Inspired by empirical studies of networked systems such as the Internet, social networks, and biological networks, researchers have in recent years developed a variety of techniques and models to help us understand or predict the behavior of these systems. Here we review developments in this field, including such concepts as the small-world effect, degree distributions, clustering, network correlations, random graph models, models of network growth and preferential attachment, and dynamical processes taking place on networks.

Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link-prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large co-authorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures. 1

Using data from three bibliographic databases in biology, physics, and mathematics respectively, networks are constructed in which the nodes are scientists and two scientists are connected if they have coauthored a paper together. We use these networks to answer a broad variety of questions about collaboration patterns, such as the numbers of papers authors write, how many people they write them with, what the typical distance between scientists is through the network, and how patterns of collaboration vary between subjects and over time. We also summarize a number of recent results by other authors on coauthorship patterns.

models for counting links between university web sites

Please do not quote without the permission of the authors. Paper prepared for presentation at the Annual Meeting of the American Association

Using the Science Citation Index (CD-Rom version) for 1990 and 2000, this paper analyses international co-authorships. The paper presents the methodology for identifying these co-authorships. Analysis is presented on observed linkages at the global level and on regional bases. The architecture of the network is further explored using statistical methods and factor analysis to reveal intense relationships as well as the core members of a global network. Findings show that, in the 10 years between 1990 and 2000, the global network has expanded to include more nations and it has become more interconnected. Regional networks show emerging hubs. Within the global network, a core set of countries has expanded from six in 1990 to eight in 2000. Factor analysis suggests that large countries compete with each other for partners in the global network. We discuss implications for public policy of the rise of a global network of scientists operating somewhat independently of national interests.

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Using data from co-authorships at the international level in all fields of science in 1990 and 2000, and within six case studies at the sub-field level in 2000, different explanations for the growth of international collaboration in science and technology are explored. We find that few of the explanations within the literature can be supported by a detailed review of the data. To enable further exploration of the role of recognition and rewards as ordering mechanisms within the system, we apply new tools emerging from network science. These enquiries shows that the growth of international co-authorships can be attributed to self-organizing phenomenon based on preferential attachment (searching for recognition and reward) within networks of co-authors. The co-authorship links can be considered as a complex network with sub-dynamics involving features of both competition and cooperation. The analysis suggests that the growth of international collaboration is more likely to emerge from dynamics at the sub-field level operating in all fields of science, albeit under institutional constraints. Implications for the management of global scientific collaborations are explored.

We present a visual exploration of the field of human-computer interaction through the author and article metadata of four of its major conferences: the ACM conferences on Computer-Human Interaction (CHI), User Interface Software and Technology (UIST) and

Internationality as a concept is being applied ambiguously and erroneously, particularly in the world of academic journal publication where it is often used as a quality indicator. Although different qualitative criteria have been used by scientometrists in order to attempt a measure of internationality in various contexts, it is now clear that the literal definition of internationality is a minimal one while other proposed measures based on individual criteria fail to provide a complete and accurate assessment. As such, internationality remains to be defined 2. Here, we present a holistic approach to the problem based on fuzzy logic. We surveyed, critically-assessed and pruned the set of internationality criteria in the context of academic publishing, selecting those that are semantically precise and amenable to quantitative measure. We have tested the ability of each criterion to measure internationality by applying them to four thematically-connected journals from the field of Health and Clinical Psychology, using descriptive statistics and the Gini Coefficient. The results of this case study revealed that, in the absence of a method of numerically weighting the criteria, any measurement of internationality remains ambiguous and incorrect. We propose that internationality is best represented by a neuro-fuzzy system of fuzzy sets of the weighted criteria linked by fuzzy rules in a multi-layer perceptron, whose output defuzzification gives a new measure – a Journal Internationality Index akin to the Impact Factor for citations. Viewing internationality in this way as an approximated fuzzy function means a quantitative measure can be found while keeping intact its semantic rule origins and meaning.