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A trust-based recommendation model is regularized with user trust and item ratings called TrustSVD. Trust networks are large-world networks where many users are socially linked, suggesting the assumption of trust in recommendation systems. An item rating downloaded from the OSN Server can be viewed by the user. If the information is accessible on the server, all the adjacent devices are enabled and a peer to peer mode of communication is initiated. User reviews from a graphical forum are shown. It focuses on the rating prediction role in the current framework and has shown that integrating user social confidence data will boost the output of recommendations. The strategy builds on the SVD++ state-of-the-art model. The data sparsity and cold start issues are resolved in the friend of friend recommendation model used. The mining method generates the user's overall rating in graphical representations and illustrates the overall rating. This model increases the utility of data by exchanging neighborhoods to protect security and privacy issues. One of the most common techniques for implementing a recommendation scheme is Collaborative filtering (CF).

Background Dyadic-based social networks analyses have been effective in a variety of behavioral- and health-related research areas. We introduce an ontology-driven approach towards social network analysis through encoding social data and inferring new information from the data. Methods The Friend of a Friend (FOAF) ontology is a lightweight social network ontology. We enriched FOAF by deriving social interaction data and relationships from social data to extend its domain scope. Results Our effort produced Friend of a Friend with Benefits (FOAF+) ontology that aims to support the spectrum of human interaction. A preliminary semiotic evaluation revealed a semantically rich and comprehensive knowledge base to represent complex social network relationships. With Semantic Web Rules Language, we demonstrated FOAF+ potential to infer social network ties between individual data. Conclusion Using logical rules, we defined interpersonal dyadic social connections, which can create inferred linked dyadic social representations of individuals, represent complex behavioral information, help machines interpret some of the concepts and relationships involving human interaction, query network data, and contribute methods for analytical and disease surveillance.

The cautionary language provided maxims that were directly relevant to how Julius makes sense of his friendships. Julius cannot afford to burn bridges as it is thanks to friendships that he has been able to remain in Japan. Julius was reluctant at first to explain how he got the job that enabled him to legally remain in Japan. Eventually, he admitted that his job resulted from a favour by a “friend of a friend” of his former coach, who also happens to have deep ties to and clout in the distance-running world in Kenya. The creativity of any income must be done individually to get something to sustain their needs. While the definition of the term is open to local interpretations, a common theme relates specifically to development or betterment. Kenyans perceive development to be a state that they are constantly lacking but perpetually desiring, particularly in the context of the deregulation of the economy in 1993.

Making a payment in a privacy-aware payment channel network is achieved by trying several payment paths until one succeeds. With a large network, such as the Lightning Network, a completion of a single payment can take up to several minutes. We introduce a network imbalance measure and formulate the optimization problem of improving the balance of the network as a sequence of rebalancing operations of the funds within the channels along circular paths within the network. As the funds and balances of channels are not globally known, we introduce a greedy heuristic with which every node despite the uncertainty can improve its own local balance. In an empirical simulation on a recent snapshot of the Lightning Network we demonstrate that the imbalance distribution of the network has a Kolmogorov-Smirnoff distance of 0.74 in comparison to the imbalance distribution after the heuristic is applied. We further show that the success rate of a single unit payment increases from 11.2% on the imbalanced network to 98.3% in the balanced network. Similarly, the median possible payment size across all pairs of participants increases from 0 to 0.5 mBTC for initial routing attempts on the cheapest possible path. We provide an empirical evidence that routing fees should be dropped for proactive rebalancing operations. Executing 4 different strategies for selecting rebalancing cycles lead to similar results indicating that a collaborative approach within the friend of a friend network might be preferable from a practical point of view, 8 Pages + references. 9 figures

