Common Friends Research Articles

Correlation between social proximity and mobility similarity

Human behaviors exhibit ubiquitous correlations in many aspects, such as individual and collective levels, temporal and spatial dimensions, content, social and geographical layers. With rich Internet data of online behaviors becoming available, it attracts academic interests to explore human mobility similarity from the perspective of social network proximity. Existent analysis shows a strong correlation between online social proximity and offline mobility similarity, namely, mobile records between friends are significantly more similar than between strangers, and those between friends with common neighbors are even more similar. We argue the importance of the number and diversity of common friends, with a counter intuitive finding that the number of common friends has no positive impact on mobility similarity while the diversity plays a key role, disagreeing with previous studies. Our analysis provides a novel view for better understanding the coupling between human online and offline behaviors, and will help model and predict human behaviors based on social proximity.

GLORY: Exploration and integration of global and local correlations to improve personalized online social recommendations

Nowadays people manage their social circles via a variety of online social media which employ social recommendation as an important component. Among social recommendation methods, global methods take an emphasis on common tastes between people while local methods assume that new relations are established mainly through people’s common friends. However, in a real social network, both local and global relations exist, which motivate us to integrate them to improve recommendation performance. To achieve the goal, we proposed a novel hybrid method GLORY to combine global associations with local correlations for social recommendation. GLORY consists of two components: GLOBE and LORY. The former is a globalised regression model to explore the concordance between people’s preference with the relatedness of their friends. The latter is an integration method to fuse global and local correlations via a rigorous statistical model to calibrate the statistical significance of these correlations. Furthermore, we demonstrated the effectiveness of our methods via 10-fold large-scale cross-validation on three real social network datasets (Facebook, Last.fm and Epinions). Results show that GLORY significantly outperform the state-of-the-art methods while LORY is effective across various global and local methods, indicating their promising future for social recommendations.

A Structural Model of Homophily and Clustering in Social Networks

Social networks display homophily and clustering, and are usually sparse. I develop and estimate a structural model of strategic network formation with heterogeneous players and latent community structure, whose equilibrium networks are sparse and exhibit homophily and clustering. Each player belongs to a community unobserved by the econometrician. Players' payoffs vary by community and depend on the composition of direct links and common neighbors, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a random matching shock. The probability of meeting people in different communities is smaller than the probability of meeting people in the same community, and it decreases with the size of the network. The model converges to an exponential family random graph, with weak dependence among links. As a consequence the equilibrium networks are sparse and the sufficient statistics of the network are asymptotically normal. The posterior distribution of structural parameters and unobserved heterogeneity is estimated with school friendship network data from Add Health, using a Bayesian exchange algorithm. The estimates detect high levels of racial homophily, and heterogeneity in both costs of links and payoffs from common friends. The posterior predictions show that the model is able to replicate the homophily levels and the aggregate clustering of the observed network, in contrast with standard exponential family network models.

The Journey of Adrienne Rich

The Journey of Adrienne Rich

Emotional Intelligence and Intimacy in Relationships

Emotional Intelligence is the ability to understand, manage one’s own and other’s feelings in order to adapt to the demands of the environment. Intimacy refers to the feelings of closeness and bondedness in close relationships. Emotional Intelligence help enhance intimacy by open expression of emotions and empathetic response to the partner’s self-disclosure. The study examined the relationship between emotional intelligence and intimacy in 108 young adults currently in a heterosexual romantic relationship, belonging to the age group of 20-35 years (M=24.1 years, SD= 3.23 years) consisting of 36 (30%) males and 72 (70%) females. The study employed three scales namely Sternberg’s Love Triangular Scale (1988), Schutte Emotional Intelligence Scale (1998) and Intimacy Attitude Scale Revised (1985). Data was collected through Google forms by emailing the questionnaire to the participants known to be in relationship through common friends and also by personally giving the questionnaire to students of universities in Delhi, NCR region. Positive correlation was found between Attitude towards intimacy and emotional intelligence; commitment and managing owns emotions subscale of emotional intelligence; attitude towards intimacy and managing other’s emotions subscale of emotional intelligence. Thus emotionally intelligent individuals have a greater desire towards intimacy and commitment in their relationships.

