A Vertex-Centric Graph Simulation Algorithm for Large Graphs

The behaviour of individual users in an online social network is a major contributing factor in determining the outcome of multiple network phenomenon. Group formation, growth of the network, information propagation, and rumour blocking are some of the many network behavioural traits that are influenced by the interaction patterns of the users in the network. Network motifs capture one such interaction pattern between users in online social networks (OSNs). For this work, four second-order (two-edged) network motifs have been considered, namely, message receiving pattern, message broadcasting pattern, message passing pattern, and reciprocal message pattern, to analyse user behaviour in online social networks. This work provides and utilizes a node interaction pattern-finding algorithm to identify the frequency of aforementioned second-order network motifs in six real-life online social networks (Facebook, GPlus, GNU, Twitter, Enron Email, and Wiki-vote). The frequency of network motifs participated in by a node is considered for the relative ranking of all nodes in the online social networks. The highest-rated nodes are considered seeds for information propagation. The performance of using network motifs for ranking nodes as seeds for information propagation is validated using statistical metrics Z-score, concentration, and significance profile and compared with baseline ranking methods in-degree centrality, out-degree centrality, closeness centrality, and PageRank. The comparative study shows the performance of centrality measures to be similar or better than second-order network motifs as seed nodes in information diffusion. The experimental results on finding frequencies and importance of different interaction patterns provide insights on the significance and representation of each such interaction pattern and how it varies from network to network.

This paper aims to identify user-centric features to calculate the strength of social ties between Online Social Network (OSN) users, and models the same using Latent Space Model (LSM). The modeling approach processes a socio-centric user-set as the users are directly (friend) or indirectly (friend-of-friend) related to a seed (target) user, which makes it easier to identify social ties between users as compared to random sampling from a set of diverse OSN users. For a given user, interaction data up to two levels is modeled and analyzed to generate a user-centric social network. Eleven different features related to Facebook have been identified to calculate the strength of social ties between users. LSM is used to visualize relationships in user-centric historical data and to estimate the probability of social ties between OSN users. The users are plotted using LSM in a three-dimensional (3D) social space around a seed user, and a link probability function is devised to calculate the probability of link between any two users with respect to the persona of the seed user. A sphere of influence around each user demarcating its active influence area is also identified and discussed in this paper.

Recently, communication and sharing the information through online social network (OSN) grows very fast in day to day life. As user share important data through online social network, therefore providing security and privacy to the online social network becomes the challenging task for the researchers. Moreover, for increasing the social connections and get the information from the particular group of people user needs to search the new friends and add them in the friend list. Now a days there isa friend recommendation method which was used by many online social networks, this method recommended the friend list to the user. As the privacy of these friend recommendation methods is the most significance task for the researchers and for the online social networks. Basically an online social network focused on the millions people's profile to spend time on an online social network, also implementing an OSN clients social circles, by using recommended suggestions. In this system, the main focus is on privacy and security of the online social network. This system basically focuses to support the users of online social network by secure trust creation with a stranger. This is achieved with multi-hop recommendation process. In the proposed method, the system provides privacy to the online social network from which system is allowing users to enhance their social networks. Existing system used secure KNN algorithm for securing social network. By using KNN algorithm, distance based learning is unclear. It is sort of distance and their component to use for the best results and calculation is very expensive. To overcome this limitation and to improve the precision, the proposed frame-work makes use of secure social coordinate matching with a Naive Bayes classifier. Also the privcy of social coordinates is maintained with Pailier encryption algorithm. Experimental results demonstrate that the proposed system is secure and time efficient than the existing one. Memory consumption is also improved with the proposed system.

The use of online social network (OSN) has become essential to humans' lives whether for entertainment, business or shopping. This increasing use of OSN motivates designing and implementing special systems that use OSN users' data to provide better user experience using machine learning and data mining algorithms and techniques. One system that is used extensively for this purpose is friend recommendation system (FRS) in which it recommends users to other users in professional or entertaining online social networks.
 For this purpose, this study proposes a novel friend recommendation system, namely Hybrid Friend Recommendation (FR) model. The Hybrid model applies dual-stage methodology on unlabeled data of 1241 users collected from OSN users via our online survey platform featuring user interests and activities based upon which users with similar social behavioral patterns are recommended to each other. The model employs a variety of techniques including user-based collaborative filtering (UBCF) approach and graph-based approach friend-of-friend recommendation (FOF). The model offers unique solutions to common problems of FRS such as data sparsity by using dimensionality technique called non-negative matrix factorization (NMF) to create a dense representation of the collected data and reduce its sparsity as well as providing seamless integration with other FRSs. The evaluation of the hybrid FR model shows positive correlation of Pearson correlation coefficient (PCC) compared to the baseline used.

