Extending SharpGraphLib: Custom Layouts and MetricsSharpGraphLib is a flexible C# graph library designed to make working with nodes, edges, and graph algorithms straightforward. While its default layouts and built‑in metrics cover a wide range of use cases, real projects often need custom behavior — specialized layouts for domain-specific visualization, or bespoke metrics that capture the structural properties important to your application. This article walks through practical patterns and concrete examples for extending SharpGraphLib with custom layouts and metrics, covering design considerations, implementation strategies, performance tips, and testing approaches.
When and why to extend
Graphs are a universal data structure, but their visualization and analysis must reflect the domain:
- Domain-specific layouts improve readability (for example, biological pathways vs. social networks).
- Custom metrics reveal application‑relevant insights (e.g., temporal centrality, multi‑edge weighting).
- Performance considerations may require tailored algorithms for massive graphs.
Extending a library instead of reimplementing functionality saves time and leverages proven core features: storage, basic algorithms (BFS/DFS), serialization, etc.
Architecture and extension points
Before implementing extensions, inspect SharpGraphLib’s architecture and identify extension points:
- Graph model: node and edge classes, attributes/metadata support.
- Layout subsystem: how layouts are registered, layout lifecycle (initialize, iterate, finalize), coordinate storage on nodes.
- Metrics subsystem: existing metric interfaces, how results are stored and exposed (node/edge attributes, separate report objects).
- Event model and rendering pipeline: if visual updates or incremental layouts are needed.
A typical extension will implement one or more public interfaces (e.g., ILayout, IMetric) and register itself with the library’s factory or service locator.
Designing a custom layout
Key design decisions:
- Static vs. iterative: force‑based or constraint solvers need multiple iterations; hierarchical or radial layouts may be single‑pass.
- Determinism: do you need repeatable layouts? Seed PRNGs or deterministic orderings can help.
- Continuous vs. batched updates: interactive apps need continuous incremental updates.
- Constraints: support fixed nodes, node grouping, or forbidden regions.
Example use cases:
- Radial layout for tree‑like hierarchies with depth‑based ring placement.
- Domain‑specific constraint layout: e.g., arrange components left-to-right in a wiring schematic while preserving topological order.
- Geo‑anchored layout: mix geographic coordinates with topology-based local adjustments.
Implementation outline for an iterative force‑directed layout:
- Create a class implementing ILayout with methods: Initialize(Graph), Step(), and Finish().
- Store per-node state (position, velocity, mass). Use node attributes or an internal dictionary keyed by node id.
- Implement forces: attractive along edges, repulsive between nodes, optional gravity to center the graph.
- Use spatial partitioning (grid or quadtree) for O(n log n) or O(n) approximations of repulsive forces for large graphs.
- Expose parameters (spring constant, repulsion constant, timestep, damping) via a public configuration object.
- Support cancellation and incremental yielding so UI thread can remain responsive (e.g., Step returns after Xms or after a fixed iteration).
Code sketch (C# pseudocode):
public class ForceLayout : ILayout { public ForceLayout(ForceLayoutConfig config) { ... } public void Initialize(Graph g) { // allocate node states, set initial positions } public bool Step() { // compute forces, update velocities and positions // return true if more iterations are needed } public void Finish() { // finalize node coordinates into Graph node attributes } } Handling fixed nodes and constraints:
- Respect a node.IsFixed flag and skip position updates.
- For grouped nodes, apply intra-group attractive forces and treat group centroids as higher‑mass meta‑nodes.
- For forbidden regions, apply collision‑avoidance forces pushing nodes out of those zones.
Creating custom metrics
Metrics can be simple (degree) or complex (community stability over time). Good metric design:
- Define input scope: node-level, edge-level, or graph-level.
- Define when the metric runs: on-demand, incremental, on-change hooks.
- Decide output storage: annotate nodes/edges with attribute keys (e.g., “centrality:betweenness”) or return a report object.
