Ontology-Based Recommendation of Editorial Products

Major academic publishers need to be able to analyse their vast catalogue of products and select the best items to be marketed in scientific venues. This is a complex exercise that requires characterising with a high precision the topics of thousands of books and matching them with the interests of the relevant communities. In Springer Nature, this task has been traditionally handled manually by publishing editors. However, the rapid growth in the number of scientific publications and the dynamic nature of the Computer Science landscape has made this solution increasingly inefficient. We have addressed this issue by creating Smart Book Recommender (SBR), an ontology-based recommender system developed by The Open University (OU) in collaboration with Springer Nature, which supports their Computer Science editorial team in selecting the products to market at specific venues. SBR recommends books, journals, and conference proceedings relevant to a conference by taking advantage of a semantically enhanced representation of about 27K editorial products. This is based on the Computer Science Ontology, a very large-scale, automatically generated taxonomy of research areas. SBR also allows users to investigate why a certain publication was suggested by the system. It does so by means of an interactive graph view that displays the topic taxonomy of the recommended editorial product and compares it with the topic-centric characterization of the input conference. An evaluation carried out with seven Springer Nature editors and seven OU researchers has confirmed the effectiveness of the solution

Intrinsically Dynamic Network Communities

Community finding algorithms for networks have recently been extended to dynamic data. Most of these recent methods aim at exhibiting community partitions from successive graph snapshots and thereafter connecting or smoothing these partitions using clever time-dependent features and sampling techniques. These approaches are nonetheless achieving longitudinal rather than dynamic community detection. We assume that communities are fundamentally defined by the repetition of interactions among a set of nodes over time. According to this definition, analyzing the data by considering successive snapshots induces a significant loss of information: we suggest that it blurs essentially dynamic phenomena - such as communities based on repeated inter-temporal interactions, nodes switching from a community to another across time, or the possibility that a community survives while its members are being integrally replaced over a longer time period. We propose a formalism which aims at tackling this issue in the context of time-directed datasets (such as citation networks), and present several illustrations on both empirical and synthetic dynamic networks. We eventually introduce intrinsically dynamic metrics to qualify temporal community structure and emphasize their possible role as an estimator of the quality of the community detection - taking into account the fact that various empirical contexts may call for distinct `community' definitions and detection criteria.Comment: 27 pages, 11 figure

Characterizing Distances of Networks on the Tensor Manifold

At the core of understanding dynamical systems is the ability to maintain and control the systems behavior that includes notions of robustness, heterogeneity, or regime-shift detection. Recently, to explore such functional properties, a convenient representation has been to model such dynamical systems as a weighted graph consisting of a finite, but very large number of interacting agents. This said, there exists very limited relevant statistical theory that is able cope with real-life data, i.e., how does perform analysis and/or statistics over a family of networks as opposed to a specific network or network-to-network variation. Here, we are interested in the analysis of network families whereby each network represents a point on an underlying statistical manifold. To do so, we explore the Riemannian structure of the tensor manifold developed by Pennec previously applied to Diffusion Tensor Imaging (DTI) towards the problem of network analysis. In particular, while this note focuses on Pennec definition of geodesics amongst a family of networks, we show how it lays the foundation for future work for developing measures of network robustness for regime-shift detection. We conclude with experiments highlighting the proposed distance on synthetic networks and an application towards biological (stem-cell) systems.Comment: This paper is accepted at 8th International Conference on Complex Networks 201

The Role of Landscape Connectivity in Planning and Implementing Conservation and Restoration Priorities. Issues in Ecology

Landscape connectivity, the extent to which a landscape facilitates the movements of organisms and their genes, faces critical threats from both fragmentation and habitat loss. Many conservation efforts focus on protecting and enhancing connectivity to offset the impacts of habitat loss and fragmentation on biodiversity conservation, and to increase the resilience of reserve networks to potential threats associated with climate change. Loss of connectivity can reduce the size and quality of available habitat, impede and disrupt movement (including dispersal) to new habitats, and affect seasonal migration patterns. These changes can lead, in turn, to detrimental effects for populations and species, including decreased carrying capacity, population declines, loss of genetic variation, and ultimately species extinction. Measuring and mapping connectivity is facilitated by a growing number of quantitative approaches that can integrate large amounts of information about organisms’ life histories, habitat quality, and other features essential to evaluating connectivity for a given population or species. However, identifying effective approaches for maintaining and restoring connectivity poses several challenges, and our understanding of how connectivity should be designed to mitigate the impacts of climate change is, as yet, in its infancy. Scientists and managers must confront and overcome several challenges inherent in evaluating and planning for connectivity, including: •characterizing the biology of focal species; •understanding the strengths and the limitations of the models used to evaluate connectivity; •considering spatial and temporal extent in connectivity planning; •using caution in extrapolating results outside of observed conditions; •considering non-linear relationships that can complicate assumed or expected ecological responses; •accounting and planning for anthropogenic change in the landscape; •using well-defined goals and objectives to drive the selection of methods used for evaluating and planning for connectivity; •and communicating to the general public in clear and meaningful language the importance of connectivity to improve awareness and strengthen policies for ensuring conservation. Several aspects of connectivity science deserve additional attention in order to improve the effectiveness of design and implementation. Research on species persistence, behavioral ecology, and community structure is needed to reduce the uncertainty associated with connectivity models. Evaluating and testing connectivity responses to climate change will be critical to achieving conservation goals in the face of the rapid changes that will confront many communities and ecosystems. All of these potential areas of advancement will fall short of conservation goals if we do not effectively incorporate human activities into connectivity planning. While this Issue identifies substantial uncertainties in mapping connectivity and evaluating resilience to climate change, it is also clear that integrating human and natural landscape conservation planning to enhance habitat connectivity is essential for biodiversity conservation

Is the European R&D network homogeneous? spatial interaction modeling of network communities determined using graph theoretic methods

Interactions between firms, universities, and research organizations are crucial for successful innovation in the modern knowledge-based economy. Systems of such interactions constitute R&D networks. R&D networks may be meaningful segmented using recent methods for identifying communities, subnetworks whose members are more tightly linked to one another than to other members of the network. In this paper, we identify such communities in the European R&D network using data on joint research projects funded by the fifth European Framework Programme. We characterize the identified communities according to their thematic orientation and spatial structure. By means of a Poisson spatial interaction model, we estimate the impact of various separation factors – such as geographical distance – on the variation of cross-region collaboration activities in a given community. The European coverage is achieved by using data on 255 NUTS-2 regions of the 25 pre-2007 EU member-states, as well as Norway and Switzerland. The results demonstrate that European R&D networks are not homogeneous, instead showing relevant community substructures with distinct thematic and spatial properties.