Friendship Links Research Articles

Percepciones de alumnado y profesorado sobre una intervención de mobile learning en Inglés como Lengua Extranjera

En la actualidad, el empleo de las tecnologías móviles está ampliamente extendido. Debido a ello, en los últimos años ha habido un creciente interés por incluir estos elementos en la enseñanza y visibilizar sus posibilidades educativas. Sin embargo, aún son escasos los trabajos que han estudiado este aspecto en Inglés como Lengua Extranjera en la etapa de educación secundaria. Por ello, el objetivo de este estudio fue el de conocer las percepciones de alumnado y profesorado sobre una intervención basada en Mobile Assisted Language Learning. Se utilizó un diseño de estudio de caso. 18 estudiantes de educación secundaria y una profesora participaron en la investigación. Se utilizaron diferentes instrumentos para la recogida de información: (1) diario del profesor, (2) pregunta abierta, y (3) entrevistas semi-estructuras. Se empleó el programa Atlas.ti 7 para ayudar a la gestión de la data. Todas las respuestas de los participantes fueron analizadas mediante métodos de comparación constante y análisis de contenido. El análisis de los datos produjo seis categorías: aprendizaje, novedad, cooperación, motivación, enfoque de enseñanza y amistad. Los resultados señalaron que la intervención ayudó al alumnado a mejorar sus competencias lingüísticas, su motivación por aprender inglés, a trabajar en equipo y a mejorar los lazos de amistad con sus compañeros. Asimismo, el planteamiento fue considerado novedoso, alejado del enfoque tradicional de enseñanza basado en el libro de texto. Como conclusión, estos resultados reflejan los beneficios educativos del aprendizaje mediante dispositivos móviles en la asignatura de Inglés como Lengua Extranjera en educación secundaria.

S186. History of clinicalneurophysiology in Morocco: From an integrated unit in neurology to an independent structure of neurophysiological explorations

Introduction Neurophysiology is a young discipline in Morocco. The first department of Neurophysiology was created in 2003 in Rabat. It is the fruit of the collaboration of the various factors of neurosciences as well clinical as fundamental. The objective of this study is to describe the evolution of clinical neurophysiology and its future prospects within the International Federation of Neurophysiology. Methods The first department of neurology in Morocco was created in 1978 by two doctors trained in hospitals in France. This unit dedicated to neurological diseases was established at the University Hospital of Rabat, the first teaching university hospital in Morocco. Currently, there are 160 qualified neurologists in Morocco. Indeed, for clinical neurophysiology as an independent department of neurology, started in 2003 in Rabat. This department was followed by the creation of another service within the Rabat University Military Teaching Hospital. Some hospitals integrate neurophysiological explorations in a department of general clinical explorations as in Fez, while others keep them attached to neurology services “Marrakech, Oujda, Casablanca …”. Results The Clinical Neurophysiology Service in Rabat has two EEG video rooms, two EEG rooms, two EMG rooms and one EP room. The military structure also has a study unit for sleep disorders. Many programs, besides routine activities, have taken place. Thanks to the contacts maintained with the Executive Committee of the International Federation of Clinical Neurophysiology at the International Congress of Clinical Neurophysiology in Berlin in 2014, an Association named “MOROCCAN SOCIETY OF NEUROPHYSIOLOGY” non-profit organization in accordance with the provisions of the Moroccan law was created with a seat based in the city of Marrakech where the first Moroccan congress of neurophysiology was held in 2016 and the second jointly with the First African Congress and MENA of clinical neurophysiology in Rabat in 2017, without taking into account the practical workshops of ENMG and in EEG. Conclusion The future of the SMNP crystallizes from its main objectives: Participate in the promotion of neurophysiology, Establish links of mutual aid, friendship and exchange of experience between its members and finally contribute to the improvement the exercise of neurophysiological techniques whether in the experimental or clinical field in Morocco. Achieving these goals will continually require the support of the International Federation of Neurophysiology.

Countries of a Feather: Analyzing Homophily and Connectivity Between Nations Through Facebook Data

Migration and trade which are facets of international connectivity, a somewhat nebulous concept often discussed in international relations. Homophily is a social concept with wide-ranging consequences. Recent studies into homophily using Facebook data have unearthed links between Facebook friendship and statistics traditionally used to understand connectivity - bilateral migration and trade. In this paper, we use data supplied by Facebook to examine these correlations in more detail for over 200 countries. We find that Facebook friend links between nations show strong correlations with the movement of humans and capital. Building upon these findings, we then discuss the value of such a metric to studies of connectivity and the possibility of a conceptual link between homophily and international connectivity.

