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Increasing performance in Force Directed Node Graphs for cancer support service provision profiling Adam Hastings, Research Assistant, Part-Time MbR, SDI, Abertay University Email: a520298@abertay.ac.uk
1. Introduction
3. Methodology
Whenever a cancer diagnosis is given in the UK, the patient is given the opportunity to fill out an electronic Holistic Needs Assessment (eHNA) from Macmillan Cancer Support [1]. This allows Macmillan to: • identify a patient's concerns • start a conversation about their needs • develop a Personalised Care and Support Plan • signpost to local support services.
Baseline FDNG: The initial FDNG used the in-built physics engine used by Unity. This worked well for small data sets, but once larger sets were visualised the performance of the program was poor. The key challenge is the computation required to determine the physics-based interactions between pairs of nodes. The baseline FDNG solves this through brute-force computation.
This has resulted inn interactive, Force-Directed Node Graph (FDNG) visualization of ‘big’ Macmillan datasets • FDNG was chosen as it doesn't require special knowledge of graph theory to understand, a user can quickly discover connections and then intuitively explore the data • Our big data is several 1000 nodes • Being interactive allows it to be more useful to users, with selection and filtering of data in real time being important Technical challenges • Big graphs = many node-to-node interactions to determine graph layout • Without optimisations it cannot be interactive as it will be compute intensive UX challenges • Big, highly connected force-directed graphs can be difficult to interpret
For initial algorithm prototyping, a 2D test environment was used that had Nodes interacting against each other. The number of different nodes was varied and the performances of both the Barnes-Hut Approximation Algorithm and brute force method of calculating each Node-to-Node physics calculation were compared for multiple numbers of nodes and interactions. The prototype was then converted to a 3D version for similar performance profiling 2D. Avg FPS as Node Number Increases
The format for this algorithm is as follows: •
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Construct a hierarchical spatial index (e.g., quadtree / octree)
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To visualise these data, a first step (by colleagues) is to find through Association Rule Mining connections that are embedded amongst the various data elements such as cancer type, concerns and demographics. Once this rule mining process was completed a visualisation tool has been prototyped using the Unity game engine that shows associations between many pairs of data elements.
The metrics recorded to show performance increase were frames per second (FPS) and CPU usage (percentage of total).
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3D. Avg FPS as Node Number Increases 120.00
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Figure 2: A diagram of how the spatial index of a Quadtree is formed [2]
Avg. FPS
The scale of the data that Macmillan receives from this eHNA process very large and this project is developing an interactive visualisation in order to profile service provision at the UK.
To optimise the visualisation an approximation algorithm is needed to speed up performance of the physics of Node – to – Node calculations. The Barnes-Hut Approximation was identified as a suitable approximation algorithm.
4. Results
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Calculate centers of mass for each cell of the tree
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Estimate N-Body forces [3] • Compute intensive part of the algorithm • Approximate the forces rather than computing every interaction • Treating clusters of faraway objects as single entities • The algorithm is fast because we do not need to individually examine any of the bodies in the group • The θ parameter is used to control and change the speed and accuracy of the simulation, with greater speed coming at the cost of accuracy • The standard value is 0.5 is used (with our system the layout just needs to look appealing and readable, so sacrificing accuracy for more performance with our interactions is not a major concern
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Figure 3: A comparison of avg. FPS when using Barnes-Hut vs Brute Force in a 2D and then 3D test environment
Figure 1: A FDNG of a smaller data set used to test the prototype Figure 4: A comparison of CPU usage (% of total) when using Barnes-Hut vs Brute Force in the full prototype using an eHNA data set
2. Aim and Objectives Aim: To implement, optimise and evaluate a force-directed node graph (FNDG) visualisation of these data Objectives
Initial FNDG visualisation for stakeholder feedback (on subset of data)
Optimize the node-node interactions using an approximation
Speed up the Edge calculations by offloading to GPU
Improve aesthetics of big graph through clustering techniques
Keywords: Force Directed Node Graphs, Unity 3D, Information Graphics, 3D Graphs
5. Future Work
Now that the Node-to-Node calculations have been improved the calculations that determine, and update Edge length can be offloaded to GPU to improve performance further
Aesthetics through clustering – The bigger data sets require a visual clarity update; we have identified clustering as a potential way of alleviating this
Optionally, there is also a Virtual Reality version of this prototype using the Oculus Rift headset. This work is currently on hold for the testing process to be opened again. It does feature all the performance upgrades of the Barnes-Hut algorithm
Figure 3: An example of the Barnes-Hut algorithm applied to a 3D collection of Nodes, showing how the Octree is formed
[1] Macmillan Cancer Support – https://www.macmillan.org.uk (2021) Accessed from: https://www.macmillan.org.uk/healthcareprofessionals/innovation-in-cancer-care/holistic-needs-assessment [2] Diagrams from: Samzidat Drafting Co. - https://samizdat.co/ (2011). Accessed from: http://arborjs.org/docs/barnes-hut [3] BH N-Body Format Explanation from J.Heer https://homes.cs.washington.edu/~jheer/ (2020) Accessed from: https://jheer.github.io/barnes-hut/
Abertay University is an operating name of the University of Abertay Dundee, a charity registered in Scotland, No: SC016040.