on 20 Jan 2020 12:00 AM

Visualizing evolution of network graphs in Open Science VREs

Visualizing a network graph is of high relevance for outbreak investigations and also emerging risk identification using network science approaches. Capturing changes that may exist in the structure of a network can be greatly facilitated by time-evolving visualization of the network.

In AgInfra+ Food Safety Risk Assessment Community VRE, the case is that network graphs have thousands of nodes that evolve over time under a multitude of perspectives. This makes evident that the visualization approach must deal with the selection of data elements that are significant for the case and exploit more visualization features in order to highlight potentially significant information to the scientist. Such features may include, the size, color and position of nodes, the color and weight of edges and labels.

For confronting the challenge, the Graph Network visualization component has been delivered as part of the Open Science Visualisation Framework in AGINFRA PLUS and delivered as part of the VRE services suite offered to project’s research communities.

The Graph Network visualization component accepts network data in json form assuming specific semantics for its elements, representing nodes, edges, time, location and other additional metadata (e.g. production type). The user interface allows the import of such datasets, and subsequently the filtering of those, according to predefined criteria. Once choices are finalized, the evolution of the network may be displayed on the browser.

For the rendering, a backend service fetches user preferences and after filtering elements (nodes and edges) tries to allocate node positions in a manner that nodes will remain mostly stable throughout the animation – though this is not always feasible. Subsequently delivers to the front-end series of nodes and edges and their coloring and sizing characteristics which are drawn from their weight features. In the browser, using pure HTML5 technology, the user can control the playback and may be also save it as a video file (mp4) in his/her desktop for any further use.

The following series of images visualizes a part of an animation where new nodes emerge in each frame due to selection rules set by the user.



Technology wise, the Graph Network element utilizes neo4j graph database at the backend for performing queries, a custom java-based backend performs all calculations necessary  while the rendering of nodes in the browser is performed by React-D3-Graph. All software is provided under FOSS licenses.






The selected AGINFRA+ Use Cases will illustrate the benefits of applying the Science as a Service approach to pressing research questions from the corresponding research communities.


This community focuses on use cases aim to support the workflow of researchers, intermediaries and business analysts working on crop modelling, crop phenology estimation and yield forecasting, as well as related activities in the area of policy and decision support in food security, farm management advice and related activities.



This community focuses on use cases to support scientists in the multidisciplinary field of risk assessment and emerging risk identification as there is currently a strong need to create new technology-supported solutions that facilitate the knowledge integration processes relevant for these tasks.



This community focuses on use cases related to the high-throughput phenotyping large amount of data which need to be analyzed immediately for decision making. This aims to support phenomics researchers to select plant species and varieties which are the most adapted to specific environments and to global changes.


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