## Bar Chart: AUC Comparison of ASGL Variants on Different Datasets ### Overview The image is a bar chart comparing the Area Under the Curve (AUC) performance of three variants of the ASGL algorithm (ASGL-, ASGL+, and ASGL) across four different datasets: Bitcoin-Alpha, Bitcoin-OCT, Slashdot, and WikiRfA. The chart visually represents the AUC scores for each ASGL variant on each dataset, allowing for a direct comparison of their performance. ### Components/Axes * **Y-axis:** AUC (Area Under the Curve), ranging from 0.65 to 0.90 in increments of 0.05. * **X-axis:** Datasets: Bitcoin-Alpha, Bitcoin-OCT, Slashdot, WikiRfA. * **Legend:** Located at the top of the chart. * ASGL-: Light blue bars with diagonal stripes. * ASGL+: Blue bars with diagonal stripes. * ASGL: Light red bars with a dotted pattern. * **Gridlines:** Horizontal gridlines are present at each 0.05 increment on the Y-axis. ### Detailed Analysis Here's a breakdown of the AUC values for each ASGL variant on each dataset: * **Bitcoin-Alpha:** * ASGL-: Approximately 0.835 * ASGL+: Approximately 0.84 * ASGL: Approximately 0.86 * **Bitcoin-OCT:** * ASGL-: Approximately 0.81 * ASGL+: Approximately 0.83 * ASGL: Approximately 0.85 * **Slashdot:** * ASGL-: Approximately 0.86 * ASGL+: Approximately 0.81 * ASGL: Approximately 0.89 * **WikiRfA:** * ASGL-: Approximately 0.79 * ASGL+: Approximately 0.70 * ASGL: Approximately 0.81 ### Key Observations * ASGL consistently outperforms ASGL- and ASGL+ on Bitcoin-Alpha, Bitcoin-OCT, and Slashdot datasets. * On WikiRfA, ASGL- performs better than ASGL+, but both are outperformed by ASGL. * The performance difference between ASGL variants is most pronounced on the WikiRfA dataset. * Slashdot shows the highest AUC values for ASGL, indicating it might be the most suitable dataset for this algorithm. * WikiRfA shows the lowest AUC values for ASGL+ and ASGL-, indicating it might be the least suitable dataset for these algorithms. ### Interpretation The bar chart provides a comparative analysis of the ASGL algorithm and its variants across different datasets. The data suggests that the standard ASGL algorithm generally achieves higher AUC scores compared to its ASGL- and ASGL+ variants, indicating better overall performance in most cases. However, the performance varies depending on the dataset, with WikiRfA showing a significant performance gap between ASGL and its variants. This could imply that the ASGL algorithm is more robust or better suited for certain types of network structures or data characteristics present in the Bitcoin-Alpha, Bitcoin-OCT, and Slashdot datasets compared to WikiRfA. The lower performance of ASGL+ on WikiRfA is a notable outlier, suggesting potential limitations or sensitivities of this variant to specific data properties. Further investigation into the characteristics of each dataset and the algorithmic differences between ASGL, ASGL-, and ASGL+ would be necessary to fully understand these performance variations.