Relationship strength estimation based on Wechat Friends Circle

Wechat has become a popular way in China for users to connect and share information with their friends. In particular, Wechat Friends Circle is a popular platform for users to express themselves. It is useful in many real applications, such as personalized recommendations, to calculate the relationship strength between Friends Circle users. However, most existed researches do not apply to the Wechat Friends Circle due to the uniqueness that users can only see a common friend's comments or point praise, which is quite different from the general social network. To meet this need, in this paper we propose a general framework to measure the relationship strengths between different users in Wechat Friends Circle, considering not only the similarity of users’ profile information, but also the interaction between users. We conduct a set of experiments on Friends Circle dataset, from which we learn that the proposed framework is efficient and promising on improving the performances of relationship strength calculation.

In the Americas with Agnese Haury: A Remembrance

In the Americas with Agnese Haury:A Remembrance David A. Yetman (bio) Agnese Haury, philanthropist, humanitarian, and internationalist, was driven by an abiding commitment to social justice and a love of anthropology and archaeology. From the 1950s until her death in 2014 at age ninety, Agnese used her wealth to promote the well-being of the people of the world and support research into the cultures that came before us. Although most of the projects that received her support were U.S.-based, she always kept in mind the broader needs of the planet. I met Agnese in the mid-1980s through a common friend, the late lawyer Clague Van Slyke II. As an attorney, he represented a variety of clients, including some whose interests were affected by the government of Pima County, in which Tucson is located. I was then a member of the Pima County Board of Supervisors. From time to time, Clague would suggest that we have lunch and would invoke pro forma a project of one client or other, mention the client, then move on to more cosmopolitan topics. Clague phoned me one day at my office and said, in his fatherly voice, "David, there is someone you need to meet." I always interpreted these sorts of Clagueisms as most promising opportunities. I agreed at once. "How about if you join us for our law firm's annual Christmas dinner at Janos's restaurant." That would be difficult to refuse. At the time Janos's was Tucson's finest dining place, located in a wing of an old adobe building now occupied by the Tucson Museum of Art. At the dinner, which was rather formal, Clague introduced me to Agnese Haury and seated us next to each other. For the next two hours we talked nonstop, and I realized I had met an extraordinary individual. [End Page 400] Click for larger view View full resolution Agnese Nelms Haury, with children in Oaxaca. (Courtesy Loren Haury.) Agnese had become friends with Clague when his law firm had assisted her in establishing a program for providing interpretation services to non-English speakers who faced court proceedings. She had become indignant upon learning of the ongoing miscarriage of justice facing people having to appear in court but lacking English and an understanding of the U.S. legal system. To this day the program she funded is the basis for the National Center for Interpretation at the University of Arizona. Soon afterward, I learned of her close friendship with Alger Hiss and his family. Hiss had been a high official in the U.S. State Department, accused of espionage and treason by, among others, Richard Nixon. In the Cold War hysteria of the late 1940s, a federal jury convicted Hiss of perjury in proceedings that Agnese considered a kangaroo court, a sentiment she retained until her death. Agnese had worked with Hiss when he was director of the Carnegie Endowment for International [End Page 401] Peace and held him in high esteem. I mentioned to her that my father had followed the Alger Hiss case and was also convinced that Hiss had been railroaded. Agnese revealed that she contributed heavily to Hiss's defense and to the support of his family while he was undergoing the judicial proceedings. Hiss's son Tony has been a long-time recipient of Agnese's generosity and has contributed pioneering work establishing world centers of biodiversity and connections among them. Following that evening, and it was memorable, I do not recall any additional contact with Agnese until she joined me as a traveler to Alamos, Sonora, through a travel program that was the brainchild of Joe Wilder, director of the University of Arizona's Southwest Center. I had accepted a position there as a research social scientist in 1992. It was probably in 1994 that we made that trip. Dr. Wilder had asked me to undertake a re-publication and expansion of Howard Scott Gentry's 1942 publication called Río Mayo Plants, a seminal study of plants and vegetation with numerous references to the role plants have played in the lives of indigenous peoples of the Río Mayo region of southern Sonora...

Determine the Close Friends for Twitter Users by Analyzing Users

In this paper a new method to extract the closed friends to a certain twitter user is proposed. This algorithm can be used inmany applications such as security and marketing. In the proposed algorithm four relation indicators are used. Theshared twitter users, the common friends, the common followers and number of “retweeted from” are used todetermine the closed friends of a certain twitter user. The great data downloading and mining tool KNIME is used with some Rcoding. The proposed model could ranks the shared twitter users according to their closeness to a certain twitter user usingthe four used relation indicator.