Example metric: Time-Weighted Betweenness Centrality
- Use when edge weights evolve over time and older interactions should count less.
- Weight an edge e at time t by w(e)exp(-λ(now – t)).
- Compute betweenness using weighted shortest paths (Dijkstra), summing pairwise dependencies with time-weighted weights.
Implementation steps:
- Implement IMetric with Compute(Graph) returning MetricResult.
- Preprocess edge weights to apply temporal decay.
- Run an all-pairs or single-source betweenness algorithm depending on graph size (Brandes’ algorithm adapted for weights).
- Store results as node attributes and optionally export a ranked list.
Example metric structure:
public class TimeWeightedBetweenness : IMetric { public double Lambda { get; set; } public MetricResult Compute(Graph g) { // apply decay to edge weights // run weighted Brandes algorithm // return results } } For very large graphs:
- Sample node pairs or use approximation algorithms (e.g., randomized Brandes, sketching approaches).
- Consider streaming metrics that update as edges are added/removed.
Performance and memory considerations
- Use adjacency lists and avoid heavy per-edge object allocations in hot loops.
- For iterative layouts, reuse memory for vector/force buffers.
- For metrics like all-pairs computations, consider multi-threading (parallelizing Dijkstra runs) — ensure thread-safe access to graph structures or work on immutable snapshots.
- Provide configurable approximation knobs (sample size, max iterations, Barnes‑Hut theta) so users trade accuracy for speed.
Integration with rendering and UI
- Expose layout progress events (ProgressChanged, IterationCompleted) so UI can render intermediate states.
- Support snapping to grid or applying visual constraints post-layout (label overlap avoidance).
- Consider GPU acceleration: offload force computations to compute shaders for very large graphs if the rendering pipeline allows it.
Testing and validation
- Unit test small deterministic graphs to verify positions or metric values.
- Use property‑based tests: e.g., layout preserves connected component containment, or metrics obey monotonicity under edge weight scaling.
- Visual regression tests: capture SVG/bitmap output and compare with tolerances.
- Performance benchmarks on representative datasets and memory profiling.
Packaging and distribution
- Follow SharpGraphLib’s plugin conventions: implement the library interfaces and add metadata attributes for automatic discovery.
- Version and document public configuration options clearly.
- Provide simple sample apps and unit tests demonstrating typical use.
Example: Implementing a Radial Hierarchical Layout (step-by-step)
- Identify root(s) — allow user override or choose highest-degree node.
- Compute BFS levels (depth) from root.
- For each level k, place nodes uniformly on a circle of radius r0 + k*dr.
- To reduce edge crossings, order nodes on each ring by parent angular positions (compute parent angle and sort).
- Optionally apply a short local force relaxation to improve spacing.
Pseudocode:
var levels = BFSLevels(graph, root); foreach (level in levels) { var angleStep = 2 * Math.PI / level.Count; for (i=0; i<level.Count; i++) { node.Position = center + Polar(radius(level), i * angleStep + offsetForParent(node)); } } LocalRelaxation(); Real-world examples and use cases
- Network operations dashboard: geo-anchored layout + custom latency metric for routing hot‑spot detection.
- Bioinformatics: pathway layouts constrained to canonical left‑to‑right flow; metrics for pathway centrality under experimental conditions.
- Social analytics: radial ego networks with time-weighted influence scores.
Summary
Extending SharpGraphLib with custom layouts and metrics gives you the power to tailor graph visualization and analysis to domain needs. Key steps are understanding extension points, choosing appropriate algorithmic designs (iterative vs. static), handling performance via spatial acceleration and approximation, and integrating cleanly with rendering and UI. Provide configurable parameters, robust tests, and clear documentation so your extensions are reusable and maintainable.
If you want, I can: provide a complete C# implementation of a force-directed layout or time-weighted betweenness metric tailored to SharpGraphLib’s API — tell me which and share the library’s relevant interface signatures (ILayout, IMetric) if available.
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