Galaxy groups provide the means for a great diversity of studies that contribute to a better understanding of the structure ofthe universe on a large scale and allow the properties of galaxies to be linked to those of the host halos. However, the identification ofgalaxy systems is a challenging task and therefore it is necessary to improve the techniques involved as much as possible.Aims. In view of the large present and forthcoming galaxy catalogues, we propose, implement, and evaluate an algorithm that combines the two most popular techniques used to identify galaxy systems. The algorithm can be easily applied to any spectroscopicgalaxy catalogue, but here we demonstrate its use on the Sloan Digital Sky Survey.Methods. Assuming that a galaxy group is a gravitationally bounded system that has at least one bright galaxy, we begin by identifying groups with a friends-of-friends algorithm adapted to fit this definition. In view of the shortcomings of this method, particularlythe lack of ability to identify low-number groups, and consequently the inability to study the occupation of halos throughout the massrange, we improve it by adding a halo-based procedure. To assess the performance, we construct a mock catalogue from a semianalytical model to compare the groups identified using our method with those obtained from the simulation.Results. The comparison of groups extracted using our method with those of a mock catalogue shows that the proposed algorithmprovides excellent results. The modifications introduced to the friends-of-friends algorithm in the first part of the procedure to fit theadopted group definition gave reliable groups. Furthermore, incorporation of the halo-based method reduces the interlopers whilemore accurately reproducing the number of galaxies per group. As a specific application, we use the algorithm to extract groups fromthe Sloan Digital Sky Survey Fil: Rodriguez, Facundo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina Fil: Merchan, Manuel Enrique. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina

This paper focusses on the minority community of the Malaysian Sindhis to show that ethnic and cultural identities can be constructed through means other practices than solely by the heritage languages. The study draws on an open-ended question directed to 79 lady members of the Sindhi community in Kuala Lumpur, Malaysia. The respondents were women aged 50-70 and a friend of a friend helped in obtaining further respondents. This age group was selected as they often met for kitty parties, gambling sessions, and lunch parties. Their one-word responses were recorded and returned to the facilitator helping in data collection. The question was “What do you mean when you say you are a Sindhi?”. Based on the data, we contend that the term ethnicity denotes a subjective sense of community, meaning a shared identity which results in a sense of group solidarity. Identity is rooted in actual cultural practices and experiences.

International audience; People often attribute rumours to an individual in a knowledgeable position two steps removed from them (a credible friend of a friend), such as 'my friend's father, who's a cop, told me about a serial killer in town'. Little is known about the influence of such attributions on rumour propagation, or how they are maintained when the rumour is transmitted. In four studies (N = 1824) participants exposed to a rumour and asked to transmit it overwhelmingly attributed it either to a credible friend of a friend, or to a generic friend (e.g. 'a friend told me about a serial killer in town'). In both cases, participants engaged in source shortening: e.g. when told by a friend that 'a friend told me …' they shared the rumour as coming from 'a friend' instead of 'a friend of friend'. Source shortening and reliance on credible sources boosted rumour propagation by increasing the rumours' perceived plausibility and participants' willingness to share them. Models show that, in linear transmission chains, the generic friend attribution dominates, but that allowing each individual to be exposed to the rumour from several sources enables the maintenance of the credible friend of a friend attribution.

The average of global life expectancy at birth was 72 years in 2016 [1], however, the global healthy life expectancy at birth was only 63.3 years in the same year, 2016 [2]. Living a long life is not any more as challenging as assuring active and associated life [25]. We propose in this paper an IoT based holistic remote health monitoring system for chronically ill and elderly patients. It supports smart clinical decision help and prediction. The patient heterogeneous vital signs and contexts gathered from wore and surrounding sensors are semantically simplified and modeled via a validated ontology composed by FOAF (Friend of a Friend), SSN (Semantic Sensors Network)/SOSA (Sensor, Observation, Sample and Actuator) and ICNP (International Classification Nursing Practices) ontologies. The reasoner engine is based on a scalable set of inference rules cohesively integrated with a ML (Machine Learning) algorithm to ensure predictive analytic and preventive personalized health services. Experimental results prove the efficiency of the proposed system.

