our offline social networks, social relationships that an individual (ego) maintains with other people (alters) can also be organized into circles or groups according to the ego network model [1]. As to online social network, in order to help users cope with personal social network, many OSN sites provide services for users to group their friends. With many restrictions, such as on Facebook one cannot access other's personal information unless they establish ties with each other, the network structure is the only information known by users. In this paper, we propose an algorithm to find circles on personal social network mainly from the structural view. Based on the defined structural similarity and the condition where nodes constitute a circle, the algorithm is able to cluster the most similar nodes into the same circle. We then give proof to demonstrate the convergence of our algorithm. To show the effectiveness of our algorithm, we realize the algorithm on Facebook datasets. Compared with the community detection algorithm, the results are evaluated from the visualized quantified aspect. From the results, we find that community detection algorithm cannot directly deal with the circle detection problem due to the characteristic of ego network and our algorithm can effectively handle the circle detection on ego network.

A novel measure of edge centrality in social networks

Misinformation in online social networks (OSNs) has been an ongoing problem, and it has been studied heavily over recent years. In this article, we use gamification to tackle misinformation propagation in OSNs. First, we construct a game based on the notion of cooperative games on graphs where the nodes of the social network are players. We use random regular networks and real networks in our simulations to show that the constructed game follows evolutionary dynamics and that the outcome of the game depends on the relation between the structural properties of the network and the benefit and cost variables defined in a cooperative game. Second, we create a game on the network level where the players control a set of nodes. We define agents whose goal is to maximize the total reward that we set up to be the number of nodes affected at the end of the game. We propose a deep reinforcement learning (RL) technique based on the multiagent deep deterministic policy gradient (MADDPG) algorithm. We test the proposed method along with well-known node selection algorithms and obtain promising results on different social networks.

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users' private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs.

Our social lives consist of various circles, such as family, friends, and colleagues. Differences in norms and expectations among these circles can create tension in large online social networks (OSNs) due to blurred boundaries. It is unclear whether this phenomenon, known as context collapse, outweighs the convenience of having diverse communities in one place for OSN users. To explore this trade-off, we examined if ego network characteristics indicating context collapse could explain users’ decisions to leave iWiW, a defunct Hungarian OSN with over 3.5 million active users at its peak. We assessed context collapse based on two conditions: the absence of overlapping communities measured by network modularity and social differences between those communities. We find that users with fragmented social networks indeed leave the platform earlier if these distinct communities differ significantly in their age profiles and urban-rural composition. However, the highest probability of leaving was among those with non-fragmented networks and similar communities. These seemingly contradicting results are caused by the process that network fragmentation itself decreases the probability of leaving. Thus, our results demonstrate simultaneously how brokerage can be valuable and context collapse stressful for users of OSNs.

In this paper we present a novel strategy to discover the community structure\nof (possibly, large) networks. This approach is based on the well-know concept\nof network modularity optimization. To do so, our algorithm exploits a novel\nmeasure of edge centrality, based on the k-paths. This technique allows to\nefficiently compute a edge ranking in large networks in near linear time. Once\nthe centrality ranking is calculated, the algorithm computes the pairwise\nproximity between nodes of the network. Finally, it discovers the community\nstructure adopting a strategy inspired by the well-known state-of-the-art\nLouvain method (henceforth, LM), efficiently maximizing the network modularity.\nThe experiments we carried out show that our algorithm outperforms other\ntechniques and slightly improves results of the original LM, providing reliable\nresults. Another advantage is that its adoption is naturally extended even to\nunweighted networks, differently with respect to the LM.\n

Sybil attacks are the major problems occurred in the distributed systems without trusted identities. It occur when the one-to-one relationship between a node and its identity is violated. This is occurred by an attacker creates multiple pseudonymous identities and pretends to be multiple, distinct nodes in the system. Intent search is another important function in social network. It mostly depends on surrounding text features which leads to noisy search result. In this paper a new Sybil defence mechanism is introduced that is Sybil protector, in order to defend against attacks in social networks. It is efficient and scalable to large social network. Network topologies are used by the Sybil protector to defend against Sybil attacks in social networks. It can effectively identify the Sybil nodes and detect the Sybil community around a Sybil node. And also limits the number of attack edges in online social networks. Here also included a novel internet image search which only requires one-click user feedback. For that expanded keywords are used to create positive example images and to include more relevant images it enlarge the image pool. It consist of four steps.1) Adaptive similarity.2) Keyword expansion. 3) Visual query expansion. Intent image search is very effective because it has extremely simple user interface. IndexTerms: Sybil attack, Sybil identity, social networks, attack edge, keyword expansion.

BackgroundThe dramatic growth of Web 2.0 technologies and online social networks offers immense potential for the delivery of health behavior change campaigns. However, it is currently unclear how online social networks may best be harnessed to achieve health behavior change.ObjectiveThe intent of the study was to systematically review the current level of evidence regarding the effectiveness of online social network health behavior interventions.MethodsEight databases (Scopus, CINAHL, Medline, ProQuest, EMBASE, PsycINFO, Cochrane, Web of Science and Communication & Mass Media Complete) were searched from 2000 to present using a comprehensive search strategy. Study eligibility criteria were based on the PICOS format, where “population” included child or adult populations, including healthy and disease populations; “intervention” involved behavior change interventions targeting key modifiable health behaviors (tobacco and alcohol consumption, dietary intake, physical activity, and sedentary behavior) delivered either wholly or in part using online social networks; “comparator” was either a control group or within subject in the case of pre-post study designs; “outcomes” included health behavior change and closely related variables (such as theorized mediators of health behavior change, eg, self-efficacy); and “study design” included experimental studies reported in full-length peer-reviewed sources. Reports of intervention effectiveness were summarized and effect sizes (Cohen’s d and 95% confidence intervals) were calculated wherever possible. Attrition (percentage of people who completed the study), engagement (actual usage), and fidelity (actual usage/intended usage) with the social networking component of the interventions were scrutinized.ResultsA total of 2040 studies were identified from the database searches following removal of duplicates, of which 10 met inclusion criteria. The studies involved a total of 113,988 participants (ranging from n=10 to n=107,907). Interventions included commercial online health social network websites (n=2), research health social network websites (n=3), and multi-component interventions delivered in part via pre-existing popular online social network websites (Facebook n=4 and Twitter n=1). Nine of the 10 included studies reported significant improvements in some aspect of health behavior change or outcomes related to behavior change. Effect sizes for behavior change ranged widely from −0.05 (95% CI 0.45-0.35) to 0.84 (95% CI 0.49-1.19), but in general were small in magnitude and statistically non-significant. Participant attrition ranged from 0-84%. Engagement and fidelity were relatively low, with most studies achieving 5-15% fidelity (with one exception, which achieved 105% fidelity).ConclusionsTo date there is very modest evidence that interventions incorporating online social networks may be effective; however, this field of research is in its infancy. Further research is needed to determine how to maximize retention and engagement, whether behavior change can be sustained in the longer term, and to determine how to exploit online social networks to achieve mass dissemination. Specific recommendations for future research are provided.

Online social media network has become part of human life by transforming the way users create new social relations or relate with family and friends. Online social network (OSN) has drastically increased the rate at which people interact with each other by simplifying the means of communication. However, privacy is raising a serious concern. All user generated data within the OSN system need to be protected against unauthorized friends or hackers or even against the provider of OSN. Many research works are going on to encounter the privacy issues in OSN. This paper analyses the limitations of the recent work being done in this field and proposes an efficient abstract solution to them.

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.