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"#ffffff"}, "title": "Behaviour Risk", "community": 17, "community_name": "Network Composition", "source_file": "images/big.png", "file_type": "image", "degree": 2}, {"id": "big_graph_risk", "label": "Graph Risk", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Graph Risk", "community": 17, "community_name": "Network Composition", "source_file": "images/big.png", "file_type": "image", "degree": 2}, {"id": "big_hub_legitimacy", "label": "Hub Legitimacy", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Hub Legitimacy", "community": 17, "community_name": "Network Composition", "source_file": "images/big.png", "file_type": "image", "degree": 2}, {"id": "big_fake_risk", "label": "Fake Risk (composite, clipped)", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 12.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Fake Risk (composite, clipped)", "community": 17, "community_name": "Network Composition", "source_file": "images/big.png", "file_type": "image", "degree": 4}, {"id": "table3_task_easy", "label": "Easy Task", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Easy Task", "community": 26, "community_name": "Evaluation Formulas", "source_file": "images/table3.png", "file_type": "image", "degree": 1}, {"id": "table3_task_medium", "label": "Medium Task", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Medium Task", "community": 26, "community_name": "Evaluation Formulas", "source_file": "images/table3.png", "file_type": "image", "degree": 2}, {"id": "table3_task_hard", "label": "Hard Task", "color": {"background": "#B07AA1", "border": "#B07AA1", "highlight": {"background": "#ffffff", "border": "#B07AA1"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Hard Task", "community": 26, "community_name": "Evaluation Formulas", "source_file": "images/table3.png", "file_type": "image", "degree": 1}, {"id": "table1_llama4_scout", "label": "Llama 4 Scout 1.7B Model", "color": {"background": "#59A14F", "border": "#59A14F", "highlight": {"background": "#ffffff", "border": "#59A14F"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Llama 4 Scout 1.7B Model", "community": 24, "community_name": "Nvidia Model Eval", "source_file": "images/table1.png", "file_type": "image", "degree": 1}, {"id": "table1_ministral", "label": "Ministral 3.8B Model", "color": {"background": "#59A14F", "border": "#59A14F", 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"source_file": "images/table1.png", "file_type": "image", "degree": 1}, {"id": "table1_rule_baseline", "label": "Rule-Based Baseline", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Rule-Based Baseline", "community": 29, "community_name": "Observation Processing", "source_file": "images/table1.png", "file_type": "image", "degree": 1}, {"id": "table1_gemma", "label": "Gemma 3 12B Model", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Gemma 3 12B Model", "community": 29, "community_name": "Observation Processing", "source_file": "images/table1.png", "file_type": "image", "degree": 1}, {"id": "logo_graphstrike", "label": "GraphStrike Logo", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "GraphStrike Logo", "community": 38, "community_name": "System Setup", "source_file": "assets/logo.png", "file_type": "image", "degree": 0}, {"id": "formulas1_node_risk", "label": "Node Risk Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Node Risk Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 2}, {"id": "formulas1_age_normalisation", "label": "Age Normalisation Formula", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 10.0, "font": {"size": 0, "color": "#ffffff"}, "title": "Age Normalisation Formula", "community": 39, "community_name": "Documentation", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 0}, {"id": "formulas1_behaviour_risk", "label": "Behaviour Risk Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Behaviour Risk Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 2}, {"id": "formulas1_flagged_neighbour_ratio", "label": "Flagged Neighbour Ratio Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Flagged Neighbour Ratio Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 1}, {"id": "formulas1_graph_risk", "label": "Graph Risk Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Graph Risk Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 3}, {"id": "formulas1_hub_legitimacy", "label": "Hub Legitimacy Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Hub Legitimacy Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 2}, {"id": "formulas1_fake_risk_composite", "label": "Fake Risk Composite Formula", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 12.