Friend recommendation (FR) in social networks has been widely studied in recent years, which mainly focuses on social relationships and user interests. Friend of Friend method is one representative. However, the disadvantage is that most of existing solutions ignored other valuable information, such as user profile, location, influence and indirect trust. In fact, being friends among users is either determined by one or two dominant factors that originate from varying information sources, or the results of multiple main factors gaming. Motivated by the observations above, we propose a scalable FR framework in social networks, where multiple sources have been integrated based on improved D-S evidence theory. More specifically, we first analyzed 7 valuable information sources and categorized them into three classes, including Personal Features, Network Structure Features and Social Features. Furthermore, we also propose a fusion recommendation framework based on D-S evidence theory which embodies the minimal conflicts among evidences. In the proposed method, we first optimize the framework by importance degree and reliability of evidence based on original D-S evidence theory. Then, we designed a novel BPA evidence function by quantifying the evidence, where each evidence measures the relevance of forming friends among users. Finally, we describe the fusion FR algorithm plugged into our recommendation framework. The experiments on real-world dataset show that our proposed approach outperforms the other state-of-the-art algorithms on five evaluation metrics. The experimental results demonstrate the effectiveness of fusing multi-source information for FR in social networks.

Is the enemy of an enemy necessarily a friend? If not, to what extent does this tend to hold? Such questions were formulated in terms of signed (social) networks and necessary and sufficient conditions for a network to be "balanced" were obtained around 1960. Since then the idea that signed networks tend over time to become more balanced has been widely used in several application areas. However, investigation of this hypothesis has been complicated by the lack of a standard measure of partial balance, since complete balance is almost never achieved in practice. We formalize the concept of a measure of partial balance, discuss various measures, compare the measures on synthetic datasets, and investigate their axiomatic properties. The synthetic data involves Erd\H{o}s-R\'enyi and specially structured random graphs. We show that some measures behave better than others in terms of axioms and ability to differentiate between graphs. We also use well-known datasets from the sociology and biology literature, such as Read's New Guinean tribes, gene regulatory networks related to two organisms, and a network involving senate bill co-sponsorship. Our results show that substantially different levels of partial balance is observed under cycle-based, eigenvalue-based, and frustration-based measures. We make some recommendations for measures to be used in future work., Comment: Peer-reviewed author copy, 31 pages, 6 figures, 5 tables

We show that hard to observe, indirect connections between a CEO and “independent” board members are associated with higher CEO compensation. While we find this result for the “friend of a friend” connection, we do not find it for direct connections, i.e. friends sitting on the board. We postulate that this differential result is caused by directors with readily observable connections to the CEO being wary of provoking outrage. In contrast we find both types of connections associated with reduced involuntary CEO turnover, suggesting that outrage is not as big a concern, e.g., compensation is the foci of stakeholders.

We live in an era where the evolution of social networks and developments in the field of information technology witnesses and exponential growth. On a daily basis, we have an ever growing, an ever expanding social network users and as a result we affirm an increase of data that is distributed across different platforms. In this paper, we aim to treat the semantic web from the prism of creating a suitable infrastructure for data integration on the web. One of the most promising applications of the semantic web is the presentation of profiles using an RDF (Resource Description Framework) schema called Friend-of-Friend (FOAF) which represents a machine-processable ontology for describing persons, their activities and relations to other people and objects. In order to demonstrate the concept on how to integrate, relate and share information using FOAF, we model and develop a mobile application called "Find professional" which consists in creating, finding and interacting in a context aware fashion with other users via FOAF. The “Find Professional” mobile app can create a FOAF profile either by asking user’s direct input or it can utilize other social network profiles such as Facebook for importing user public profiles into FOAF. User profiles represent RDF files that can be read and queried by semantic query languages like SPARQL, which returns data to their user profiles as well as finds professionals in the FOAF app based on user’s geographic location. The application is also evaluated with a field usability testing performed against ten randomly picked user from various profiles and backgrounds. Usability testing process did not yield severe problems during application evaluation and comments are more related to user interface rather than difficulty in user task completion.

Objectives. To test the hypothesis that violence among US adolescents spreads like a contagious disease through social networks. Methods. Participants were a nationally representative sample of 90 118 US students aged 12 to 18 years who were involved in the National Longitudinal Study of Adolescent Health. Violence was assessed by having participants report the number of times in the preceding 12 months they had been involved in a serious physical fight, had hurt someone badly, and had pulled a weapon on someone. Results. Participants were 48% more likely to have been involved in a serious fight, 183% more likely to have hurt someone badly, and 140% more likely to have pulled a weapon on someone if a friend had engaged in the same behavior. The influence spread up to 4 degrees of separation (i.e., friend of friend of friend of friend) for serious fights, 2 degrees for hurting someone badly, and 3 degrees for pulling a weapon on someone. Conclusions. Adolescents were more likely to engage in violent behavior if their friends did the same, and contagion of violence extended beyond immediate friends to friends of friends.