# Technical Data Extraction: Performance Comparison Chart (AUC) ## 1. Document Overview This image is a grouped bar chart comparing the performance of three algorithmic variants across four distinct datasets. The performance metric used is **AUC** (Area Under the ROC Curve). ## 2. Component Isolation ### A. Header / Legend * **Location:** Top-left quadrant, overlaid on the chart area. * **Legend Items:** 1. **ASGL-**: Represented by a light teal bar with diagonal hatching (bottom-left to top-right). 2. **ASGL+**: Represented by a light blue bar with diagonal hatching (top-left to bottom-right). 3. **ASGL**: Represented by a light red/pink bar with a dotted/stippled pattern. ### B. Main Chart Area (Axes) * **Y-Axis (Vertical):** * **Label:** "AUC" * **Range:** 0.65 to 0.90. * **Major Tick Marks:** 0.65, 0.70, 0.75, 0.80, 0.85, 0.90. * **X-Axis (Horizontal):** * **Categories (Datasets):** Bitcoin-Alpha, Bitcoin-OTC, Slashdot, WikiRfA. * **Gridlines:** Horizontal and vertical dashed grey lines are present to facilitate value estimation. ## 3. Data Table Reconstruction The following table provides estimated numerical values based on the spatial positioning of the bar heights relative to the Y-axis markers. | Dataset | ASGL- (Teal/Hatched) | ASGL+ (Blue/Hatched) | ASGL (Red/Dotted) | | :--- | :---: | :---: | :---: | | **Bitcoin-Alpha** | ~0.832 | ~0.839 | ~0.859 | | **Bitcoin-OTC** | ~0.805 | ~0.826 | ~0.846 | | **Slashdot** | ~0.862 | ~0.810 | ~0.890 | | **WikiRfA** | ~0.786 | ~0.701 | ~0.805 | ## 4. Trend Verification and Analysis ### Dataset Trends * **Bitcoin-Alpha:** Shows a consistent upward trend across the variants. ASGL performs best, followed by ASGL+, with ASGL- being the lowest. * **Bitcoin-OTC:** Follows the same upward trend as Bitcoin-Alpha. ASGL > ASGL+ > ASGL-. * **Slashdot:** Displays a "V" shaped trend or a dip. ASGL- is relatively high, ASGL+ drops significantly, and ASGL reaches the highest peak of the entire chart (approx. 0.89). * **WikiRfA:** Displays a sharp downward trend from ASGL- to ASGL+, followed by a significant recovery for ASGL. ASGL+ performs notably poorly here, barely exceeding the 0.70 mark. ### Variant Performance Summary * **ASGL (Red/Dotted):** This variant is the top performer across all four datasets. It consistently achieves the highest AUC, peaking at nearly 0.90 on the Slashdot dataset. * **ASGL- vs ASGL+:** The relationship between these two is inconsistent. In the "Bitcoin" datasets, ASGL+ outperforms ASGL-. However, in the "Slashdot" and "WikiRfA" datasets, ASGL- significantly outperforms ASGL+. * **Overall Stability:** ASGL shows the most stability in maintaining high performance (all values > 0.80), whereas ASGL+ shows the highest volatility, with a significant performance drop in the WikiRfA dataset.

## Grouped Bar Chart: AUC Comparison of ASGL Variants Across Datasets ### Overview The image displays a grouped bar chart comparing the Area Under the Curve (AUC) performance of three model variants—ASGL-, ASGL+, and ASGL—across four different network datasets. The chart uses hatching patterns instead of solid colors to distinguish the data series. ### Components/Axes * **Chart Type:** Grouped Bar Chart. * **Y-Axis:** - **Label:** "AUC" - **Scale:** Linear, ranging from 0.65 to 0.90, with major gridlines at intervals of 0.05 (0.65, 0.70, 0.75, 0.80, 0.85, 0.90). * **X-Axis:** - **Categories (Datasets):** Four distinct categories are labeled: "Bitcoin-Alpha", "Bitcoin-OCT", "Slashdot", and "WikiRfA". * **Legend:** - **Position:** Top-left corner of the chart area. - **Series:** 1. **ASGL-:** Represented by bars with a diagonal stripe pattern (lines sloping down from left to right). 2. **ASGL+:** Represented by bars with a dotted pattern. 3. **ASGL:** Represented by bars with a cross-hatch pattern (diagonal lines in both directions). ### Detailed Analysis The chart presents AUC values for each variant on each dataset. Values are approximate based on visual alignment with the Y-axis gridlines. **1. Bitcoin-Alpha:** * **ASGL- (Diagonal Stripes):** ~0.83 * **ASGL+ (Dots):** ~0.84 * **ASGL (Cross-hatch):** ~0.86 * **Trend:** Performance increases from ASGL- to ASGL+ to ASGL. **2. Bitcoin-OCT:** * **ASGL- (Diagonal Stripes):** ~0.805 * **ASGL+ (Dots):** ~0.825 * **ASGL (Cross-hatch):** ~0.845 * **Trend:** Performance increases from ASGL- to ASGL+ to ASGL. **3. Slashdot:** * **ASGL- (Diagonal Stripes):** ~0.86 * **ASGL+ (Dots):** ~0.81 * **ASGL (Cross-hatch):** ~0.89 * **Trend:** Non-monotonic. ASGL- performs better than ASGL+, but ASGL achieves the highest score. **4. WikiRfA:** * **ASGL- (Diagonal Stripes):** ~0.785 * **ASGL+ (Dots):** ~0.70 * **ASGL (Cross-hatch):** ~0.805 * **Trend:** Non-monotonic. ASGL+ shows a significant drop in performance compared to ASGL-, while ASGL recovers to achieve the highest score for this dataset. ### Key Observations 1. **Consistent Leader:** The **ASGL** variant (cross-hatch pattern) achieves the highest AUC score on all four datasets. 