## Flowchart: Knowledge Graph Reasoning and Poisoning Process ### Overview The image depicts a multi-stage technical process involving knowledge graph reasoning (KGR), latent-space optimization, input-space approximation, and poisoning knowledge. The workflow begins with sampled queries, progresses through surrogate KGR and latent-space optimization, transitions to input-space approximation, and concludes with poisoning knowledge. Arrows indicate directional flow, and color-coded nodes/edges represent different components or statuses. --- ### Components/Axes 1. **Sampled Queries** - Three diagrams with nodes (green, black) and edges (blue). - One node in each diagram is marked with a red question mark (indicating uncertainty or target for poisoning). 2. **Surrogate KGR** - A graph with interconnected nodes (green, black) and edges (blue). - Positioned between "sampled queries" and "latent-space optimization." 3. **Latent-Space Optimization** - Two panels: - **Left Panel**: Nodes (black) with red points (targets) connected by arrows. - **Right Panel**: Nodes (black) with red points (targets) and additional red edges. - Arrows connect the surrogate KGR to both panels, indicating mapping to latent space. 4. **Input-Space Approximation** - A graph with nodes (green, black) and edges (blue, red). - Arrows connect the latent-space optimization panels to this stage, showing reconstruction in input space. 5. **Poisoning Knowledge** - Three diagrams with nodes (green, black) and edges (red). - Red edges dominate, suggesting adversarial modifications. --- ### Detailed Analysis - **Color Coding**: - **Green**: Likely represents correct/valid nodes. - **Black**: Neutral or unspecified nodes. - **Red**: Targets for poisoning or adversarial modifications. - **Flow Direction**: - Queries → Surrogate KGR → Latent-space optimization → Input-space approximation → Poisoning knowledge. - **Key Relationships**: - The surrogate KGR acts as a bridge between raw queries and latent-space optimization. - Latent-space optimization refines targets (red points) before input-space approximation. - Poisoning knowledge introduces adversarial edges (red) into the final graph. --- ### Key Observations 1. **Adversarial Focus**: The red question marks and edges highlight the process's focus on manipulating specific nodes/edges. 2. **Bidirectional Mapping**: The latent-space optimization includes feedback loops (arrows pointing back to input-space approximation), suggesting iterative refinement. 3. **Poisoning Mechanism**: The final stage replaces blue edges with red ones, indicating a shift from valid to adversarial relationships. --- ### Interpretation This flowchart illustrates a machine learning pipeline for knowledge graph reasoning with adversarial attacks. The process begins by sampling queries, then uses a surrogate KGR to map them into a latent space for optimization. The optimized targets are reconstructed in the input space, and finally, adversarial modifications (poisoning) are introduced to corrupt the knowledge graph. The use of red nodes/edges in the poisoning stage suggests a deliberate attempt to degrade model performance by altering critical relationships. The bidirectional arrows between latent and input spaces imply a feedback mechanism to improve approximation accuracy.