This paper presents the design of both Web and Mobile applications for managing the data of students and teachers of an educational institute using RDF (Resource Description Framework) and FOAF (Friend Of A Friend) based vocabulary. The implementation of an OWL (Ontology Web Language) as the core of a school administration system shows advantages compared to the standard database structure systems, this also increases the possibilities for smartphones to become the staple tool for classroom management. This project involves two main phases, the first one focused on the creation of the tools for the academic staff and the second being a mobile app for students to use in class. Partial results showed that the use of linked data provides useful information for the teacher to be aware of details of the students like how being close to a specific classmate has a noticeable impact on his/her class performance.

Humans exhibit a remarkable capacity for cooperation among genetically unrelated individuals. Yet, human cooperation is neither universal, nor stable. Instead, cooperation is often bounded to members of particular groups, and such groups endogenously form or break apart. Cooperation networks are parochial and under constant reconfiguration. Here, we demonstrate how parochial cooperation networks endogenously emerge as a consequence of simple reputation heuristics people may use when deciding to cooperate or defect. These reputation heuristics, such as “a friend of a friend is a friend” and “the enemy of a friend is an enemy” further lead to the dynamic formation and fission of cooperative groups, accompanied by a dynamic rise and fall of cooperation among agents. The ability of humans to safeguard kin-independent cooperation through gossip and reputation may be, accordingly, closely interlinked with the formation of group-bounded cooperation networks that are under constant reconfiguration, ultimately preventing global and stable cooperation., Group membership can inform individuals’ decisions on whether to cooperate. Here, the authors show how cooperative groups themselves can emerge and change due to use of reputation heuristics (such as “the enemy of a friend is an enemy”), and how this destabilizes cooperation over time.

Management accountants, and especially those without corporate allegiances, are in a special position to interrogate the workings of business and government, to raise questions and to recognise good work as well as highlight the bad. The alternative might not be to resume the abandoned project but at least to recognise and exploit positive spin-offs. [...]we have two different and provocative aspects of critical thinking, one likely obstructive and the other potentially productive. Creative writing in the form of poetry and short prose pieces is edited for the Literature and Insights Section only and does not undergo the refereeing procedures required for all research papers published in the main body of AAAJ.

With the advancement of the Internet and web technologies, social networks have gained attention as a new paradigm for user-centered information systems. As the amount of accessible information increases, the need for personalized information increases as well. Under such circumstances, social networks that are based on trust between users are increasingly utilized to provide efficient and reliable information management. This paper proposes the cognitive social network analysis that analyzes relationships between users with typical properties. The proposed approach analyzes users’ habitual activities and creates a local social network. The framework then integrates the local networks via the friend of a friend, thereby creating a global social network. User relationships in the global network are reinforced to maximize information sharing. To evaluate the performance of the information shared in the proposed autonomic cognitive social network framework, the accuracy of the information associated with social network was measured using the ROC Curve. In future, we should analyze the social influence factors from relationship between community and users.

In this paper, we present a proposal for searching, retrieving and recommending documents, based on the similarity of their content, using an ontology of the type “Friend of a Friend” that establishes, if it is the case, the transitivity of similarity between the document selected by the user and the other documents in the repository. Document similarity is calculated as the cosine of the angle between the vectors of terms that represent them. The Vector Space Model is used for representing the documents and the weight of the terms is obtained by using the TF-IDF metric. The architecture that implements the proposal is presented as well as experimental results.

Social network sites have become de factor in fostering human relationships and business prospects. Several social networks abound with little interoperability functionality that enables exchange of profiles of users. Though, proprietary Application Programming Interfaces (APIs) are provided as endpoints for applications in retrieval of user profile. Moreover, semantic web Friend of a Friend (FOAF) is now been used as a medium for realizing semantic social networks to be able to share user's profile across sites. And since the goal of semantic web is to provide autonomous data centric system coupled on ontology and reasoning, we propose a novel communication protocol named iProc, and usable by autonomous agents that relies on the distributive nature of social network data in coalescing a virtually centralized social network and as well providing means to enlarge user's connectivity to other users across different sites. This paper presents the architecture for a proposed iProc. Furthermore, an implementation of the FOAF files to be used was carried out and discussed.

