Calculated Compression Results
This panel appears above the form so the latest submitted result is visible immediately.
Compression Stage Plot
Calculator Inputs
Detailed Results Table
| Metric | Value |
|---|---|
| Original Representation | Adjacency Matrix |
| Compressed Representation | CSR Sparse Row |
| Directed Graph | No |
| Weighted Graph | Yes |
| Nodes | 2400 |
| Edges | 8600 |
| Theoretical Max Edges | 2878800 |
| Graph Density | 0.002987 |
| Average Degree | 7.1667 |
| Vertex Identifier Bits | 12 |
| Compressed Nodes | 1560 |
| Compressed Edges | 3268 |
| Original Size | 5.83 MB (48,939,600.00 bits) |
| Structural Compressed Size | 12.87 KB (105,407.00 bits) |
| After Deduplication | 9.52 KB (78,001.18 bits) |
| After Entropy Coding | 6.47 KB (53,040.80 bits) |
| Metadata Overhead | 2,048 B |
| Final Compressed Size | 8.47 KB (69,424.80 bits) |
| Compression Ratio | 704.9296:1 |
| Space Savings | 99.86% |
| Absolute Savings | 5.83 MB (48,870,175.20 bits) |
| Bits per Edge Before | 5,690.6512 |
| Bits per Edge After | 21.2438 |
| Quality Retention | 92.00% |
| Compression Loss | 8.00% |
| Efficiency Index | 91.87 |
Interpretation Notes
Structure Impact
Structural compression reduced the graph to 1560 nodes and 3268 edges, which changes the base storage estimate before coding.
Savings Outlook
The current scenario yields 99.86% space savings with a quality retention estimate of 92.00%.
Practical Caution
Metadata, serialization rules, and storage alignment can raise final file size. Treat this tool as an engineering estimate rather than a byte-perfect filesystem measurement.
Example Data Table
| Dataset | Nodes | Edges | Output Format | Original Size | Compressed Size | Ratio | Savings |
|---|---|---|---|---|---|---|---|
| Road network | 12,000 | 18,400 | CSR Sparse Row | 1.43 MB | 412.87 KB | 3.55:1 | 71.77% |
| Citation graph | 5,500 | 23,900 | Edge List | 350.10 KB | 153.80 KB | 2.28:1 | 56.07% |
| Dense lab matrix | 1,600 | 620,000 | Adjacency Matrix | 2.44 MB | 1.08 MB | 2.26:1 | 55.74% |
| Social cluster map | 9,800 | 41,300 | CSR Sparse Row | 768.50 KB | 229.44 KB | 3.35:1 | 70.15% |
Formula Used
1) Maximum Edges
Directed: maxEdges = n × (n − 1)
Undirected: maxEdges = n × (n − 1) / 2
2) Density and Average Degree
Density: density = edges / maxEdges
Average degree: directed uses edges / n, while undirected uses 2 × edges / n
3) Identifier Bit Width
vertexBits = ceil(log2(n))
4) Base Storage Estimate
Adjacency Matrix: cells × cellBits
Edge List: edges × (2 × vertexBits + weightBits)
CSR Sparse Row: (n + 1) × vertexBits + edges × (vertexBits + weightBits)
5) Structural Compression
compressedNodes = n × (1 − nodeReduction / 100)
compressedEdges = edges × (1 − edgeReduction / 100)
6) Bit-Level Compression
afterDedup = structuralBits × dedupFactor
afterEntropy = afterDedup × entropyFactor
finalCompressedBits = afterEntropy + metadataBits
7) Final Metrics
compressionRatio = originalBits / finalCompressedBits
savingsPercent = (1 − finalCompressedBits / originalBits) × 100
efficiencyIndex = savingsPercent × qualityRetention / 100
How to Use This Calculator
- Enter the original node and edge counts for your graph.
- Choose the original storage format and the target compressed format.
- Turn on directed or weighted mode when needed.
- Set bits per weight for weighted graphs.
- Enter node reduction and edge reduction percentages to model graph coarsening.
- Adjust deduplication and entropy retained-bit percentages to reflect coding strength.
- Add metadata overhead for dictionaries, offsets, block headers, or file indexes.
- Use quality retention to reflect how much structural fidelity remains after compression.
- Click Calculate Compression to update the results, chart, and export-ready table.
FAQs
What does this calculator measure?
It estimates how much storage a graph may need before and after compression. It combines structural reduction, deduplication, entropy coding, and metadata overhead into one final size estimate.
What is graph compression in this model?
Graph compression reduces nodes, edges, or repeated patterns while preserving useful structure. This page models coarsening, repeated-pattern removal, and bit-level encoding efficiency in a practical way.
Why do representation types matter?
An adjacency matrix, edge list, and CSR sparse row format store information differently. Sparse graphs often compress better when stored in list-based or CSR structures.
What does the entropy factor mean?
It represents the percentage of bits remaining after entropy coding. Lower values mean stronger coding efficiency, while higher values indicate less benefit from symbol compression.
Why can savings become negative?
Negative savings mean the compressed result is larger than the original estimate. That can happen when metadata is high, reduction rates are weak, or the chosen output structure is inefficient.
How should I choose node reduction?
Use it to model community aggregation, quotient graphs, or structural merging. Larger values mean more aggressive compression, but usually imply greater information loss in the original graph.
Is this suitable for weighted graphs?
Yes. Weighted graphs require extra bits per stored connection. The calculator includes a weight-bit input so size estimates can reflect costlier edge attributes.
Can I use this for exact production storage planning?
Use it for strong estimation, comparison, and scenario analysis. Real storage will also depend on file headers, serialization rules, hardware alignment, and implementation details.