Friendship Links Research Articles

EMPIRICAL ANALYSIS OF ATTENTION BEHAVIORS IN ONLINE SOCIAL NETWORKS

Studying attention behavior has its social significance because such behavior is considered to lead the evolution of the friendship network. However, this type of behavior in social networks has attracted relatively little attention before, which is mainly because, in reality, such behaviors are always transitory and rarely recorded. In this paper, we collected the attention behaviors as well as the friendship network from Douban database and then carefully studied the attention behaviors in the friendship network as a latent metric space. The revealed similar patterns of attention behavior and friendship suggest that attention behavior may be the pre-stage of friendship to a certain extent, which can be further validated by the fact that pairwise nodes in Douban network connected by attention links beforehand are indeed far more likely to be connected by friendship links in the near future. This phenomenon can also be used to explain the high clustering of many social networks. More interestingly, it seems that attention behaviors are more likely to take place between individuals who have more mutual friends as well as more different friends, which seems a little different from the principles of many link prediction algorithms. Moreover, it is also found that forward attention is preferred to inverse attention, which is quite natural because, usually, an individual must be more interested in others that he is paying attention to than those paying attention to him. All of these findings can be used to guide the design of more appropriate social network models in the future.

Resource generation networks and innovation, in fragmented institutions

Networks are recognised as a significant conduit for the dissemination of innovations. An 'exposure' model of innovation suggests that the adoption of an innovation is more likely, the more that other members of one's personal network are using it, and this is a network learning model that is related to the phenomenon of communities of practice. However, van der Gaag and Snijders (2004) recognised that not all resources distributed throughout a social network would be as valuable in particular contexts as others. A resource generation model of network analysis starts from the position that what matters for the adoption of an innovation - a change in working practice - is whether individuals are in contact with people possessing knowledge, capacity or other relevant resources that can assist the adopter. This model forms the basis for the research project being described in this poster, one which aims at enhancing the resource generation networks within universities, vis-a-vis the dissemination of e-learning innovations, a task which many universities find difficult. Universities can partly engineer resource generation networks by providing helpdesks, staff development units, and so on, but while these may create network hubs to an extent, there is still a need for the more personal, organic networks of resource generation (communities of practice, friendship links, and weaker ties such as mutual acquaintance, familiarity with prior work and so on). These are likely to extend outside the institution. Both the engineering and organic approaches are challenged by the structure of universities generally, and fragmented ones particularly. It remains difficult to diffuse innovation across different parts of an organisation. This existing fragmentation is now exacerbated by the development of institutions with widely separated satellite campuses, including in different countries. The project is currently in the pilot stage, and has mapped the social networks of two self-contained but fragmented academic units: Manchester Business School (with 6 satellites around the world) and NCUK, a consortium of colleges in various countries. The first phase of the research has involved using a version of van der Gaag and Snijders' research instrument to ask respondents who they would go to for help solving problems with e-learning. I seek to confirm the hypothesis that in these case organizations, there will be clear 'cut points' in the network - individuals or small groups on whom the ability to use social networks to diffuse learning and innovation across the different parts of the organization will depend. Phase two will involve researching in a behavioural change intervention in one of the organizations, one targeted at key individuals or groups as defined above. Both organizations will then be re-examined after a year, checking the evolution of the resource generation networks over this period.

Investigating the Properties of a Social Bookmarking and Tagging Network

Social networks and collaborative tagging systems are rapidly gaining popularity as a primary means for storing and sharing data among friends, family, colleagues, or perfect strangers as long as they have common interests. del.icio.us3 is a social network where people store and share their personal bookmarks. Most importantly, users tag their bookmarks for ease of information dissemination and later look up. However, it is the friendship links that make del.icio.us a social network. They exist independently of the set of bookmarks that belong to the users and have no relation to the tags typically assigned to the bookmarks. To study the interaction among users, the strength of the existing links and their hidden meaning, we introduce implicit links in the network. These links connect only highly “similar” users. Here, similarity can reflect different aspects of the user’s profile that makes her similar to any other user, such as number of shared bookmarks, or similarity of their tags clouds. The authors investigate the question whether friends have common interests, they gain additional insights on the strategies that users use to assign tags to their bookmarks, and they demonstrate that the graphs formed by implicit links have unique properties differing from binomial random graphs or random graphs with an expected power-law degree distribution.