In collaborative filtering users with highly similar tastes are termed 'near neighbours' and recommendations for a user are based on her 'near neighbours' ratings. However, for a number of users no near neighbours can be found, a problem termed as the 'grey sheep' problem. This problem is more intense in sparse datasets. In this work, we propose an algorithm for alleviating this problem by exploiting the friend of a friend (FOAF) concept. The proposed algorithm, CFfoaf, has been evaluated against eight widely used sparse datasets and under two widely used collaborative filtering correlation metrics, namely the Pearson Correlation Coefficient and the Cosine Similarity and has been proven to be particularly effective.

Trust aware recommender systems (TARS) are a branch of the most popular technique of recommender systems that is Collaborative Filtering. Lots of studies used trust as an enhancement factor for improve the accuracy of TARS, but the main problem of using trust is sparsity and also scalability problem for updating explicit data for new users, trust propagation used as a solution for solving the problem, but recent studies shows that using distrust values can also be useful, but same as trust and even worse, sparsity is a critical problem of dataset for using distrust. In this paper we used friend of friend and enemy of enemy concept as a way for propagating distrust and exploit new implicit relations for solving the sparsity problem and also improving the accuracy of recommendations. The results shows that propagating of distrust values is beneficial in enhancement of trust ware recommender systems.

Paper-based systems are being transported to electronic platforms with the development of technology. The Internet usage of people is increasing day by day. While the number of smartphone users is 2.1 billion in 2016, it is estimated that this number will be 2.8 billion in 2020. According to another study, the number of smartphone users in 2020 is estimated to be 6.8 billion. Personal data pass to the digital world because of the intensive use of the Internet and electronic systems. Person’s consent is necessary to use the data that has passed to the digital platform. In this study, a Consent ontology is developed by providing a semantic solution to the consent management in order to control the approval of persons. The developed Consent ontology imports Friend of a Friend (FOAF) and Relationship ontologies. The FOAF and Relationship ontologies are extended in Consent ontology in order to handle persons’ consent management. Thus, consent management could be provided by ensuring data privacy.

AimTo explore, in a general practice setting, the concerns, beliefs and attitudes about intrauterine contraception (IUC) reported by women, who had never used the methods.MethodsWe used a sequential mixed-method (QUAL/quant) approach. A pragmatic, self-selecting sample of 30 women, aged 18–46 years, who had never used IUC), was recruited through seven general practices in South East England. Themes arising from qualitative interviews were used to construct a quantitative survey, completed by a pragmatic sample of 1195 women, aged 18–49 years, attending 32 general practices in the same region, between February and August 2015.ResultsQualitative themes were concerns about the long-acting nature of IUC, concerns about body boundaries, and informal knowledge of IUC, especially ‘friend of a friend’ stories. Women were not sure if the devices can be removed before their full 5- or 10-year duration of use, and felt that these timeframes did not fit with their reproductive intentions. Quantitative survey data showed that the most commonly endorsed concerns among never-users were painful fitting (55.8%), unpleasant removal of the device (60.1%), and concern about having a device ’inside me' (60.2%).ConclusionsTo facilitate fully informed contraceptive choice, information provided to women considering IUC should be tailored to more fully address the concerns expressed by never-users, particularly around the details of insertion and removal, and concerns about the adverse, long-term effects of the device. Women need to be reassured that IUC can be removed and fertility restored at any time following insertion.Trial registrationTrial registration NIHR CRN portfolio; 15912.

To Ramanujan, each number was a personal friend in whose company, a lifetime he did spend. Let us too begin this quest to befriend numbers of interest. A friend of a friend is a friend, may we not pretend ?!

Not too long ago, at a lighthearted social gathering, I stumbled into a conversation on the United States’ martial presence in the Middle East. The person with whom Ispoke is a friend of a friend, a young man with a military background. I do not share these ties. We were struggling to find our feet with one another. And despite our best efforts at civility, our talk was taking a turn for the tense.

