Unveiling Scholarly Communities over Knowledge Graphs

Authors : Sahar Vahdati, Guillermo Palma, Rahul Jyoti Nath, Christoph Lange, Sören Auer, Maria-Esther Vidal

Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction.

In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present Korona, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks.

Korona implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly entities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply Korona to uncover co-authorship networks.

Results observed from our empirical evaluation suggest that exploiting semantics in scholarly knowledge graphs enables the identification of previously unknown relations between researchers.

By extending the ontology, these observations can be generalized to other scholarly entities, e.g., articles or institutions, for the prediction of other scholarly patterns, e.g., co-citations or academic collaboration.

URL : https://arxiv.org/abs/1807.06816

The Social Structure of Consensus in Scientific Review

Authors : Misha Teplitskiy, Daniel Acuna, Aida Elamrani-Raoult, Konrad Kording, James Evans

Personal connections between creators and evaluators of scientific works are ubiquitous, and the possibility of bias ever-present. Although connections have been shown to bias prospective judgments of (uncertain) future performance, it is unknown whether such biases occur in the much more concrete task of assessing the scientific validity of already completed work, and if so, why.

This study presents evidence that personal connections between authors and reviewers of neuroscience manuscripts are associated with biased judgments and explores the mechanisms driving the effect.

Using reviews from 7,981 neuroscience manuscripts submitted to the journal PLOS ONE, which instructs reviewers to evaluate manuscripts only on scientific validity, we find that reviewers favored authors close in the co-authorship network by ~0.11 points on a 1.0 – 4.0 scale for each step of proximity.

PLOS ONE’s validity-focused review and the substantial amount of favoritism shown by distant vs. very distant reviewers, both of whom should have little to gain from nepotism, point to the central role of substantive disagreements between scientists in different « schools of thought. »

The results suggest that removing bias from peer review cannot be accomplished simply by recusing the closely-connected reviewers, and highlight the value of recruiting reviewers embedded in diverse professional networks.

URL : https://arxiv.org/abs/1802.01270

Linking Behavior in the PER Coauthorship Network

Authors : Katharine A. Anderson, Matthew Crespi, Eleanor C. Sayre

There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities.

Work examining emerging social networks is rarer, simply because data is difficult to obtain in real time. In this paper, we use thirty years of data from an emerging scientific community to look at that crucial early stage in the development of a social network.

We show that when the field is very young, islands of individual researchers labored in relative isolation, and the co authorship network is disconnected. Thirty years later, rather than a cluster of individuals, we find a true collaborative community, bound together by a robust collaboration network.

However, this change did not take place gradually — the network remained a loose assortment of isolated individuals until the mid-2000s, when those smaller parts suddenly knit themselves together into a single whole.

In the rest of this paper, we consider the role of three factors in these observed structural changes: growth, changes in social norms, and the introduction of institutions such as field-specific conferences and journals.

We have data from the very earliest years of the field, and thus are able to observe the introduction of two different institutions: the first field-specific conference, and the first field-specific journals.

We also identify two relevant behavioral shifts: a discrete increase in co authorship coincident with the first conference, and a shift among established authors away from collaborating with outsiders, towards collaborating with each other. The interaction of these factors gives us insight into the formation of collaboration networks more broadly.

URL : https://arxiv.org/abs/1609.09339

Coauthorship networks: A directed network approach considering the order and number of coauthors

« In many scientific fields, the order of coauthors on a paper conveys information about each individual’s contribution to a piece of joint work. We argue that in prior network analyses of coauthorship networks, the information on ordering has been insufficiently considered because ties between authors are typically symmetrized. This is basically the same as assuming that each co-author has contributed equally to a paper. We introduce a solution to this problem by adopting a coauthorship credit allocation model proposed by Kim and Diesner (2014), which in its core conceptualizes co-authoring as a directed, weighted, and self-looped network. We test and validate our application of the adopted framework based on a sample data of 861 authors who have published in the journal Psychometrika. Results suggest that this novel sociometric approach can complement traditional measures based on undirected networks and expand insights into coauthoring patterns such as the hierarchy of collaboration among scholars. As another form of validation, we also show how our approach accurately detects prominent scholars in the Psychometric Society affiliated with the journal. »

URL : http://arxiv.org/abs/1503.00361