Exploiting Publication Contents and Collaboration Networks for Collaborator Recommendation.
Ontology highlight
ABSTRACT:
SUBMITTER: Kong X
PROVIDER: S-EPMC4743965 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Information technology (IT) adoption enables biomedical research. Publications are an accepted measure of research output, and network models can describe the collaborative nature of publication. In particular, ecological networks can serve as analogies for publication and technology adoption. We constructed network models of adoption of bioinformatics programming languages and health IT (HIT) from the literature.We selected seven programming languages and four types of HIT. We performed PubMed searches to identify publications since 2001. We calculated summary statistics and analyzed spatiotemporal relationships. Then, we assessed ecological models of specialization, cooperativity, competition, evolution, biodiversity, and stability associated with publications.Adoption of HIT has been variable, while scripting languages have experienced rapid adoption. Hospital systems had the largest HIT research corpus, while Perl had the largest language corpus. Scripting languages represented the largest connected network components. The relationship between edges and nodes was linear, though Bioconductor had more edges than expected and Perl had fewer. Spatiotemporal relationships were weak. Most languages shared a bioinformatics specialization and appeared mutualistic or competitive. HIT specializations varied. Specialization was highest for Bioconductor and radiology systems. Specialization and cooperativity were positively correlated among languages but negatively correlated among HIT. Rates of language evolution were similar. Biodiversity among languages grew in the first half of the decade and stabilized, while diversity among HIT was variable but flat. Compared with publications in 2001, correlation with publications one year later was positive while correlation after ten years was weak and negative.Adoption of new technologies can be unpredictable. Spatiotemporal relationships facilitate adoption but are not sufficient. As with ecosystems, dense, mutualistic, specialized co-habitation is associated with faster growth. There are rapidly changing trends in external technological and macroeconomic influences. We propose that a better understanding of how technologies are adopted can facilitate their development.
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Project description:The very notion of social network implies that linked individuals interact repeatedly with each other. This notion allows them not only to learn successful strategies and adapt to them, but also to condition their own behavior on the behavior of others, in a strategic forward looking manner. Game theory of repeated games shows that these circumstances are conducive to the emergence of collaboration in simple games of two players. We investigate the extension of this concept to the case where players are engaged in a local contribution game and show that rationality and credibility of threats identify a class of Nash equilibria--that we call "collaborative equilibria"--that have a precise interpretation in terms of subgraphs of the social network. For large network games, the number of such equilibria is exponentially large in the number of players. When incentives to defect are small, equilibria are supported by local structures whereas when incentives exceed a threshold they acquire a nonlocal nature, which requires a "critical mass" of more than a given fraction of the players to collaborate. Therefore, when incentives are high, an individual deviation typically causes the collapse of collaboration across the whole system. At the same time, higher incentives to defect typically support equilibria with a higher density of collaborators. The resulting picture conforms with several results in sociology and in the experimental literature on game theory, such as the prevalence of collaboration in denser groups and in the structural hubs of sparse networks.
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Project description:The development of Web 2.0 and the rapid growth of available data have led to the development of systems, such as recommendation systems (RSs), that can handle the information overload. However, RS performance is severely limited by sparsity and cold-start problems. Thus, this paper aims to alleviate these problems. To realize this objective, a new model is proposed by integrating three sources of information: a user-item matrix, explicit and implicit relationships. The core strategy of this study is to use the multi-step resource allocation (MSRA) method to identify hidden relations in social information. First, explicit social information is used to compute the similarity between each pair of users. Second, for each non-friend pair of users, the MSRA method is applied to determine the probability of their relation. If the probability exceeds a threshold, a new relationship will be established. Then, all sources are incorporated into the Singular Value Decomposition (SVD) method to compute the missing prediction values. Furthermore, the stochastic gradient descent technique is applied to optimize the training process. Additionally, two real datasets, namely, Last.Fm and Ciao, are utilized to evaluate the proposed method. In terms of accuracy, the experiment results demonstrate that the proposed method outperforms eight state-of-the-art approaches: Heats, PMF, SVD, SR, EISR-JC, EISR-CN, EISR-PA and EISR-RAI.
