# Technical Analysis of Algorithm Performance Across Datasets
## Overview
The image presents five comparative line graphs (a-e) evaluating the performance of six graph neural network algorithms (SDGNN, SiGAT, SGCN, GAP, LSNE, ASGL) across different datasets. Performance is measured using Area Under the Curve (AUC) against varying privacy budgets (ε). All graphs share identical axis labels and legend structure.
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## Legend & Color Mapping
Legend located at the top of all graphs:
- **SDGNN**: Gray line with circle markers
- **SiGAT**: Pink line with circle markers
- **SGCN**: Blue line with circle markers
- **GAP**: Green line with square markers
- **LSNE**: Yellow line with diamond markers
- **ASGL (Proposed)**: Red line with triangle markers
Spatial grounding: Legend occupies the top 15% of the image area, aligned horizontally.
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## Dataset-Specific Analysis
### (a) Bitcoin_Alpha
**Key Trends**:
1. ASGL (red) demonstrates the steepest ascent, increasing from 0.75 (ε=1) to 0.85 (ε=6)
2. SiGAT (pink) shows consistent growth, reaching 0.80 at ε=6
3. SDGNN (gray) maintains moderate performance, plateauing at 0.78
4. LSNE (yellow) exhibits the slowest improvement, ending at 0.72
**Data Points**:
| ε | SDGNN | SiGAT | SGCN | GAP | LSNE | ASGL |
|---|-------|-------|------|-----|------|------|
| 1 | 0.65 | 0.70 | 0.55 | 0.50| 0.52 | 0.75 |
| 2 | 0.68 | 0.72 | 0.60 | 0.55| 0.55 | 0.78 |
| 3 | 0.70 | 0.74 | 0.65 | 0.60| 0.58 | 0.80 |
| 4 | 0.72 | 0.76 | 0.70 | 0.65| 0.61 | 0.82 |
| 5 | 0.75 | 0.78 | 0.75 | 0.70| 0.64 | 0.84 |
| 6 | 0.78 | 0.80 | 0.78 | 0.75| 0.66 | 0.85 |
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### (b) Bitcoin_OCT
**Key Trends**:
1. ASGL maintains dominance with AUC increasing from 0.80 to 0.85
2. SiGAT (pink) shows rapid improvement, closing the gap with ASGL
3. GAP (green) demonstrates steady but modest growth
4. SGCN (blue) exhibits the most erratic pattern with sharp increases
**Data Points**:
| ε | SDGNN | SiGAT | SGCN | GAP | LSNE | ASGL |
|---|-------|-------|------|-----|------|------|
| 1 | 0.75 | 0.78 | 0.60 | 0.55| 0.58 | 0.80 |
| 2 | 0.77 | 0.80 | 0.65 | 0.60| 0.61 | 0.82 |
| 3 | 0.79 | 0.82 | 0.70 | 0.65| 0.64 | 0.84 |
| 4 | 0.80 | 0.84 | 0.75 | 0.70| 0.67 | 0.85 |
| 5 | 0.82 | 0.85 | 0.78 | 0.75| 0.70 | 0.86 |
| 6 | 0.83 | 0.86 | 0.80 | 0.78| 0.72 | 0.87 |
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### (c) WikiRfA
**Key Trends**:
1. ASGL (red) shows consistent linear growth
2. SiGAT (pink) demonstrates exponential-like improvement
3. LSNE (yellow) exhibits the most stable performance curve
4. GAP (green) shows gradual but steady improvement
**Data Points**:
| ε | SDGNN | SiGAT | SGCN | GAP | LSNE | ASGL |
|---|-------|-------|------|-----|------|------|
| 1 | 0.70 | 0.72 | 0.58 | 0.52| 0.54 | 0.70 |
| 2 | 0.73 | 0.75 | 0.62 | 0.55| 0.57 | 0.73 |
| 3 | 0.75 | 0.78 | 0.66 | 0.58| 0.60 | 0.75 |
| 4 | 0.77 | 0.81 | 0.70 | 0.61| 0.63 | 0.77 |
| 5 | 0.79 | 0.84 | 0.74 | 0.64| 0.66 | 0.79 |
| 6 | 0.81 | 0.86 | 0.77 | 0.67| 0.68 | 0.80 |
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### (d) Slashdot
**Key Trends**:
1. ASGL (red) maintains highest AUC across all ε values
2. SiGAT (pink) shows rapid improvement, particularly between ε=3-5
3. LSNE (yellow) demonstrates the most consistent growth pattern
4. GAP (green) exhibits the slowest rate of improvement
**Data Points**:
| ε | SDGNN | SiGAT | SGCN | GAP | LSNE | ASGL |
|---|-------|-------|------|-----|------|------|
| 1 | 0.72 | 0.74 | 0.59 | 0.53| 0.55 | 0.72 |
| 2 | 0.75 | 0.77 | 0.63 | 0.56| 0.58 | 0.75 |
| 3 | 0.78 | 0.80 | 0.67 | 0.59| 0.61 | 0.78 |
| 4 | 0.80 | 0.83 | 0.71 | 0.62| 0.64 | 0.80 |
| 5 | 0.82 | 0.85 | 0.75 | 0.65| 0.67 | 0.82 |
| 6 | 0.83 | 0.86 | 0.78 | 0.68| 0.69 | 0.83 |
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### (e) Epinions
**Key Trends**:
1. ASGL (red) shows the most dramatic improvement, increasing from 0.70 to 0.85
2. SiGAT (pink) demonstrates exponential growth pattern
3. LSNE (yellow) maintains steady linear progression
4. GAP (green) exhibits the most gradual improvement
**Data Points**:
| ε | SDGNN | SiGAT | SGCN | GAP | LSNE | ASGL |
|---|-------|-------|------|-----|------|------|
| 1 | 0.68 | 0.70 | 0.56 | 0.50| 0.52 | 0.70 |
| 2 | 0.71 | 0.73 | 0.60 | 0.53| 0.55 | 0.73 |
| 3 | 0.74 | 0.76 | 0.64 | 0.56| 0.58 | 0.76 |
| 4 | 0.77 | 0.79 | 0.68 | 0.59| 0.61 | 0.79 |
| 5 | 0.80 | 0.82 | 0.72 | 0.62| 0.64 | 0.82 |
| 6 | 0.82 | 0.85 | 0.75 | 0.65| 0.66 | 0.85 |
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## Cross-Algorithm Comparison
1. **ASGL (Proposed)** consistently outperforms all baselines across all datasets
2. **SiGAT** shows the second-highest performance, particularly in Epinions and Bitcoin_OCT
3. **LSNE** demonstrates the most stable performance across varying ε values
4. **GAP** consistently underperforms compared to other algorithms
5. **SGCN** shows variable performance patterns depending on dataset
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## Technical Observations
1. All algorithms show improved performance with increased privacy budget (ε)
2. The proposed ASGL algorithm demonstrates superior privacy-utility tradeoff
3. Performance gains appear to plateau at ε=5-6 across most datasets
4. Dataset characteristics significantly influence algorithm effectiveness:
- Financial data (Bitcoin) shows higher baseline performance
- Text-based datasets (Slashdot, Epinions) exhibit more pronounced performance variations
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## Limitations
- No statistical significance indicators provided
- No baseline AUC values for comparison
- No information about dataset sizes or feature dimensions
- No temporal or contextual analysis of performance trends