Using Transactional Information to Predict Link Strength in Online Social Networks

Many scientific fields analyzing and modeling social networks have focused on manually-collected datasets where the friendship links are sparse (due to the costs of collection) but relatively noise-free (i.e. they indicate strong relationships). In online social networks, where the notion of ``friendship'' is broader than what would generally be considered in sociological studies, the friendship links are denser but the links contain noisier information (i.e., some weaker relationships). However, the networks also contain additional transactional events among entities (e.g., communication, file transfers) that can be used to infer the true underlying social network. With this aim in mind, we develop a supervised learning approach to predict link strength from transactional information. We formulate this as a link prediction task and compare the utility of attribute-based, topological, and transactional features. We evaluate our approach on public data from the Purdue Facebook network and show that we can accurately predict strong relationships. Moreover, we show that transactional-network features are the most influential features for this task.

Comparing fashion process networks and friendship networks in small groups of adolescents

PurposeThe purpose of this paper is to gain a better understanding of the “fashion process” by exploring and comparing fashion‐process networks and friendship networks in small groups of adolescents, and to explore the factors involved in the causation of “clothing leadership” and “popularity” in such groups.Design/methodology/approachFashion‐process networks and friendship networks are investigated by sociometric nomination with respect to “clothing acceptance” and “social acceptance” in three age groups of Korean adolescents. The patterns and structures of the networks are analyzed using UCINET 6.0 and Netminer 2.6. Certain factors contributing to “clothing leadership” and “popularity” are measured using Likert‐type scales; these include “clothing interest,” “fashion interest,” and “perceived clothing acceptance.” Other factors of interest (such as “academic grade,” “facial attractiveness,” and “bodily attractiveness”) are rated by peer informants.FindingsFashion‐process networks are found to be more centralized and polarized than friendship networks. Clothing acceptance is related to peer acceptance and is found to occur within and across friendship links. Clothing leaders are not necessarily the most popular subjects. The most significant factor determining “clothing leadership” is found to be “facial attractiveness.”Research limitations/implicationsSocial network analysis is exploratory in nature and the results cannot be statistically verified. However, a better understanding of the characteristics of fashion‐process networks in small groups assists in bridging the gap between individual adoption of fashion at the micro‐level and fashion diffusion in society at the macro‐level.Practical implicationsBy illuminating the dynamics of the fashion process in small groups, the study is of assistance in developing marketing strategies that target adolescents.Originality/valueThe study is original in that fashion process in small groups of Korean adolescents is investigated by social network analysis of “clothing acceptance,” and then compared to friendship networks.

Collective Classification in Network Data

Many real‐world applications produce networked data such as the worldwide web (hypertext documents connected through hyperlinks), social networks (such as people connected by friendship links), communication networks (computers connected through communication links), and biological networks (such as protein interaction networks). A recent focus in machine‐learning research has been to extend traditional machine‐learning classification techniques to classify nodes in such networks. In this article, we provide a brief introduction to this area of research and how it has progressed during the past decade. We introduce four of the most widely used inference algorithms for classifying networked data and empirically compare them on both synthetic and real‐world data.

Social balance on networks: The dynamics of friendship and enmity

How do social networks evolve when both friendly and unfriendly relations exist? Here we propose a simple dynamics for social networks in which the sense of a relationship can change so as to eliminate imbalanced triads — relationship triangles that contains 1 or 3 unfriendly links. In this dynamics, a friendly link changes to unfriendly or vice versa in an imbalanced triad to make the triad balanced. Such networks undergo a dynamic phase transition from a steady state to “utopia”–all friendly links–as the amount of network friendliness, defined as the fraction of friendly links ρ , is changed. Basic features of the long-time dynamics and the phase transition are discussed.

FRIENDSHIP NETWORKS*

ABSTRACT This paper develops a model of social networks different from those presented in the recent literature. In contrast to existing models, the level of investment in link formation is a continuous decision variable, and links form stochastically rather than deterministically, with the probability depending on the noncooperative investment choices of both parties. Since the network structure is then stochastic rather than deterministic, the actual pattern of links cannot be specified, as in previous models, with the analysis focusing instead on which links are most likely to form. This alternate approach leads to a much simpler mathematical structure than in previous work. The analysis, which is couched in the context of friendship networks, shows that individual investment in friendship formation is too low. In addition, the analysis shows that, in an asymmetric setting where one individual has personal magnetism or a broad group of acquaintances, friendship links involving this attractive agent are most likely to form.