Common friends discovery for multiple parties with friendship ownership and replay-attack resistance in mobile social networks

Social networking sites have emerged as a powerful tool for maintaining contact and sharing information between people, allowing users to quickly and easily communicate and interact over the Internet. However, such services have raised serious privacy concerns, especially in terms of ensuring the security of users’ personal information in the process of data exchange while also allowing for effective and complete data matching. Many studies have examined privacy matching issues and proposed solutions which could be applied to the current private matching issue. However, these solutions are almost entirely based on dual-matching designs. Therefore, this paper proposes a tripartite privacy matching protocol between common friends. In contexts with multiple users, this protocol searches for matching problems for common friends to produce a new solution. This approach does not rely on a trusted third party, and can be used on most mobile devices. In addition to providing outstanding operating performance and effective communication, this approach also accounts for context-specific privacy preservation, mutual authentication, mutual friendship certification, prevention of privacy spoofing and replay attack resistance, allowing users to safely and effectively identify mutual friends. The proposed methods are shown to be secure and efficient, and are implemented in mobile phones that allow users to find common friends securely in seconds. To the best of our knowledge, this is the first work done on mobile common friends discovery for three parties with advanced privacy preservation.

The most common friend first immunization**Project supported by the National Natural Science Foundation of China (Grant No. 61263019), the Program for International Science and Technology Cooperation Projects of Gansu Province, China (Grant No. 144WCGA166), and the Program for Longyuan Young Innovation Talents and the Doctoral Foundation of Lanzhou University of Technology, China.

In this paper, a standard susceptible-infected-recovered-susceptible(SIRS) epidemic model based on the Watts–Strogatz (WS) small-world network model and the Barabsi–Albert (BA) scale-free network model is established, and a new immunization scheme — “the most common friend first immunization” is proposed, in which the most common friend’s node is described as being the first immune on the second layer protection of complex networks. The propagation situations of three different immunization schemes — random immunization, high-risk immunization, and the most common friend first immunization are studied. At the same time, the dynamic behaviors are also studied on the WS small-world and the BA scale-free network. Moreover, the analytic and simulated results indicate that the immune effect of the most common friend first immunization is better than random immunization, but slightly worse than high-risk immunization. However, high-risk immunization still has some limitations. For example, it is difficult to accurately define who a direct neighbor in the life is. Compared with the traditional immunization strategies having some shortcomings, the most common friend first immunization is effective, and it is nicely consistent with the actual situation.

Application of pair approximation method to modeling and analysis of a marriage network

Numerous studies on marital issues have frequently resorted to statistical analysis or mathematical modeling on a well-mixed homogeneous population. However, such mathematical models have largely ignored the effects of heterogeneous contact pattern among individuals of the population. In this paper, we propose a pair model to describe the evolution of a marriage network, where an unmarried male and an unmarried female become friends with a certain probability through the introduction of their common friends. For convenience this probability is termed as the introduction probability. A threshold is obtained to determine whether there is a married couple or not. We verify the model by good agreement between the numerical solution of the model, computational simulations and the real marriage data in Beijing. The results show that the positive equilibrium of the model is steady and increases with the threshold. Moreover, the introduction probability contributes to increase both the threshold and the married population size. Furthermore, it is found that increasing the number of same sex friends, and balancing the sex ratio can also enlarge the married population size. All these results can provide some insights into the evolution and the structure of the marriage network.

Social Ties of University Students: Evidence from a Longitudinal Survey in Russia

Student friendship networks can be considered as social capital, which is known to be a very useful resource during university and after it. Several empirical studies have examined static models of student behaviour in social networks. In this study we analyse the dynamic changes of student social connections. We use original longitude data of student social ties from one Russian university. Data was collected within the framework of a research project of the International Research Laboratory for Institutional Analysis of Economic Reforms. To investigate factors influencing the evolution of social ties during university probit regressions were tested. We found that students with similar characteristics such as gender and academic achievement are more likely to become friends and continue to be friends. Both studying in the same group and living in a dormitory increase the likelihood of being friends. We also found a transitivity effect. We observe a positive effect of having common friend on friendship ties. We also notice a positive link between reciprocity and friendship stability.

Spatial representation of coherence.