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Project description:Globally, the current coronavirus disease 2019 (COVID-19) pandemic is resulting in high fatality rates. Consequently, the prevention of further transmission is very vital. Until vaccines are widely available, the only available infection prevention methods include the following: contact tracing, case isolation and quarantine, social (physical) distancing, and hygiene measures (washing of hands with soap and water and using alcohol-based hand sanitizers). Contact tracing, which is key in preventing the spread of COVID-19, refers to the process of finding unreported people who maybe infected by using a verified case to trace back possible infections of contacts. Consequently, the wide and fast spread of COVID-19 requires computational approaches which utilize innovative algorithms that build a memory of proximity contacts of cases that are positive. In this paper, a recommender algorithm called socially aware recommendation of people probably infected with COVID-19 (SARPPIC) is proposed. SARPPIC initially utilizes betweenness centrality in a social network to measure the number of target contact points (nodes/users) who have come into contact with an infected contact point (COVID-19 patient). Then, using contact durations and contact frequencies, tie strengths of the same contact points above are also computed. Finally, the above algorithmic computations are hybridized through profile integration to generate results for effective contact tracing recommendations of possible COVID-19-infected patients who will require testing in a healthcare facility. Benchmarking experimental results in the paper demonstrate that, using two interconnected relevant real-world datasets, SARPPIC outperforms other relevant methods in terms of suitable evaluation metrics such as precision, recall, and F-measure.
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Project description:To ascertain attitudes to resourcing, collaboration and publication in toxicology, a survey was developed and distributed to British Toxicology Society (BTS) members. The survey comprised 14 questions with 5 response options (strongly agree; agree; conflicted; disagree; strongly disagree) and a free text box. One hundred completed surveys were received by the cut-off date for data analysis. Unsurprisingly, 60% of participants disagreed or strongly disagreed that toxicology research is adequately funded in the UK; only 12% agreed with this statement. A similar proportion of participants (53%) disagreed with the statement that funding councils give equal opportunity to toxicology whereas 31% were conflicted on this point. An overwhelming 97% of respondents agreed that collaboration is important in driving toxicology research whereas only 38% agreed that competition is important. When this question was broadened out beyond the discipline of toxicology, a similar profile was seen suggesting that participants held similar views on toxicology versus other types of research. Many respondents were conflicted regarding the role of competition both in toxicology and in other research disciplines. Free text comments suggested that some competition is good to drive quality but can be counterproductive when competing for limited resources. Most participants were in favour of making toxicology research data openly available (86%) and in favour of open access publication (89%) although there were reservations about the cost of open access. Many (60%) thought the current system of peer review is fair but 65% also supported the idea of double-blind peer review (where both reviewer and author are anonymized). Others suggested a step in the opposite direction towards increased transparency (revealing and holding reviewers to account) would be preferable. Overall, there was a broad theme in free text responses that the need for experienced toxicologists has increased at a time when training and investment in the discipline has declined. However, not all respondents held that view with some noting that toxicology both as a research and as an applied discipline is strong within the UK scientific community.
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Project description:Today, many complex tasks are assigned to teams, rather than individuals. One reason for teaming up is expansion of the skill coverage of each individual to the joint team skill set. However, numerous empirical studies of human groups suggest that the performance of equally skilled teams can widely differ. Two natural question arise: What are the factors defining team performance? and How can we best predict the performance of a given team on a specific task? While the team members' task-related capabilities constrain the potential for the team's success, the key to understanding team performance is in the analysis of the team process, encompassing the behaviors of the team members during task completion. In this study, we extend the existing body of research on team process and prediction models of team performance. Specifically, we analyze the dynamics of historical team performance over a series of tasks as well as the fine-grained patterns of collaboration between team members, and formally connect these dynamics to the team performance in the predictive models. Our major qualitative finding is that higher performing teams have well-connected collaboration networks-as indicated by the topological and spectral properties of the latter-which are more robust to perturbations, and where network processes spread more efficiently. Our major quantitative finding is that our predictive models deliver accurate team performance predictions-with a prediction error of 15-25%-on a variety of simple tasks, outperforming baseline models that do not capture the micro-level dynamics of team member behaviors. We also show how to use our models in an application, for optimal online planning of workload distribution in an organization. Our findings emphasize the importance of studying the dynamics of team collaboration as the major driver of high performance in teams.