The daily exposure of social media users to propaganda and disinformation campaigns has reinvigorated the need to investigate the local and global patterns of diffusion of different (mis)information content on social media. Echo chambers and influencers are often deemed responsible of both the polarization of users in online social networks and the success of propaganda and disinformation campaigns. This article adopts a data-driven approach to investigate the structuration of communities and propaganda networks on Twitter in order to assess the correctness of these imputations. In particular, the work aims at characterizing networks of propaganda extracted from a Twitter dataset by combining the information gained by three different classification approaches, focused respectively on (i) using Tweets content to infer the “polarization” of users around a specific topic, (ii) identifying users having an active role in the diffusion of different propaganda and disinformation items, and (iii) analyzing social ties to identify topological clusters and users playing a “central” role in the network. The work identifies highly partisan community structures along political alignments; furthermore, centrality metrics proved to be very informative to detect the most active users in the network and to distinguish users playing different roles; finally, polarization and clustering structure of the retweet graphs provided useful insights about relevant properties of users exposure, interactions, and participation to different propaganda items.

In the new millennium, information and communication technologies (ICTs) such as the internet and mobile phones have been developed rapidly. These new technologies have changed people’s communication patterns and provided new ways of maintaining online social networks which play ever-important roles in shaping the behavior of users on the web in the new millennium. ICTs also offer new computational models and data to investigate the dynamics and structure of exploiting the relationships and influences among individuals in online social networks. As an example, users on Wikipedia can vote for or against the nomination of others to adminship; users on Epinions can express trust or distrust of others. These facts illustrate that the relationship among the users of online social networks can be either positive or negative. The chapter will investigate negative as well as positive relationships of users in online social networks. We will focus on a novel dominating set named Weighted Positive Influence Dominating Set (WPIDS) problem arising from some social problems.

Online social networking usage is growing rapidly, especially among at-risk populations, such as men who have sex with men (MSM). However, little research has studied the relationship between online social networking usage and sexual risk behaviors among at-risk populations. One hundred and eighteen Facebook-registered MSM (60.1% Latino, 28% African American; 11.9% other) were recruited from online (social networking websites and banner advertisements) and offline (local clinics, restaurants and organizations) venues frequented by minority MSM. Inclusion criteria required participants to be men who were 18 years of age or older, had had sex with a man in the past 12 months, were living in Los Angeles, and had a Facebook account. Participants completed an online survey on their social media usage and sexual risk behaviors. Results from a multivariable regression suggest that number of sexual partners met from online social networking technologies is associated with increased: 1) likelihood of having exchanged sex for food, drugs, or a place to stay within the past 3 months; 2) number of new partners within the past 3 months; 3) number of male sex partners within the past 3 months; and 4) frequency of engaging in oral sex within the past 3 months, controlling for age, race, education, and total number of sexual partners. Understanding the relationship between social media sex-seeking and sexual risk behaviors among at-risk populations will help inform population-focused HIV prevention and treatment interventions.

Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user’s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user’s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.

The primary threat to online social network (OSN) users is account compromisation. The challenge in detecting a compromised account is due to the trusted relationship established between the account owners, their friends, and the service providers. The available research which focuses on using machine learning has limitations with human experts involved in feature selection and a standardized dataset. The paper discusses users` various behaviors of users of OSN and the up-to-date approaches in detecting a compromised OSN account with emphasis on the limitations and challenges. Furthermore, we propose an enhanced machine learning approach Word Embedding and KNN (WE-KNN), which addresses the limitations faced by the previous techniques used. We detailed our proposed WE-KNN for feature extraction, selection of behavior of OSN users, and classification. Our proposed model is evaluated using the standard benchmark datasets, namely KDD Cup ‘99 and NSL-KDD and implemented it in WEKA. Besides, we used state-of-the-art evaluation metrics to assess the performance of our model. The results obtained depicts that the proposed approach in compromise account detection performs better.