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Fake Risk Composite Formula", "community": 17, "community_name": "Network Composition", "source_file": "assets/formulas-1.png", "file_type": "image", "degree": 4}, {"id": "formulas2_recall_precision", "label": "Recall and Precision Metrics", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Recall and Precision Metrics", "community": 27, "community_name": "Knowledge Distillation", "source_file": "assets/formulas-2.png", "file_type": "image", "degree": 1}, {"id": "formulas2_efficiency", "label": "Efficiency Metric", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Efficiency Metric", "community": 27, "community_name": "Knowledge Distillation", "source_file": "assets/formulas-2.png", "file_type": "image", "degree": 1}, {"id": "formulas2_score_piecewise", "label": "Piecewise Score Function", "color": {"background": "#FF9DA7", "border": "#FF9DA7", "highlight": {"background": "#ffffff", "border": "#FF9DA7"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Piecewise Score Function", "community": 27, "community_name": "Knowledge Distillation", "source_file": "assets/formulas-2.png", "file_type": "image", "degree": 2}, {"id": "reflexion_run_episode", "label": "Run Episode", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Run Episode", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 2}, {"id": "reflexion_episode_ends", "label": "Episode Ends WIN or LOSS", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Episode Ends WIN or LOSS", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 3}, {"id": "reflexion_save_best_trajectory", "label": "Save Best Trajectory + Success Reflection", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Save Best Trajectory + Success Reflection", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 2}, {"id": "reflexion_generate_lesson", "label": "Generate Lesson (Owen writes reflection)", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Generate Lesson (Owen writes reflection)", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 2}, {"id": "reflexion_memory_store", "label": "Memory Store (reflections + \u03b1 + trajectory)", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.6, "font": {"size": 0, "color": "#ffffff"}, "title": "Memory Store (reflections + \u03b1 + trajectory)", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 3}, {"id": "reflexion_next_episode", "label": "Next Episode (richer prompt context)", "color": {"background": "#F28E2B", "border": "#F28E2B", "highlight": {"background": "#ffffff", "border": "#F28E2B"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Next Episode (richer prompt context)", "community": 21, "community_name": "Gemma Model Eval", "source_file": "assets/reflexion.png", "file_type": "image", "degree": 2}, {"id": "hybrid_observation", "label": "Observation", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Observation", "community": 19, "community_name": "Bedrock Integration", "source_file": "assets/hybrid.png", "file_type": "image", "degree": 2}, {"id": "hybrid_rule_engine", "label": "Rule Engine (returns action, confidence)", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": "#BAB0AC"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Rule Engine (returns action, confidence)", "community": 19, "community_name": "Bedrock Integration", "source_file": "assets/hybrid.png", "file_type": "image", "degree": 2}, {"id": "hybrid_qwen3_llm", "label": "Qwen3-80B LLM", "color": {"background": "#BAB0AC", "border": "#BAB0AC", "highlight": {"background": "#ffffff", "border": 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"OBSERVE Phase", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "OBSERVE Phase", "community": 25, "community_name": "Detection Formulas", "source_file": "assets/episode.png", "file_type": "image", "degree": 1}, {"id": "episode_investigate", "label": "INVESTIGATE Phase", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "INVESTIGATE Phase", "community": 25, "community_name": "Detection Formulas", "source_file": "assets/episode.png", "file_type": "image", "degree": 2}, {"id": "episode_flag", "label": "FLAG Phase", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "FLAG Phase", "community": 25, "community_name": "Detection Formulas", "source_file": "assets/episode.png", "file_type": "image", "degree": 2}, {"id": "episode_submit", "label": "SUBMIT Phase", "color": {"background": "#EDC948", "border": "#EDC948", "highlight": {"background": "#ffffff", "border": "#EDC948"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "SUBMIT Phase", "community": 25, "community_name": "Detection Formulas", "source_file": "assets/episode.png", "file_type": "image", "degree": 1}, {"id": "gs_graph_signals", "label": "Graph Signals (computed at INSPECT)", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Graph Signals (computed at INSPECT)", "community": 28, "community_name": "State Machine Logic", "source_file": "assets/gs.png", "file_type": "image", "degree": 1}, {"id": "gs_behavioural_signals", "label": "Behavioural Signals (temporal + device)", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 11.1, "font": {"size": 0, "color": "#ffffff"}, "title": "Behavioural Signals (temporal + device)", "community": 28, "community_name": "State Machine Logic", "source_file": "assets/gs.png", "file_type": "image", "degree": 2}, {"id": "gs_node_signals", "label": "Node Signals (pre-computed offline)", "color": {"background": "#9C755F", "border": "#9C755F", "highlight": {"background": "#ffffff", "border": "#9C755F"}}, "size": 10.5, "font": {"size": 0, "color": "#ffffff"}, "title": "Node Signals (pre-computed offline)", "community": 28, "community_name": "State Machine Logic", "source_file": "assets/gs.png", "file_type": "image", "degree": 1}]; | |