Collective aspects of privacy in the Twitter social network

Preserving individual control over private information is one of the rising concerns in our digital society. Online social networks exist in application ecosystems that allow them to access data from other services, for example gathering contact lists through mobile phone applications. Such data access might allow social networking sites to create shadow profiles with information about non-users that has been inferred from information shared by the users of the social network. This possibility motivates the shadow profile hypothesis: the data shared by the users of an online service predicts personal information of non-users of the service. We test this hypothesis for the first time on Twitter, constructing a dataset of users that includes profile biographical text, location information, and bidirectional friendship links. We evaluate the predictability of the location of a user by using only information given by friends of the user that joined Twitter before the user did. This way, we audit the historical prediction power of Twitter data for users that had not joined Twitter yet. Our results indicate that information shared by users in Twitter can be predictive of the location of individuals outside Twitter. Furthermore, we observe that the quality of this prediction increases with the tendency of Twitter users to share their mobile phone contacts and is more accurate for individuals with more contacts inside Twitter. We further explore the predictability of biographical information of non-users, finding evidence in line with our results for locations. These findings illustrate that individuals are not in full control of their online privacy and that sharing personal data with a social networking site is a decision that is collectively mediated by the decisions of others.

Leaders in Juvenile Crime

This paper presents a new theory of crime where leaders transmit a crime technology and act as a role model for other criminals. We show that, in equilibrium, an individual's crime effort and crime decisions depend on the geodesic distance to the leader in his or her network of social contacts. By using data on friendship networks among U.S. high-school students, we structurally estimate the model and find evidence supporting its predictions. In particular, by using a definition of a criminal leader that is exogenous to the network formation of friendship links, we find that the longer is the distance to the leader, the lower is the criminal activity of the delinquents and the less likely they are to become criminals. This result highlights the importance of the closeness centrality of the leaders in explaining criminal behaviors. We finally perform a counterfactual experiment that reveals that a policy that removes all criminal leaders from a school can, on average, reduce criminal activity by about 20% and the individual probability of becoming a criminal by 10%.

Asocial balance\u2014how your friends determine your enemies: understanding the co-evolution of friendship and enmity interactions in a virtual world

Social interactions take place simultaneously through different interaction types, such as communication, friendship, trade, exchange, enmity, revenge, etc. These interactions can be conveniently described with time-dependent multi-layer networks. Little is known about the dynamics of social network formation on single layers. How the dynamics on one layer is coupled to and influences the dynamics on another layer is a completely unexplored territory. This is mainly due to the lack of comprehensive microscopic interaction data on time-dependent multi-layer networks. In this work, we study a unique dataset of 350,000 odd players in a massive multi-player online game, for which we know practically every social interaction event. We focus on the dynamics of friendship interactions and how they are coupled to the dynamics of enmity interactions. We are able to identify the driving processes behind the joint network formation of friendship and enmity links. The essential mechanisms turn out to be specific triadic closure rules. We propose a simple dynamical model that allows us to predict not only the correct levels of social balance but also the detailed simultaneous structural properties of the friendship and enmity networks, including their degree distributions, clustering coefficients and nearest neighbor degrees. While the formation of new friendship links can be largely understood on the basis of structural features of the friendship network alone, this is not true for enmity networks. The formation of enmity links is driven by the need to socially balance triadic relations that contain negative and positive interactions. Networks of enmity relations can only be understood structurally in the context of the positive social ties they are embedded in.

Measuring Social Connectedness

We introduce a new measure of social connectedness between U.S. county-pairs, as well as between U.S. counties and foreign countries. Our measure, which we call the Connectedness Index (SCI), is based on the number of friendship links on Facebook, the world's largest online social networking service. Within the U.S., social connectedness is strongly decreasing in geographic distance between counties: for the population of the average county, 62.8% of friends live within 100 miles. The populations of counties with more geographically dispersed social networks are generally richer, more educated, and have a higher life expectancy. Region-pairs that are more socially connected have higher trade flows, even after controlling for geographic distance and the similarity of regions along other economic and demographic measures. Higher social connectedness is also associated with more cross-county migration and patent citations. Social connectedness between U.S. counties and foreign countries is correlated with past migration patterns, with social connectedness decaying in the time since the primary migration wave from that country. Trade with foreign countries is also strongly related to social connectedness. These results suggest that the SCI captures an important role of social networks in facilitating both economic and social interactions. Our findings also highlight the potential for the SCI to mitigate the measurement challenges that pervade empirical research on the role of social interactions across the social sciences.

From community detection to community profiling

Most existing community-related studies focus on detection, which aim to find the community membership for each user from user friendship links. However, membership alone, without a complete profile of what a community is and how it interacts with other communities, has limited applications. This motivates us to consider systematically profiling the communities and thereby developing useful community-level applications. In this paper, we for the first time formalize the concept of community profiling. With rich user information on the network, such as user published content and user diffusion links, we characterize a community in terms of both its internal content profile and external diffusion profile. The difficulty of community profiling is often underestimated. We novelly identify three unique challenges and propose a joint Community Profiling and Detection (CPD) model to address them accordingly. We also contribute a scalable inference algorithm, which scales linearly with the data size and it is easily parallelizable. We evaluate CPD on large-scale real-world data sets, and show that it is significantly better than the state-of-the-art baselines in various tasks.