2. **Dataset Difficulty:** The **WikiRfA** dataset appears to be the most challenging, as all three variants show their lowest performance here. The **ASGL+** variant's performance on WikiRfA (~0.70) is a notable outlier, being the lowest single data point in the chart. 3. **Anomalous Trend on Slashdot:** The **Slashdot** dataset is the only one where the **ASGL-** variant outperforms the **ASGL+** variant. This breaks the pattern seen in the Bitcoin datasets where ASGL+ consistently scored higher than ASGL-. 4. **Visual Encoding:** The chart relies entirely on hatching patterns for differentiation, which is effective for black-and-white printing but requires careful visual matching between the legend and bars. ### Interpretation This chart demonstrates the comparative effectiveness of the ASGL method and its ablated versions (ASGL-, ASGL+) for a link prediction or network analysis task (inferred from the use of AUC and common network dataset names). * **Core Finding:** The full **ASGL** model is robust and superior across diverse network types (cryptocurrency trust networks like Bitcoin-Alpha/OCT, social news like Slashdot, and Wikipedia adminship votes like WikiRfA). This suggests its algorithmic components work synergistically. * **Ablation Insight:** Removing components (to create ASGL- and ASGL+) generally harms performance, but the impact is dataset-dependent. The severe drop of **ASGL+** on **WikiRfA** indicates that the component removed in ASGL- but present in ASGL+ is critical for that specific type of voting/controversy network. Conversely, on **Slashdot**, that same component may be slightly detrimental or noisy. * **Practical Implication:** The results argue for using the complete ASGL model for general-purpose application. However, the performance variance of the ablated versions provides diagnostic insight into which parts of the model are most valuable for different network structures, guiding future refinement. The chart effectively communicates that model architecture choices have non-uniform effects across different data domains.

## Bar Chart: AUC Scores for Different ASGL Models ### Overview The bar chart compares the Area Under the Curve (AUC) scores of four different ASGL (Adversarial Self-Generation Learning) models: ASGL-, ASGL+, ASGL, and WikiRFA, across four different datasets: Bitcoin-Alpha, Bitcoin-OCT, Slashdot, and WikiRFA. ### Components/Axes - **X-axis**: Represents the different datasets (Bitcoin-Alpha, Bitcoin-OCT, Slashdot, WikiRFA). - **Y-axis**: Represents the AUC scores, ranging from 0.65 to 0.90. - **Legend**: Shows the different ASGL models (ASGL-, ASGL+, ASGL, WikiRFA) with their respective colors. ### Detailed Analysis or ### Content Details - **Bitcoin-Alpha**: ASGL+ has the highest AUC score of approximately 0.88, followed by ASGL with a score of about 0.85. ASGL- and WikiRFA have scores of approximately 0.82 and 0.78, respectively. - **Bitcoin-OCT**: ASGL+ has the highest AUC score of approximately 0.87, followed by ASGL with a score of about 0.84. ASGL- and WikiRFA have scores of approximately 0.81 and 0.76, respectively. - **Slashdot**: ASGL+ has the highest AUC score of approximately 0.89, followed by ASGL with a score of about 0.86. ASGL- and WikiRFA have scores of approximately 0.84 and 0.79, respectively. - **WikiRFA**: ASGL+ has the highest AUC score of approximately 0.87, followed by ASGL with a score of about 0.85. ASGL- and WikiRFA have scores of approximately 0.82 and 0.77, respectively. ### Key Observations - ASGL+ consistently outperforms the other models across all datasets. - ASGL+ has the highest AUC scores, indicating the best performance. - WikiRFA has the lowest AUC scores, indicating the worst performance. - There is a noticeable difference in performance between ASGL+ and the other models, with ASGL+ consistently outperforming them. ### Interpretation The data suggests that ASGL+ is the most effective ASGL model among the four tested, with the highest AUC scores across all datasets. This indicates that ASGL+ is the best at generating adversarial examples that are difficult for other models to classify. The other models, ASGL- and WikiRFA, have lower AUC scores, indicating that they are less effective at generating adversarial examples. The performance of ASGL+ is significantly higher than that of the other models, suggesting that ASGL+ is the most effective ASGL model among the four tested.

