Communities and patterns of scientific collaboration

This is the author's accepted version of this article deposited at arXiv (arXiv:1006.1788v2 [physics.soc-ph]) and subsequently published in Scientometrics October 2011, Volume 89, Issue 1, pp 381-396. The final publication is available at link.springer.com http://link.springer.com/article/10.1007%2Fs11192-011-0439-1Author's note: 17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)17 pages. To appear in special edition of Scientometrics. Abstract on arXiv meta-data a shorter version of abstract on actual paper (both in journal and arXiv full paper version)This paper investigates the role of homophily and focus constraint in shaping collaborative scientific research. First, homophily structures collaboration when scientists adhere to a norm of exclusivity in selecting similar partners at a higher rate than dissimilar ones. Two dimensions on which similarity between scientists can be assessed are their research specialties and status positions. Second, focus constraint shapes collaboration when connections among scientists depend on opportunities for social contact. Constraint comes in two forms, depending on whether it originates in institutional or geographic space. Institutional constraint refers to the tendency of scientists to select collaborators within rather than across institutional boundaries. Geographic constraint is the principle that, when collaborations span different institutions, they are more likely to involve scientists that are geographically co-located than dispersed. To study homophily and focus constraint, the paper will argue in favour of an idea of collaboration that moves beyond formal co-authorship to include also other forms of informal intellectual exchange that do not translate into the publication of joint work. A community-detection algorithm is applied to the co-authorship network of the scientists that submitted in Business and Management in the 2001 UK RAE. While results only partially support research-based homophily, they indicate that scientists use status positions for discriminating between potential partners by selecting collaborators from institutions with a rating similar to their own. Strong support is provided in favour of institutional and geographic constraints. Scientists tend to forge intra-institutional collaborations; yet, when they seek collaborators outside their own institutions, they tend to select those who are in geographic proximity

Transitive reduction of citation networks

In many complex networks, the vertices are ordered in time, and edges represent causal connections. We propose methods of analysing such directed acyclic graphs taking into account the constraints of causality and highlighting the causal structure. We illustrate our approach using citation networks formed from academic papers, patents and US Supreme Court verdicts. We show how transitive reduction (TR) reveals fundamental differences in the citation practices of different areas, how it highlights particularly interesting work, and how it can correct for the effect that the age of a document has on its citation count. Finally, we transitively reduce null models of citation networks with similar degree distributions and show the difference in degree distributions after TR to illustrate the lack of causal structure in such models

Linking the network centrality measures closeness and degree

We propose a non-linear relationship between two of the most important measures of centrality in a network: degree and closeness. Based on a shortest-path tree approximation, we give an analytic derivation that shows the inverse of closeness is linearly dependent on the logarithm of degree. We show that our hypothesis works well for a range of networks produced from stochastic network models including the Erdos-Reyni and Barabasi-Albert models. We then test our relation on networks derived from a wide range of real-world data including social networks, communication networks, citation networks, co-author networks, and hyperlink networks. We find our relationship holds true within a few percent in most, but not all, cases. We suggest some ways that this relationship can be used to enhance network analysis

Making communities show respect for order

In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms

Temporal Evolution Of Universal Performance Indicators For Academic Publication

We show universal behaviour for two indicators of the quality of publications taken from two different data sets, papers from a single institution and those on arXiv. We demonstrate this universality for different years and subjects. This distribution is well fitted by a lognormal with a variance of around 1.3, consistent with Radicchi et al (2008). We will also discuss the evolution over time of our measures describing the data and note that simple models do not have the correct temporal behaviour for our parameters. Based on arXiv:1110.3271 with additional new material. Poster given at ECCS 201

Dynamical analysis of spatial interaction models

We develop a novel dynamical method to examine spatial interaction models (SIMs). For each SIM, we use our dynamical framework to model emigration patterns. We look at the resulting population distributions to see if they are realistic or not. We use the US census data from 2010 and various spatial statistics to access the success or failure of each model. While we looked at over eighty different SIMs, we will focus here on two examples: the production constrained gravity model and the Radiation model. The results suggest that all these models fail to produce realistic population distributions and we identify the flaws within existing models. This leads us to suggest that we should define site attractiveness in terms of a second short range SIM leading to a new spatial interaction model - the Two-Trip model - which offers significant improvements when examined via our method. We also note that our Two-Trip adaptation can be used in any spatial modelling contexts, not just emigration

Uncovering space-independent communities in spatial networks

Accepted versio

Beyond theory:an examination of lean new product introduction practices in the UK

Interest is growing around the application of lean techniques to new product introduction (NPI). Although a relatively emergent topic compared with the application of ‘lean’ within the factory, since 2000 there has been an exponential rise in the literature on this subject. However, much of this work focuses on describing and extolling the virtues of the ‘Toyota approach’ to design. Therefore, by way of a stock take for the UK, the present authors' research has set out to understand how well lean product design practices have been adopted by leading manufacturers. This has been achieved by carrying out in-depth case studies with three carefully selected manufacturers of complex engineered products. This paper describes these studies, the detailed results and subsequent findings, and concludes that both the awareness and adoption of practices is generally embryonic and far removed from the theory advocated in the literature

Embedding graphs in Lorentzian spacetime

Geometric approaches to network analysis combine simply defined models with great descriptive power. In this work we provide a method for embedding directed acyclic graphs (DAG) into Minkowski spacetime using Multidimensional scaling (MDS). First we generalise the classical MDS algorithm, defined only for metrics with a Riemannian signature, to manifolds of any metric signature. We then use this general method to develop an algorithm which exploits the causal structure of a DAG to assign space and time coordinates in a Minkowski spacetime to each vertex. As in the causal set approach to quantum gravity, causal connections in the discrete graph correspond to timelike separation in the continuous spacetime. The method is demonstrated by calculating embeddings for simple models of causal sets and random DAGs, as well as real citation networks. We find that the citation networks we test yield significantly more accurate embeddings that random DAGs of the same size. Finally we suggest a number of applications in citation analysis such as paper recommendation, identifying missing citations and fitting citation models to data using this geometric approach