Four experiments examined spatial correlates of the experience of coherence, that is, the extent to which propositions "fit together." Experiment 1 demonstrates for Heiderian triads (i.e., sets of liking/disliking relations between 3 fictitious persons) that name pairs from balanced triads, such as 2 friends commonly disliking a third person (high coherence) are seen as closer to each other in physical space as compared to name pairs from unbalanced triads, such as 2 persons disliking each other and having a common friend (low coherence). This pattern of results is conceptually replicated in 2 further experiments for categorical syllogisms. Two terms in conclusions from valid syllogisms (high coherence) were seen as spatially closer to each other than when 2 terms came from invalid syllogisms (low coherence). In the final 2 experiments, similar closeness effects are demonstrated for word pairs from scenarios that "made sense" in terms of causal connectedness (latent causality) as opposed to word pairs from scenarios perceived as causally unconnected. These findings are discussed in the context of spatial binding theories, applied psychology, and embodied cognition in general, and their methodological implications are highlighted. (PsycINFO Database Record

FRIEND RECOMMENDATION SYSTEM BY LDA MODEL USING TEXT MINING

A Few years ago, people casually made friends with people who work or live close to themselves, such partner or companion. This connection can be defined as G-friends, where G-friends stand for earthly location based friends because they are motivated by the earthly distances between each other. With the large advances in social networks, services such as Google+, Twitter, and Facebook have provided us many radical ways of making new friends. According to one of the popular social networks 'Facebook' statistics, single user has an average of 130 friends, conceivably larger than any other time in history. One challenge with current social networking services is how to recommend suitable friend to a user. Most of them depend on already existing user relationships to select friend candidates. For example, Facebook count on a social link analysis among those who already share common friends and recommends proportioned users as probable friends. Regrettably, this approach may not be the most appropriate based on recent friend findings.

FRIENDBOOK: A SEMANTIC-BASED FRIEND RECOMMENDATION SYSTEM FOR SOCIAL NETWORKS

TWENTY years past, folks generally created friends with others who live or work near themselves, like neighbors or colleagues. we have a tendency to decision friends created through this ancient fashion as G-friends, that stands for geographical location-based friends as a result of they're influenced by the geographical distances between one another. With the speedy advances in social networks, services like Facebook, Twitter and Google+ have provided us revolutionary ways in which of creating friends.According to Facebook statistics, a user has a mean of one hundred thirty friends, maybe larger than the other time in history. One challenge with existing social networking services is a way to suggest a good or reliable friend to a user. Most of them rely on pre-existing user relationships to choose friend candidates. for instance, Facebook depends on a social link analysis among those that already share common friends and recommends users as potential friends.Unfortunately, this approach might not be the foremost applicable supported recent social science findings. according to these studies, the principles to group individuals along include: 1) habits or life style; 2) attitudes; 3) tastes; 4) ethical standards; 5) economic level; and 6) individuals they already know. life styles are typically closely correlate with daily routines and activities. Therefore, if we tend to may gather data on users’ daily routines and activities, we are able to exploit rule #1 and suggest friends to individuals supported their similar life styles. This recommendation mechanism may be deployed as a standalone app on smartphones for existing social network frameworks.

“The Patrimony of Our Race”: Louis-Albert Bourgault-Ducoudray and the Emergence of the Discourse on Greek National Music

In 1876, Albert-Louis Bourgault-Ducoudray published a collection of “melodies from Greece and the Orient” in the conviction that he had found vestiges of Ancient Greek music in Greek folk music of his day. Bringing folk music to the attention of the educated classes, he hoped to fertilize art music and create the preconditions for a Greek national music. He also supplied the melodies that he collected with piano accompaniment, that is, he harmonized them. In his mind, harmonization was but the first step toward the formation of a Greek national music. Soon after this, his Greek project was embedded in hindsight in a broader Aryanist project explicitly described in the “Introduction” to his 1885 collection of Breton folk songs. Folk song harmonizations then became the first step toward the formation of the Aryan art music of the future. In the 1890s, he worked with Greek baritone Periklis Aravantinos, alias Aramis, to produce what he thought of as musical arguments for Aryanism. Aramis, a collector and harmonizer of folk songs himself, appeared in 1903 before Greek audiences in an attempt to promote a national school of music. Aramis’s and Bourgault-Ducoudray’s ideas on folk song harmonization and national music were in part vindicated by composer Spyros Samaras, a common friend, especially in the latter’s symbolically loaded Rhea (1908). His and other musical nationalisms were eclipsed by the appearance of the man most readily connected today to a Greek national school of music, Manolis Kalomoiris.