# Technical Document: Chart Analysis ## Chart Title **AUC Comparison Across Datasets and Models** ## Chart Type Bar chart with grouped bars representing model performance across datasets. ## Axes - **X-axis**: Datasets Categories: `Bitcoin-Alpha`, `Bitcoin-OCT`, `Slashdot`, `WikiRfA` - **Y-axis**: AUC (Area Under the Curve) Range: 0.65 to 0.90 (in increments of 0.05) ## Legend - **Position**: Top-right corner - **Labels**: - `ASGL-`: Light blue with diagonal stripes - `ASGL+`: Dark blue with diagonal stripes - `ASGL`: Red with dotted pattern ## Data Points and Trends ### Dataset: Bitcoin-Alpha - **ASGL-**: ~0.83 (light blue, diagonal stripes) - **ASGL+**: ~0.84 (dark blue, diagonal stripes) - **ASGL**: ~0.86 (red, dotted) **Trend**: ASGL > ASGL+ > ASGL- ### Dataset: Bitcoin-OCT - **ASGL-**: ~0.81 (light blue, diagonal stripes) - **ASGL+**: ~0.83 (dark blue, diagonal stripes) - **ASGL**: ~0.85 (red, dotted) **Trend**: ASGL > ASGL+ > ASGL- ### Dataset: Slashdot - **ASGL-**: ~0.86 (light blue, diagonal stripes) - **ASGL+**: ~0.81 (dark blue, diagonal stripes) - **ASGL**: ~0.89 (red, dotted) **Trend**: ASGL > ASGL- > ASGL+ ### Dataset: WikiRfA - **ASGL-**: ~0.79 (light blue, diagonal stripes) - **ASGL+**: ~0.70 (dark blue, diagonal stripes) - **ASGL**: ~0.81 (red, dotted) **Trend**: ASGL > ASGL- > ASGL+ ## Key Observations 1. **ASGL** consistently achieves the highest AUC across all datasets, with the largest margin in `Slashdot` (~0.89). 2. **ASGL+** underperforms compared to `ASGL` in all datasets, with the steepest drop in `WikiRfA` (~0.70). 3. **ASGL-** shows moderate performance, outperforming `ASGL+` in `WikiRfA` but lagging behind `ASGL` in all cases. 4. **Slashdot** exhibits the highest overall AUC values, while **WikiRfA** has the lowest. ## Spatial Grounding - **Legend**: Top-right corner (confirmed via visual alignment). - **Color Consistency**: - `ASGL-` (light blue) matches all light blue bars. - `ASGL+` (dark blue) matches all dark blue bars. - `ASGL` (red) matches all red bars. ## Language and Text - **Primary Language**: English - **No additional languages detected**. ## Component Isolation 1. **Header**: Chart title and legend. 2. **Main Chart**: Grouped bars for datasets and models. 3. **Footer**: No additional text or annotations. ## Conclusion The chart demonstrates that `ASGL` outperforms `ASGL+` and `ASGL-` across all datasets, with the most significant advantage in `Slashdot`. `ASGL+` shows the weakest performance, particularly in `WikiRfA`.