| const RAW_EDGES = [{"from": "baseline_agent_py", "to": "baseline_agent_gang_score", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "baseline_agent_py", "to": "baseline_agent_easy_agent", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "baseline_agent_py", "to": "baseline_agent_medium_agent", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "baseline_agent_py", "to": "baseline_agent_hard_agent", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "baseline_agent_py", "to": "baseline_agent_evaluate", "label": "contains", "title": "contains [EXTRACTED]", "dashes": false, "width": 2, "color": 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1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "big_behaviour_risk", "to": "big_fake_risk", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "big_behaviour_risk", "to": "formulas1_behaviour_risk", "label": "semantically_similar_to", "title": "semantically_similar_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "big_graph_risk", "to": "big_fake_risk", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "big_graph_risk", "to": "formulas1_graph_risk", "label": "semantically_similar_to", "title": "semantically_similar_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "big_hub_legitimacy", "to": "big_fake_risk", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "big_hub_legitimacy", "to": "formulas1_hub_legitimacy", "label": "semantically_similar_to", "title": "semantically_similar_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table3_task_easy", "to": "table3_task_medium", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table3_task_medium", "to": "table3_task_hard", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table1_llama4_scout", "to": "table1_ministral", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table1_ministral", "to": "table1_deepseek", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table1_deepseek", "to": "table1_nemotron", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "table1_rule_baseline", "to": "table1_gemma", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "formulas1_node_risk", "to": "formulas1_fake_risk_composite", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas1_behaviour_risk", "to": "formulas1_fake_risk_composite", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas1_flagged_neighbour_ratio", "to": "formulas1_graph_risk", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas1_graph_risk", "to": "formulas1_fake_risk_composite", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas1_hub_legitimacy", "to": "formulas1_fake_risk_composite", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas2_recall_precision", "to": "formulas2_score_piecewise", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "formulas2_efficiency", "to": "formulas2_score_piecewise", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_run_episode", "to": "reflexion_episode_ends", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_run_episode", "to": "reflexion_next_episode", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_episode_ends", "to": "reflexion_save_best_trajectory", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_episode_ends", "to": "reflexion_generate_lesson", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_save_best_trajectory", "to": "reflexion_memory_store", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_generate_lesson", "to": "reflexion_memory_store", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "reflexion_memory_store", "to": "reflexion_next_episode", "label": "shares_data_with", "title": "shares_data_with [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_observation", "to": "hybrid_rule_engine", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_observation", "to": "hybrid_qwen3_llm", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_rule_engine", "to": "hybrid_blend_alpha", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_qwen3_llm", "to": "hybrid_blend_alpha", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_blend_alpha", "to": "hybrid_alpha_updater", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_blend_alpha", "to": "hybrid_final_action", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "hybrid_alpha_updater", "to": "hybrid_alpha_cap_per_task", "label": "rationale_for", "title": "rationale_for [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "episode_observe", "to": "episode_investigate", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "episode_investigate", "to": "episode_flag", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "episode_flag", "to": "episode_submit", "label": "references", "title": "references [EXTRACTED]", "dashes": false, "width": 2, "color": {"opacity": 0.7}, "confidence": "EXTRACTED"}, {"from": "gs_graph_signals", "to": "gs_behavioural_signals", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}, {"from": "gs_behavioural_signals", "to": "gs_node_signals", "label": "conceptually_related_to", "title": "conceptually_related_to [INFERRED]", "dashes": true, "width": 1, "color": {"opacity": 0.35}, "confidence": "INFERRED"}]; | |
| const LEGEND = [{"cid": 0, "color": "#4E79A7", "label": "Risk Scoring Components", "count": 78}, {"cid": 1, "color": "#F28E2B", "label": "Model Infrastructure", "count": 40}, {"cid": 2, "color": "#E15759", "label": "Agent Memory & Learning", "count": 29}, {"cid": 3, "color": "#76B7B2", "label": "Hybrid Policy Architecture", "count": 27}, {"cid": 4, "color": "#59A14F", "label": "Inference Pipeline", "count": 25}, {"cid": 5, "color": "#EDC948", "label": "Data Generation", "count": 25}, {"cid": 6, "color": "#B07AA1", "label": "Evaluation Metrics", "count": 25}, {"cid": 7, "color": "#FF9DA7", "label": "Base LLM Models", "count": 25}, {"cid": 8, "color": "#9C755F", "label": "Environment Management", "count": 25}, {"cid": 9, "color": "#BAB0AC", "label": "Signal Detection", "count": 25}, {"cid": 10, "color": "#4E79A7", "label": "Rule Engine", "count": 25}, {"cid": 11, "color": "#F28E2B", "label": "Episode Workflow", "count": 24}, {"cid": 12, "color": "#E15759", "label": "Graph Analysis", "count": 22}, {"cid": 13, "color": "#76B7B2", "label": "Integration Testing", "count": 15}, {"cid": 14, "color": "#59A14F", "label": "Judgement Benchmarks", "count": 12}, {"cid": 15, "color": "#EDC948", "label": "Performance Monitoring", "count": 12}, {"cid": 16, "color": "#B07AA1", "label": "Reflexion Framework", "count": 11}, {"cid": 17, "color": "#FF9DA7", "label": "Network Composition", "count": 11}, {"cid": 18, "color": "#9C755F", "label": "Safe Action Selection", "count": 10}, {"cid": 19, "color": "#BAB0AC", "label": "Bedrock Integration", "count": 7}, {"cid": 20, "color": "#4E79A7", "label": "Llama Model Eval", "count": 6}, {"cid": 21, "color": "#F28E2B", "label": "Gemma Model Eval", "count": 6}, {"cid": 22, "color": "#E15759", "label": "DeepSeek Model Eval", "count": 5}, {"cid": 23, "color": "#76B7B2", "label": "Mistral Model Eval", "count": 4}, {"cid": 24, "color": "#59A14F", "label": "Nvidia Model Eval", "count": 4}, {"cid": 25, "color": "#EDC948", "label": "Detection Formulas", "count": 4}, {"cid": 26, "color": "#B07AA1", "label": "Evaluation Formulas", "count": 3}, {"cid": 27, "color": "#FF9DA7", "label": "Knowledge Distillation", "count": 3}, {"cid": 28, "color": "#9C755F", "label": "State Machine Logic", "count": 3}, {"cid": 29, "color": "#BAB0AC", "label": "Observation Processing", "count": 2}, {"cid": 30, "color": "#4E79A7", "label": "Flagging Logic", "count": 1}, {"cid": 31, "color": "#F28E2B", "label": "Investigation Protocol", "count": 1}, {"cid": 32, "color": "#E15759", "label": "Baseline Comparison", "count": 1}, {"cid": 33, "color": "#76B7B2", "label": "Architecture Diagrams", "count": 1}, {"cid": 34, "color": "#59A14F", "label": "Trajectory Replay", "count": 1}, {"cid": 35, "color": "#EDC948", "label": "Trust Calibration", "count": 1}, {"cid": 36, "color": "#B07AA1", "label": "API Clients", "count": 1}, {"cid": 37, "color": "#FF9DA7", "label": "Configuration", "count": 1}, {"cid": 38, "color": "#9C755F", "label": "System Setup", "count": 1}, {"cid": 39, "color": "#BAB0AC", "label": "Documentation", "count": 1}]; | |
| // HTML-escape helper — prevents XSS when injecting graph data into innerHTML | |
| function esc(s) { | |
| return String(s).replace(/&/g,'&').replace(/</g,'<').replace(/>/g,'>').replace(/"/g,'"').replace(/'/g,'''); | |
| } | |
| // Build vis datasets | |
| const nodesDS = new vis.DataSet(RAW_NODES.map(n => ({ | |
| id: n.id, label: n.label, color: n.color, size: n.size, | |
| font: n.font, title: n.title, | |
| _community: n.community, _community_name: n.community_name, | |
| _source_file: n.source_file, _file_type: n.file_type, _degree: n.degree, | |
| }))); | |
| const edgesDS = new vis.DataSet(RAW_EDGES.map((e, i) => ({ | |
| id: i, from: e.from, to: e.to, | |
| label: '', | |
| title: e.title, | |
| dashes: e.dashes, | |
| width: e.width, | |
| color: e.color, | |
| arrows: { to: { enabled: true, scaleFactor: 0.5 } }, | |
| }))); | |
| const container = document.getElementById('graph'); | |
| const network = new vis.Network(container, { nodes: nodesDS, edges: edgesDS }, { | |
| physics: { | |
| enabled: true, | |
| solver: 'forceAtlas2Based', | |
| forceAtlas2Based: { | |