Flickr group recommendation using rich social media information

Today online social media communities have spanned the globe, browsing news from social networks almost becomes an essential part in our daily life. Groups organized by users always share something interesting. Joining groups which fit the users׳ tastes will help them to obtain information. However, traditional group recommendation methods usually focus on how to recommend an item to a group of users. In this paper, we study how to recommend groups to an individual user and reveal the factors which push a user to join groups. In social networks, a commonly adopted recommendation method takes advantage of the tastes of a user׳s trust neighbors and recommends groups which his/her neighbors have joined. It will performs poorly for the inactive users who have few trust neighbors. To overcome this problem, we try to find users׳ similar neighbors using tag information, which is not only from users׳ photos but also from their favorite photos and the common friend information. Hence we propose a group recommendation scheme utilizing users׳ trust neighbors and similar neighbors׳ tastes. We do the experiments on a real-world Flickr dataset and obtain a promising result especially for inactive users.

Revocation and update of trust in autonomous delay tolerant networks

A Delay Tolerant Network (DTN) is a dynamic, fragmented, and ephemeral network formed by a large number of highly mobile nodes. DTNs are ephemeral networks with highly mobile autonomous nodes. This requires distributed and self-organised approaches to trust management. Revocation and replacement of security credentials under adversarial influence by preserving the trust on the entity is still an open problem. Existing methods are mostly limited to detection and removal of malicious nodes. This paper makes use of the mobility property to provide a distributed, self-organising, and scalable revocation and replacement scheme. The proposed scheme effectively utilises the Leverage of Common Friends (LCF) trust system concepts to revoke compromised security credentials, replace them with new ones, whilst preserving the trust on them. The level of achieved entity confidence is thereby preserved. Security and performance of the proposed scheme is evaluated using an experimental data set in comparison with other schemes based around the LCF concept. Our extensive experimental results show that the proposed scheme distributes replacement credentials up to 35% faster and spreads spoofed credentials of strong collaborating adversaries up to 50% slower without causing any significant increase on the communication and storage overheads, when compared to other LCF based schemes.

Trust-Based Sybil Nodes Detection with Robust Seed Selection and Graph Pruning on SNS

On SNS (Social Networking Services), detecting Sybils is an urgent demand. The most famous approach is called “SybilRank” scheme where each node evenly distributes its trust value that is firstly given to honest seeds and detects Sybils based on the trust value. Furthermore, Zhang et al. proposed to avoid trust values from being distributed into Sybils by pruning suspicious relationships before SybilRank. However, we point out that the above two schemes have shortcomings that must be remedied. In the former scheme, seeds are concentrated on the specific communities and thus the trust value is not evenly distributed. Against the latter one, a sophisticated attacker can avoid graph pruning by making relationships between Sybil nodes. In this paper, we propose a robust seed selection and graph pruning scheme to detect Sybil nodes. To more evenly distribute trust value into honest nodes, we first detect communities in the SNS and select honest seeds from each detected community. And then, based on the fact that Sybils cannot make dense relationships with honest nodes, we also propose a graph pruning scheme based on the density of relationships between trusted nodes. We prune the relationships which have sparse relationships with trusted nodes and this enables robust pruning malicious relationships even if the attackers make a large number of common friends. By the computer simulation with real dataset, we show that our scheme improves the detection accuracy of both Sybil and honest nodes.

A Novel Fuzzy Logic Model to Identify Closeness for Alias Detection

The detection of person alias names are important for improving the accuracy of the information quality. The fuzzy-based decision system is proposed for alias detection, which is a rule-based system that uses fuzzy logic to make a decision about the closeness between the given name pairs. A fuzzy logic is formulated by a set of linguistic variables based on feature’s score value. An entity pair’s association score values are calculated using string and link-based features like Hamming Distance, Leventein Distance, Normalized String Edit Distance, Common Friends, Normalized Dot Product and Co-occurrence Relevance and an output variable accuracy as closeness. These features are transformed into fuzzy input variables and designed with proper membership functions. The proposed novel fuzzy inference system gives the decision of aliases closeness in the form of crisp values ranging from 0 to 1. In this work, the model achieves upto 90% accuracy compared to estimated accuracy.