Virtual worlds (VWs) are becoming effective interactive platforms in the fields of education, social sciences and humanities. Computing similarity among users is a technique commonly used to make friend recommendations in social networks. However, user communities in virtual worlds tend to have fewer real world linkages and more entertainment-related goals than those in social networks. The above characteristics result in an ineffective modality with respect to applying existing friend recommendation methods in virtual worlds. This study develops a virtual friend recommendation approach based on user similarity and contact strengths in virtual worlds. In the proposed approach, users' contact activities in virtual worlds are characterized into dynamic features and contact types to derive their contact strengths in communication-based, social-based, transaction-based, quest-based and relationship-based contact types. Classification approaches were developed to predict friend relationships based on user similarity and contact strengths among users. A novel friend recommendation approach is further developed herein to recommend friends as regards certain virtual worlds based on friend-classifiers. The evaluation uses mass data collected from an online virtual world in Taiwan, and validates the effectiveness of the proposed methodology. The experiment results show that the friend classifier that takes into account user similarity and contact strengths can elicit stronger prediction performance than the friend-classifier that considers only user similarity. Moreover, the proposed friend recommendation method outperforms the traditional friend of friend (FOF) method of friend recommendation in virtual worlds. User contact activities are characterized into dynamic features and contact types to derive their contact strengths.Classification approaches are developed to predict friend relationships based on user similarity and contact strengths.Novel friend recommendation approaches are further developed to recommend virtual friends based on friend-classifiers.

International audience; Trust and reputation management is introduced to the Online Social networks (OSNs) as a solution to promote a healthy collaboration relationship among participants. Currently, most trust and reputation systems focus on evaluating the credibility of the users. The reputation systems in OSNs have as objective to help users to make difference between trustworthy and untrustworthy, and encourage honest users by rewarding them with high trust values. Computing reputation of one user within a network requires knowledge of trust degrees between the users. In this paper, we develop a new Fuzzy Clustering Reputation algorithm, called FCR, based on trusted network. This algorithm computes the membership degrees for each user and classifies the users of OSNs by their trust similarity such that most trustworthy users belong to the same cluster. Moreover, to satisfy the needs of OSNs' users by storing important trust data in a well organized and easy to understand structure, we propose to enrich the Friend of Friend (FOAF) vocabulary with the reputation of users. Experimental results with data from the real OSN, Twitter, show that our work generates high quality results

While the potential benefits of the Friend of a Friend (FOAF) vocabulary within the context of facilitating Social Networking Site (SNS) user profile portability have been broadly discussed, the potential benefits for users with visual impairments have been overlooked. Previous studies have identified SNSs categorically as presenting a number of unique usability challenges for users with visual impairments, a contentions supported by our ethnographic study of Facebook involving blind participants and the JAWS screen reader. Participants were most severely challenged by the process of creating and modifying user profile information and identifying other users with whom to establish relationships. The ability to decouple these activities from any single SNS using a FOAFbased cloud system may enable users to perform these activities in a more screen reader compatible (e.g. JAWS, NVDA) environment. In this paper we propose a portable profile cloud architecture based on the FOAF vocabulary and discuss the implementation of this architecture using several services of the Windows Azure cloud ecosystem. This system, as implemented, enables users to create multiple, parallel SNS-agnostic user profiles. As the system is currently constituted, these profiles can be generated with new data or information retrieved from an existing Facebook user profile. This modifiable profile and relationship information is then exported to Azure BLOB storage as an RDF file that may be accessed via a URL and may be used with any compatible system.

Contents: Introduction: Our Mutual Friend: 'the poorest of Mr Dickens's works' The man from somewhere: Ellen Ternan, Staplehurst, and the remaking of Charles Dickens The cup and the lip: writing Our Mutual Friend Putting a price upon a man's mind: Our Mutual Friend in the marketplace A dismal swamp? Our Mutual Friend and Victorian critics The voice of society: Our Mutual Friend since 1870 Appendices Bibliography Index.