Leveraging Neighbor Attributes for Classification in Sparsely Labeled Networks

Many analysis tasks involve linked nodes, such as people connected by friendship links. Research on link-based classification (LBC) has studied how to leverage these connections to improve classification accuracy. Most such prior research has assumed the provision of a densely labeled training network. Instead, this article studies the common and challenging case when LBC must use a single sparsely labeled network for both learning and inference, a case where existing methods often yield poor accuracy. To address this challenge, we introduce a novel method that enables prediction via “neighbor attributes,” which were briefly considered by early LBC work but then abandoned due to perceived problems. We then explain, using both extensive experiments and loss decomposition analysis, how using neighbor attributes often significantly improves accuracy. We further show that using appropriate semi-supervised learning (SSL) is essential to obtaining the best accuracy in this domain and that the gains of neighbor attributes remain across a range of SSL choices and data conditions. Finally, given the challenges of label sparsity for LBC and the impact of neighbor attributes, we show that multiple previous studies must be re-considered, including studies regarding the best model features, the impact of noisy attributes, and strategies for active learning.

Presenting new collaborative link prediction methods for activity recommendation in Facebook

One of the common methods used in recommender systems is collaborative filtering methods. In these methods, same-interest users' preferences are often recommended to each other based on examining their past interests. On the other hand, one of the recommendation methods in social networks is to measure the proximity of the two nodes in the graph. Although many researchers have dealt with friendship link prediction in different online social networks, very little notice has been spent on activity prediction based on different users' interactions. The main objective of this paper is the use of collaborative filtering methods for activity prediction and recommendation both for pairs of users without any interaction background and also for user pairs with the activity background. In this regard, a new concept is initially presented named as “collaborative path”. Then based on the collaborative path, four directed proximity measures are proposed. In addition, three new algorithms, including two algorithms based on collaborative random walks, one for mixed network and one for multilayer network and the Collaborative-Association-Rule algorithm are presented. Finally, in order to evaluate our proposed methods, we perform some experiments on the dataset of different Facebook activity networks including like, comment, post, and share networks. The results show that the proposed collaborative methods deal with the activity prediction well without suffering from the cold start problem, and outperform the existing state of the art methods.

Link-Prediction Enhanced Consensus Clustering for Complex Networks.

Many real networks that are collected or inferred from data are incomplete due to missing edges. Missing edges can be inherent to the dataset (Facebook friend links will never be complete) or the result of sampling (one may only have access to a portion of the data). The consequence is that downstream analyses that “consume” the network will often yield less accurate results than if the edges were complete. Community detection algorithms, in particular, often suffer when critical intra-community edges are missing. We propose a novel consensus clustering algorithm to enhance community detection on incomplete networks. Our framework utilizes existing community detection algorithms that process networks imputed by our link prediction based sampling algorithm and merges their multiple partitions into a final consensus output. On average our method boosts performance of existing algorithms by 7% on artificial data and 17% on ego networks collected from Facebook.

The Relationship from Friendship Links to Educational Achievement

Abstract Using the pseudo-panel structure of friendship links, this paper employs a control function approach to estimate the impact of the number of friends on one’s own educational achievement in linear models. Results show that having one additional reciprocal or out-degree friend increases about 2 % of a standard deviation in cognitive test scores.

Inferring social network user profiles using a partial social graph

User profile inference on online social networks is a key task for targeted advertising and building recommender systems that rely on social network data. However, current algorithms for user profiling suffer from one or more of the following limitations: (1) assuming that the full social graph or a large training set of crawled data is available for training, (2) not exploiting the rich information that is available in social networks such as group memberships and likes, (3) treating numeric attributes as nominal attributes, and (4) not assessing the certainty of their predictions. In this paper, to address these limitations, we propose an algorithm named Partial Graph Profile Inference+ (PGPI+). The PGPI+ algorithm can accurately infer user profiles under the constraint of a partial social graph. PGPI+ does not require training, and it lets the user select the trade-off between the amount of information to be crawled for inferring a user profile and the accuracy the inference. Besides, PGPI+ is designed to use rich information about users when available: user profiles, friendship links, group memberships, and the "views" and "likes" from social networks such as Facebook. Moreover, to also address limitations 3 and 4, PGPI+ considers numeric attributes in addition to nominal attributes, and can evaluate the certainty of its predictions. An experimental evaluation with 31,247 user profiles from the Facebook and Pokec social networks shows that PGPI+ predicts user profiles with a higher accuracy than several start-of-the-art algorithms, and by accessing (crawling) less information from the social graph. Furthermore, an interesting result is that some profile attributes such as the status (student/professor) and genre can be predicted with more than 95 % accuracy using PGPI+.