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Project description:For almost four decades, cooperation has been studied through the lens of the prisoner's dilemma game, with cooperation modelled as the play of a specific strategy. However, an alternative approach to cooperative behavior has recently been proposed. Known as collaboration, the new approach considers mutualistic strategic choice and can be applied to any game. Here, we bring these approaches together and study the effect of collaboration on cooperative dynamics in the standard prisoner's dilemma setting. It turns out that, from a baseline of zero cooperation in the absence of collaboration, even relatively rare opportunities to collaborate can support material, and robust, levels of cooperation. This effect is mediated by the interaction structure, such that collaboration leads to greater levels of cooperation when each individual strategically interacts with relatively few other individuals, matching well-known characteristics of human interaction networks. Conversely, collaboratively induced cooperation vanishes from dense networks, thus placing environmental limits on collaboration's successful role in cooperation.
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Project description:Much empirical evidence shows that individuals usually exhibit significant homophily in social networks. We demonstrate, however, skill complementarity enhances heterophily in the formation of collaboration networks, where people prefer to forge social ties with people who have professions different from their own. We construct a model to quantify the heterophily by assuming that individuals choose collaborators to maximize utility. Using a huge database of online societies, we find evidence of heterophily in collaboration networks. The results of model calibration confirm the presence of heterophily. Both empirical analysis and model calibration show that the heterophilous feature is persistent along the evolution of online societies. Furthermore, the degree of skill complementarity is positively correlated with their production output. Our work sheds new light on the scientific research utility of virtual worlds for studying human behaviors in complex socioeconomic systems.
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Project description:Amid an increasingly demanding research environment, there has been a growing interest in studies concerning Research Integrity and Research Ethics (RIRE). Between 1990 and 2020, over 9700 publications were published to address problematic research conduct such as falsification, plagiarism, and related protocols and standards. In this work, country-level trends and collaborative structures are examined with respect to economic group. Our results showed that RIRE publications are predominantly led by the West, with North America and Western Europe contributing the most. While there is interest within growing economies such as China, the pace is not comparable to its overall publications. However, international collaborations on RIRE grew to account for nearly 30% of all publications on the subject in 2020. Although there is a stronger preference for high income countries to collaborate with other high income countries, we observe a rise in partnerships between high-/middle-income and middle-/lower-income co-authorship pairs in the last decade. These trends point to a maturing global community with distributed knowledge transfer, towards more unified international standards for research ethics and integrity.Supplementary informationThe online version contains supplementary material available at 10.1007/s11192-022-04400-y.
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Project description:This paper explores the evolution of research collaboration networks in the 'stakeholder theory and management' (STM) discipline and identifies the longitudinal effect of co-authorship networks on research performance, i.e., research productivity and citation counts. Research articles totaling 6,127 records from 1989 to 2020 were harvested from the Web of Science Database and transformed into bibliometric data using Bibexcel, followed by applying social network analysis to compare and analyze scientific collaboration networks at the author, institution and country levels. This work maps the structure of these networks across three consecutive sub-periods (t1: 1989-1999; t2: 2000-2010; t3: 2011-2020) and explores the association between authors' social network properties and their research performance. The results show that authors collaboration network was fragmented all through the periods, however, with an increase in the number and size of cliques. Similar results were observed in the institutional collaboration network but with less fragmentation between institutions reflected by the increase in network density as time passed. The international collaboration had evolved from an uncondensed, fragmented and highly centralized network, to a highly dense and less fragmented network in t3. Moreover, a positive association was reported between authors' research performance and centrality and structural hole measures in t3 as opposed to ego-density, constraint and tie strength in t1. The findings can be used by policy makers to improve collaboration and develop research programs that can enhance several scientific fields. Central authors identified in the networks are better positioned to receive government funding, maximize research outputs and improve research community reputation. Viewed from a network's perspective, scientists can understand how collaborative relationships influence research performance and consider where to invest their decision and choices.
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