The structure of community represents the latent social context of user activities and has important implementations in the field of collaborative filtering in recommendation systems, in particular; when the recommended items can be inferred based on all users' interests within a community. The important issue when there are some social networks which have no ability to division due to high connectivity among their nodes leads to overlapping communities. In this paper, we try to improve the quality of communities which created from social relations networks that have relatively cohesive connectivity. The adjacency matrix was created using direct friends and friend of a friend (FOAF) to identify the communities of friends using PAM (Partitioning Around Medoid) clustering algorithm. Experimentally, three datasets have been used as test bed for our proposed method: Delicious1, Lastfm2 and ego-Facebook3 datasets. For the performance evaluation purpose, the results obtained by proposed method are compared with results of Gephi, standard graph visualization software, using the association measure Cramer's V. Eventually, taking into consideration the direct friends and FOAF in the social graph rather than only direct friends show improvement in the quality of communities which is nearly comparable to that of Gephi. Furthermore, it is arguable to say that the outlier nodes have been mitigated as well, which represent users who are almost separate from their communities.

Recently, social networks have attracted a particular interest in the web, particularly Facebook. Consequently, a large amount of social data is produced. For semantic modeling of such data, several social network ontologies are introduced. The most popular is FoaF (Friend of a Friend) ontology.

We study how strangers become friends within an evolving online social network. By modeling the co-evolution of individual users' friendship tie formations (when and with whom) and their concurrent online activities, we uncover important drivers underlying individuals' friendship decisions and, at the same time, quantify the resulting peer effects on individuals' actions. We estimate our model using a novel data set capturing the continuous development of a network and users' entire action histories within the network. Our results reveal that similarity (homophily) with a potential friend, the properties of a potential friend's network, and her domain expertise all play a role in friendship formation. Via prediction exercises, we find that stimulating anime watching is the most effective site-wide intervention which leads to the highest overall site traffic and the largest number of active users and that recommending a friend of a friend as a potential friend is the most effective strategy in stimulating friendship tie formation. In contrast to the common finding for static networks, our results indicate that seeding to users with the most friends is not the most effective strategy to increase users' activity levels in an evolving network.

Renaissance Man (Fig. 1) refers to a person capable of excelling in a broad range of intellectual fields. A curious mind, open to new thoughts and ideas. At the same time, a concrete and pragmatic person, while projected in the future.

Collaborative filtering (CF), the most prevalent technique in the area of recommender systems (RSs), provides suggestions to users based on the tastes of their similar users. However, the new user and sparsity problems, degrade its efficiency of recommendations. Trust can enhance the recommendation quality by mimicking social dictum “friend of a friend will be a friend”. However distrust, the another face of coin is yet to be explored along with trust in the area of RSs. Our work in this paper is an attempt toward introducing trust-distrust enhanced recommendations based on the novel similarity measure that combines user ratings and trust values for generating more quality recommendations. Our approach also exploits distrust links among users and analyses their propagation effects. Further, distrust values are also used for filtering more distrust-worthy neighbours from the neighbourhood set. Our experimental results show that our proposed approaches outperform the traditional CF and existing trust enhanced approaches in terms of various performance measures.

HighlightsWe propose a novel method for injecting links between users in a social network.The proposed method aims at boosting the spread of information cascades.The injected links are being predicted based on factorizing the adjacency matrix.We evaluate performance by examining real data sets from social networks.Our results show that the proposed method boosts information cascades. We investigate information cascades in the context of viral marketing applications. Recent research has identified that communities in social networks may hinder cascades. To overcome this problem, we propose a novel method for injecting social links in a social network, aiming at boosting the spread of information cascades. Unlike the proposed approach, existing link prediction methods do not consider the optimization of information cascades as an explicit objective. In our proposed method, the injected links are being predicted in a collaborative-filtering fashion, based on factorizing the adjacency matrix that represents the structure of the social network. Our method controls the number of injected links to avoid an "aggressive" injection scheme that may compromise the experience of users. We evaluate the performance of the proposed method by examining real data sets from social networks and several additional factors. Our results indicate that the proposed scheme can boost information cascades in social networks and can operate as a "people recommendations" strategy complementary to currently applied methods that are based on the number of common neighbors (e.g., "friend of friend") or on the similarity of user profiles.

