| As data visualizations go, force-directed graphs can be fun to play with; and intuitively insightful and informative – especially in an executive management dashboard . A force directed graph is a physics-based simulation of visual elements linked as a graph. Elements are acted on by defined forces setup in the visualization layout. Forces include:
- Repulsion or attraction between elements, subject to maintaining an approximate specified distance between elements;
- Attraction to centers of gravity; and
- Collision prevention.
The interplay of these defined forces make a force-directed graph a richly tactile tool for visualizing complex and dynamic linkages between concepts. E.g. As used here to visualize the complex operational linkages between key high level trading & regulatory concepts.
- Each node represents a concept i.e. a fundamental business/operational concept, or a class of similar regulations (e.g. CCAR and other capital adequacy stress-testing related regulations, as a class).
- Each edge represents the operational impact/linkage between two concepts – it may not suggest causality or directionality of impact. The strength of this impact is reflected in the attraction force between nodes.
- The size of a node is proportional to the impact-weighted sum-product of all operational linkages the node has with other nodes.
- Each node exerts a global repulsing force that is proportional to its size.
- Finally, the canvas itself exerts pinning forces – a central gravity-like force that prevents nodes drifting away from the center of the canvas, and a clustering non-central but local force that aims to keep the regulatory concepts roughly grouped together (similarly but more weakly clustered groups are: decision driver concepts, revenue & risk origination concepts, operational cost driver concepts, and don’t-go-bust-now concepts).
These forces are tied to weights derived from organizational data, and collectively provide the physics guiding how the nodes move when dragged around; reflecting not only the expected linkages between concepts, but also the client-specific organizational and operational (in)efficiencies around these concepts. i.e. Some concepts pull more (or less) weight than they should e.g. In this example Insight (as a function of Research and Predictive Modeling) would appear to affect Risk Appetite (as it should); unfortunately Risk Appetite (an expected fundamental driver) does not seem to pull much weight with the things it should. |
Edges (first 12 – example)
| source |
target |
summary |
impact |
| Liquidity CLAR/NSFR… |
Regulation |
Significant Impact:(Controls,Operations,Staff) |
0.6 |
| Liquidity CLAR/NSFR… |
Funding |
Significant Impact:(Asset Quality,Inventory,Funding model) |
0.8 |
| Liquidity CLAR/NSFR… |
Scenario |
Validate,Calibrate,Review:(Controls,Inputs,Model) |
0.6 |
| Liquidity CLAR/NSFR… |
Valuation |
Validate,Calibrate,Review,Narrative:(Controls,Model,Outputs) |
0.55 |
| Liquidity CLAR/NSFR… |
Risk Factors |
Minimal Net Impact |
0.5 |
| Liquidity CLAR/NSFR… |
IDs & Taxonomies |
Significant Impact:(Inputs,Inventory) |
0.5 |
| Liquidity CLAR/NSFR… |
Capital |
GSIB buffer capital: CET1 up to 4.5% |
0.65 |
| CCAR/DFAST/BofE/EBA |
Regulation |
Significant Impact:{Controls,Operations) |
0.85 |
| CCAR/DFAST/BofE/EBA |
Scenario |
Validate,Calibrate,Review:(Controls,Inputs,Model) |
0.5 |
| CCAR/DFAST/BofE/EBA |
IDs & Taxonomies |
Integration,Aggregation:(Finance,Risk) |
0.8 |
| CCAR/DFAST/BofE/EBA |
Risk Factors |
Validate,Calibrate,Review:(Controls,Inputs,Model) |
0.75 |
| CCAR/DFAST/BofE/EBA |
Price Model |
Validate,Calibrate,Review:(Controls,Inputs,Model) |
0.65 |
Impact of one concept on an another, a [0,1] range. May be estimated as a regression of y-predictors like cost changes, resource changes, skill upgrades. Determines the Strength of the Edge.
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