As the number of users in online social networks increases, the diffusion of information and users' opinions on events become more complex, making it difficult for traditional complex networks to accurately capture their characteristics and patterns. To address this, this paper proposes an online social network opinion evolution model that accounts for higher-order interactions. The model incorporates the higher-order effects of group interactions and introduces the acceptance, non-commitment, and rejection dimensions from social judgment theory. Different approaches, such as acceptance, neutrality, and contrastive rejection, are adopted when individuals exchange opinions with their neighbors. Through numerical simulations, it is shown that higher-order interactions significantly enhance the speed and coverage of information propagation. When the interaction dimensions are appropriate, increasing the average size of hyperedges significantly contributes to the formation of consensus. In contrast, simply increasing the number of hyperedges that nodes are involved in has a limited impact on consensus formation. This work provides a theoretical and model-based foundation for better understanding the dynamics of opinion evolution in social networks.

Due to the explosive growth of internet social networks, fake news for political and economic goals has become more pervasive in recent years. Users of online social networks may easily detect these online false news, which have already had a significant influence on offline society, through misleading phrasing. For online social networks to become more reliable sources of information, it is essential to quickly identify bogus news. This study aims to analyse and evaluate the guiding concepts, methodologies, and algorithms for identifying subjects, writers, and false news items from online social networks. This study focuses on the challenges brought about by the diverse relationships among news pieces, authors, and subjects as well as the unidentified traits of fake news. The ground-breaking automated fake news credibility inference algorithm FAKE DETECTOR is given in this work. This research looks at Word Embedding, the Count vectorizer, and the TF-IDF vectorizer, among other methods for detecting fake news. The classification methods that are also explored include decision trees, SVM, Logistic Regression and Gradient Boosting, Random Forest, Decision Trees, KNN, and XG-Boost.

A large body of work has been devoted to address corporate-scale privacy concerns related to social networks. Most of this work focuses on how to share social networks owned by organizations without revealing the identities or the sensitive relationships of the users involved. Not much attention has been given to the privacy risk of users posed by their daily information-sharing activities. In this article, we approach the privacy issues raised in online social networks from the individual users’ viewpoint: we propose a framework to compute the privacy score of a user. This score indicates the user’s potential risk caused by his or her participation in the network. Our definition of privacy score satisfies the following intuitive properties: the more sensitive information a user discloses, the higher his or her privacy risk. Also, the more visible the disclosed information becomes in the network, the higher the privacy risk. We develop mathematical models to estimate both sensitivity and visibility of the information. We apply our methods to synthetic and real-world data and demonstrate their efficacy and practical utility.

SentiHealth-Cancer: A sentiment analysis tool to help detecting mood of patients in online social networks

The introduction of online social networking has allowed people across the world to share information with each other. It has risen to be one of the most popular forms of internet usage where almost everyone has created one or multiple types of social network accounts. Along with its great benefits has come many concerns though, mainly that the overabundance of information has created a plethora of privacy issues for not only the users of online social networks, but also for the company hosting the specific social network. Due to the wide range of privacy concerns associated with online social networks, it is incredibly difficult to tackle all the current possible concerns. In this thesis, we propose works to tackle two privacy issues associated with online social networks. These two privacy issues are: the friend search engine, and image content. Firstly we will introduce a new sub-graph approach to the friend search engine that removes the ability of attackers to gain more information of your friend list than your privacy settings allow. Secondly we will introduce a new privacy setting that allows users to define locations they do not wish their face to be seen in images. If an image is posted with their face in such a location, they will be privatized through facial replacement so that they are unrecognizable. The overall efficiency of these works will be tested so that their enhanced privacy does not cause usability issues if they are adopted by a social network site. These works allow users to remain more private while using social media and also help users to remain confident that their privacy is kept safe. These improvements not only help strengthen the privacy of users, but also help social network sites retain users that are more wary of privacy breaches online.

Evaluating trust and distrust between users in online social networks is an important research problem. To address this problem, we provide a method for estimating continuous trust /distrust value between unconnected users. Our method is based on co-citation and transpose trust propagation. We determine on average how differently two users trust or are trusted by other users, and how differently a user trusts another user from how it is trusted by that user. Using these differences, we estimate four partial trust estimates and compute the final trust value from trustor to trustee as the weighted average of these partial estimates. We propose a basic framework that maximizes accuracy, robustness and coverage and show how we can further improve the accuracy at a lower coverage. We perform experiments on real world trust related networks that show that our proposed method outperforms recent state of the art trust computation methods in terms of accuracy and robustness on commonly used datasets.