Social networks arise from complex interactions among multiple individuals and affect the emergent properties of groups (e.g. cooperation, disease spread, information transfer, etc.). Cooperation among nonkin is generally predicted to be favoured in structured social networks where individuals primarily interact only with certain individuals. Long-tailed manakins, Chiroxiphia linearis, form lek groups of as many as 15 unrelated males, whose members can attend multiple leks. At each lek, several top-ranked males perform the majority of obligate cooperative courtship displays. We used exponential random graph (ERG) modelling to analyse manakin cooperation networks constructed from 2-year time intervals over a 14-year study period. ERG modelling evaluates how local processes contribute to formation of global social network structure. We found that four local processes of link formation largely explained the overall structure of male manakin cooperation networks: (1) the spatial proximity of birds: males were more likely to cooperate if they primarily displayed at the same or neighbouring leks; (2) social status of birds: males were more likely to cooperate as they moved up the social queue at leks; (3) triad closure: males were more likely to cooperate with a ‘friend of a friend’ than with males with which they did not share a mutual partner; and (4) link persistence: males were more likely to cooperate with males whom they had cooperated with in the past. Other plausible mechanisms, such as selective mixing (the tendency to interact with individuals of similar or dissimilar social status) and preferential attachment by degree (whereby individuals with many social links gain additional links) did not consistently explain the structure of male cooperation networks at leks. These local processes may facilitate cooperation among long-tailed manakins by creating structured social networks in which males interact with only a subset of the population.

This paper presents a model of social capital and social network formation. The key interaction within the model is that whom an individual chooses to become friends with affects the value (social capital) of the friendship. In the model, how a player searches for and then forms friendships reveals how willing she is to engage in cooperation with a potential friend. Individuals observe their local network structure (friends and cliques) and the actions of players within these. Willingness to cooperate is private information and is captured by the discount factor of an individual. Cooperative types have high discount factors and can signal their type by forming a clique through befriending a friend of a friend. Uncooperative types do not form these kinds of friendships because of the local observability of their actions to all members of a clique. Thus, when a player meets someone with whom she shares a friend, her belief that the individual has a high discount factor is greater than the population average. In this sense, people “trust” each other more when they share a friend in common. Finally, I relate the primitives of the model to characteristics of the implied social network by nesting the sequential equilibrium in an algorithm of network formation proposed by Jackson and Rogers (Am Econ Rev 97(3):890–915, 2007). The model highlights a trade-off between maximizing the total amount of social capital in a society and distributing it equitably across individuals.

Mining frequent subgraphs is an important operation on graphs; it is defined as finding all subgraphs that appear frequently in a database according to a given frequency threshold. Most existing work assumes a database of many small graphs, but modern applications, such as social networks, citation graphs, or protein-protein interactions in bioinformatics, are modeled as a single large graph. In this paper we present GraMi, a novel framework for frequent subgraph mining in a single large graph. GraMi undertakes a novel approach that only finds the minimal set of instances to satisfy the frequency threshold and avoids the costly enumeration of all instances required by previous approaches. We accompany our approach with a heuristic and optimizations that significantly improve performance. Additionally, we present an extension of GraMi that mines frequent patterns. Compared to subgraphs, patterns offer a more powerful version of matching that captures transitive interactions between graph nodes (like friend of a friend) which are very common in modern applications. Finally, we present CGraMi, a version supporting structural and semantic constraints, and AGraMi, an approximate version producing results with no false positives. Our experiments on real data demonstrate that our framework is up to 2 orders of magnitude faster and discovers more interesting patterns than existing approaches.

This paper presents a novel model for scoring web pages, entitled SCOPAS (Semantic COmputation of PAge Score). With the prolific growth in the number of users of World Wide Web and the heterogeneity of their information needs, it becomes mandatory to evaluate the relevance of a web page in terms of user specific requirements. SCOPAS is aimed at modeling the web pages to facilitate efficient evaluation by harnessing the inherent features of the page in terms of its content and structure. The proposed model further enriches the scoring procedure by fine-graining the evaluation to a micro level through segmentation of the page. A variable magnitude, multi-dimensional approach is proposed for evaluating each of the segments by incorporating the relevance of intra-segment level components. The user-interest is captured with the help of FOAF (Friend Of A Friend) Ontology to achieve personalized page scoring. The generic SCOPAS model is extended to SCOPAS-Rank, which explores utilization of the model in improving the web search engine's result ordering. A prototype implementation of the proposed SCOPAS-Rank model is developed and experiments were conducted on it. The results of the experiments validate the effectiveness of the proposed model.