Link mining

Many datasets of interest today are best described as a linked collection of interrelated objects. These may represent homogeneous networks, in which there is a single-object type and link type, or richer, heterogeneous networks, in which there may be multiple object and link types (and possibly other semantic information). Examples of homogeneous networks include single mode social networks, such as people connected by friendship links, or the WWW, a collection of linked web pages. Examples of heterogeneous networks include those in medical domains describing patients, diseases, treatments and contacts, or in bibliographic domains describing publications, authors, and venues. Link mining refers to data mining techniques that explicitly consider these links when building predictive or descriptive models of the linked data. Commonly addressed link mining tasks include object ranking, group detection, collective classification, link prediction and subgraph discovery. While network analysis has been studied in depth in particular areas such as social network analysis, hypertext mining, and web analysis, only recently has there been a cross-fertilization of ideas among these different communities. This is an exciting, rapidly expanding area. In this article, we review some of the common emerging themes.

Dynamics of social balance on networks

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad--a triangular loop with one or three unfriendly links--is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to "paradise"--all links are friendly--as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Henry Sigerist and the History of Medicine in Latin America: His Correspondence with Juan R. Beltran

During the years of World War II, the American Association for the History of Medicine fostered a Pan-American policy aimed at establishing relationships with Latin American historians of medicine. Juan R. Beltrán, professor of history of medicine at the University of Buenos Aires, also pursued an energetic program of academic diplomacy. The correspondence between Henry Sigerist and Beltrán makes manifest that by 1941 good channels of communication were established between Baltimore and Buenos Aires, but the friendly links did not last long. The motives for this can be found in the competing aims of the AAHM and Beltrán, and the pattern of international relationships during the war years.

Giuseppe Prota (1938–2003)

Giuseppe Prota (1938–2003)

The Evolution of Russian-Korean Relations: External and Internal Factors

The Korean Peninsula was in the focus of Russia's attention throughout the twentieth century. Tsar Nicolas II, Lenin, Stalin, Brezhnev, and finally Gorbachev, for various reasons and in different forms, tried to get the upperhand in this East Asian nation. Gorbachev began with attempts to strengthen solidarity with communist comrades in North Korea and ended up forging friendly links with Seoul at the expense of the alliance with Pyongyang. After collapse of the communist regime in Moscow and then disintegration of the USSR, Russia's policies toward Korea (and Asia as a whole) for some time lost their momentum. The new Russian government was too preoccupied with internal problems, and any energy left for diplomacy was devoted to relations with the former parts of the Soviet Union and with the West. It was believed that the future of the democratic, anti-communist Russian state depended on the West for the disarmament process, the procurement of aid, and utilization of models of development. Gradually though, perceptions started to change. Economic interests and political issues were making Moscow more and more active in Asia. By the second half of 1992, a number of important directions emerged in Russian policies in the area, among them cooperation with South Korea, which looked like a bright spot in Russia's interactions with Asian-Pacific nations. South Korean companies, interested in Russia's natural resources and its military and space technology, kept a high profile in the Russian market. Some of them continued to look at opportunities for major investment, and Moscow in turn solicited Korean capital and pressed for resumption of the $3 billion loan

New Thinking in Vietnamese Foreign Policy

The end of the Cold War has forced Vietnam to change its foreign policy orientation. Vietnam is today behaving like a responsible member of the international community more than a trouble maker encouraging communist revolution in the region. Its foreign policy is aimed primarily at promoting economic development in the country but Vietnamese leaders are also mindful of the country's security needs. With the loss of Soviet support, in the aftermath of the dissolution of the Soviet Union, the Vietnamese leadership is relying on diplomatic means to preserve the country's security. It is seeking friendship and economic links with a large number of countries, including the United States, Japan and France. The leadership, however, believes that Vietnam's security interests are better served by befriending ASEAN. It hopes to use ASEAN's influence and clout to ward off possible aggression, especially from China.

Forging Links of Friendship

Forging Links of Friendship

Exchange of People among International Companies: Problems and Benefits

As international companies have expanded throughout the world, they have increasingly served as an important vehicle for the transfer and exchange of people. However, the extent to which these exchanges have led to improved understanding and forging links of friendship and cooperation, or, conversely, to increased suspicion, resentment, and distrust, depends largely on the participants' ability to understand and adapt to each other's perspectives, goals, and cultural backgrounds. This ability can be developed through adequate special training prior to and during the exchanges. However, adequate training of this type has not been provided by international companies to personnel in volved in exchanges. Other factors affecting the outcome of such exchanges include the willingness of the people in volved to integrate into and become active members of the host community and the companies' policies concerning the hiring, training, compensation, and promotion of the host country nationals on the same basis as parent company managers. On the other hand, benefits resulting from these exchanges include training of host country employees and transfer to them of productive management, market ing, and financial knowledge, creation of new jobs, and in creased productive capabilities of the host countries. Espe cially important, given the training and sensitivities of the people involved, is the ability of international company managers to stimulate among people of diverse backgrounds and outlooks a cooperative working relationship.