The IEEE Industrial Electronics Society Is Cultivating Friendly Links Between Its Members [Message from the President

The IEEE Industrial Electronics Society Is Cultivating Friendly Links Between Its Members [Message from the President

Understanding latent interactions in online social networks

Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions , that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.

A Distributed Algorithm for Identifying Information Hubs in Social Networks

This paper addresses the problem of identifying the top-k information hubs in a social network. Identifying top-k information hubs is crucial for many applications such as advertising in social networks where advertisers are interested in identifying hubs to whom free samples can be given. Existing solutions are centralized and require time stamped information about pair-wise user interactions and can only be used by social network owners as only they have access to such data. Existing distributed algorithms suffer from poor accuracy. In this paper, we propose a new algorithm to identify information hubs that preserves user privacy. Our method can identify hubs without requiring a central entity to access the complete friendship graph. We achieve this by fully distributing the computation using the Kempe-McSherry algorithm, while addressing user privacy concerns. We evaluate the effectiveness of our proposed technique using three real-world data set; The first two are Facebook data sets containing about 6 million users and more than 40 million friendship links. The third data set is from Twitter and comprises of a little over 2 million users. The results of our analysis show that our algorithm is up to 50% more accurate than existing algorithms. Results also show that the proposed algorithm can estimate the rank of the top-k information hubs users more accurately than existing approaches.

The Embeddedness of Adolescent Friendship Nominations: The Formation of Social Capital in Emergent Network Structures.

Although research on social embeddedness and social capital con-firms the value of friendship networks, little has been written about how social relations form and are structured by social institutions. Using data from the Adolescent Health and Academic Achievement study and the National Longitudinal Study of Adolescent Health, the authors show that the odds of a new friendship nomination were 1.77 times greater within clusters of high school students taking courses together than between them. The estimated effect cannot be attributed to exposure to peers in similar grade levels, indirect friendship links, or pair-level course overlap, and the finding is robust to alternative model specifications. The authors also show how tendencies associated with status hierarchy inhering in triadic friendship nominations are neutralized within the clusters. These results have implications for the production and distribution of social capital within social systems such as schools, giving the clusters social salience as "local positions."

An agent-based model of the development of friendship links within Facebook

This paper investigates how local preferences and social structural constraints might affect the development of the friendship network in Facebook. We do this by analyzing a dataset of an American university, Caltech, and by building an agent-based simulation for comparison. Several different, but plausible, processes of friendship network development are proposed in which the structural information of the growing network and the student preferences are taken into account. `Network formation based on personal preference and social structure' matches the data best, and is thus the preferred hypothesis for the way that students add "friends" on Facebook.

Comparative analysis of articulated and behavioural social networks in a social news sharing website

This study analyses and contrasts the explicit (articulated) and implicit (behavioural) social networks on the Spanish Digg-like social news website meneame.net. The explicit network is given in the form of declared but not necessarily bidirectional friendship links; the behavioural network is extracted from conversations through comments to the shared links. These two directed social networks and their intersection are analysed and described in detail, which leads to some important conclusions about user behaviour on link sharing websites and online conversation habits in general. We find that reply interactions are more likely to occur between non-friends and that these interactions are (if bidirectional) also more balanced in the case of non-friends. A k-core decomposition of the networks reveals a fundamental difference in the practice of establishing behavioural and articulated links.

Scalable Affiliation Recommendation using Auxiliary Networks

Social network analysis has attracted increasing attention in recent years. In many social networks, besides friendship links among users, the phenomenon of users associating themselves with groups or communities is common. Thus, two networks exist simultaneously: the friendship network among users, and the affiliation network between users and groups. In this article, we tackle the affiliation recommendation problem, where the task is to predict or suggest new affiliations between users and communities, given the current state of the friendship and affiliation networks. More generally, affiliations need not be community affiliations---they can be a user’s taste, so affiliation recommendation algorithms have applications beyond community recommendation. In this article, we show that information from the friendship network can indeed be fruitfully exploited in making affiliation recommendations. Using a simple way of combining these networks, we suggest two models of user-community affinity for the purpose of making affiliation recommendations: one based on graph proximity, and another using latent factors to model users and communities. We explore the affiliation recommendation algorithms suggested by these models and evaluate these algorithms on two real-world networks, Orkut and Youtube. In doing so, we motivate and propose a way of evaluating recommenders, by measuring how good the top 50 recommendations are for the average user, and demonstrate the importance of choosing the right evaluation strategy. The algorithms suggested by the graph proximity model turn out to be the most effective. We also introduce scalable versions of these algorithms, and demonstrate their effectiveness. This use of link prediction techniques for the purpose of affiliation recommendation is, to our knowledge, novel.