| gravitationalConstant: -60, | |
| centralGravity: 0.005, | |
| springLength: 120, | |
| springConstant: 0.08, | |
| damping: 0.4, | |
| avoidOverlap: 0.8, | |
| }, | |
| stabilization: { iterations: 200, fit: true }, | |
| }, | |
| interaction: { | |
| hover: true, | |
| tooltipDelay: 100, | |
| hideEdgesOnDrag: true, | |
| navigationButtons: false, | |
| keyboard: false, | |
| }, | |
| nodes: { shape: 'dot', borderWidth: 1.5 }, | |
| edges: { smooth: { type: 'continuous', roundness: 0.2 }, selectionWidth: 3 }, | |
| }); | |
| network.once('stabilizationIterationsDone', () => { | |
| network.setOptions({ physics: { enabled: false } }); | |
| }); | |
| function showInfo(nodeId) { | |
| const n = nodesDS.get(nodeId); | |
| if (!n) return; | |
| const neighborIds = network.getConnectedNodes(nodeId); | |
| const neighborItems = neighborIds.map(nid => { | |
| const nb = nodesDS.get(nid); | |
| const color = nb ? nb.color.background : '#555'; | |
| return `<span class="neighbor-link" style="border-left-color:${esc(color)}" onclick="focusNode(${JSON.stringify(nid)})">${esc(nb ? nb.label : nid)}</span>`; | |
| }).join(''); | |
| document.getElementById('info-content').innerHTML = ` | |
| <div class="field"><b>${esc(n.label)}</b></div> | |
| <div class="field">Type: ${esc(n._file_type || 'unknown')}</div> | |
| <div class="field">Community: ${esc(n._community_name)}</div> | |
| <div class="field">Source: ${esc(n._source_file || '-')}</div> | |
| <div class="field">Degree: ${n._degree}</div> | |
| ${neighborIds.length ? `<div class="field" style="margin-top:8px;color:#aaa;font-size:11px">Neighbors (${neighborIds.length})</div><div id="neighbors-list">${neighborItems}</div>` : ''} | |
| `; | |
| } | |
| function focusNode(nodeId) { | |
| network.focus(nodeId, { scale: 1.4, animation: true }); | |
| network.selectNodes([nodeId]); | |
| showInfo(nodeId); | |
| } | |
| // Track hovered node — hover detection is more reliable than click params | |
| let hoveredNodeId = null; | |
| network.on('hoverNode', params => { | |
| hoveredNodeId = params.node; | |
| container.style.cursor = 'pointer'; | |
| }); | |
| network.on('blurNode', () => { | |
| hoveredNodeId = null; | |
| container.style.cursor = 'default'; | |
| }); | |
| container.addEventListener('click', () => { | |
| if (hoveredNodeId !== null) { | |
| showInfo(hoveredNodeId); | |
| network.selectNodes([hoveredNodeId]); | |
| } | |
| }); | |
| network.on('click', params => { | |
| if (params.nodes.length > 0) { | |
| showInfo(params.nodes[0]); | |
| } else if (hoveredNodeId === null) { | |
| document.getElementById('info-content').innerHTML = '<span class="empty">Click a node to inspect it</span>'; | |
| } | |
| }); | |
| const searchInput = document.getElementById('search'); | |
| const searchResults = document.getElementById('search-results'); | |
| searchInput.addEventListener('input', () => { | |
| const q = searchInput.value.toLowerCase().trim(); | |
| searchResults.innerHTML = ''; | |
| if (!q) { searchResults.style.display = 'none'; return; } | |
| const matches = RAW_NODES.filter(n => n.label.toLowerCase().includes(q)).slice(0, 20); | |
| if (!matches.length) { searchResults.style.display = 'none'; return; } | |
| searchResults.style.display = 'block'; | |
| matches.forEach(n => { | |
| const el = document.createElement('div'); | |
| el.className = 'search-item'; | |
| el.textContent = n.label; | |
| el.style.borderLeft = `3px solid ${n.color.background}`; | |
| el.style.paddingLeft = '8px'; | |
| el.onclick = () => { | |
| network.focus(n.id, { scale: 1.5, animation: true }); | |
| network.selectNodes([n.id]); | |
| showInfo(n.id); | |
| searchResults.style.display = 'none'; | |
| searchInput.value = ''; | |
| }; | |
| searchResults.appendChild(el); | |
| }); | |
| }); | |
| document.addEventListener('click', e => { | |
| if (!searchResults.contains(e.target) && e.target !== searchInput) | |
| searchResults.style.display = 'none'; | |
| }); | |
| const hiddenCommunities = new Set(); | |
| const legendEl = document.getElementById('legend'); | |
| LEGEND.forEach(c => { | |
| const item = document.createElement('div'); | |
| item.className = 'legend-item'; | |
| item.innerHTML = `<div class="legend-dot" style="background:${c.color}"></div> | |
| <span class="legend-label">${c.label}</span> | |
| <span class="legend-count">${c.count}</span>`; | |
| item.onclick = () => { | |
| if (hiddenCommunities.has(c.cid)) { | |
| hiddenCommunities.delete(c.cid); | |
| item.classList.remove('dimmed'); | |
| } else { | |
| hiddenCommunities.add(c.cid); | |
| item.classList.add('dimmed'); | |
| } | |
| const updates = RAW_NODES | |
| .filter(n => n.community === c.cid) | |
| .map(n => ({ id: n.id, hidden: hiddenCommunities.has(c.cid) })); | |
| nodesDS.update(updates); | |
| }; | |
| legendEl.appendChild(item); | |
| }); | |
| </script> | |
| <script> | |
| // Render hyperedges as shaded regions | |
| const hyperedges = [{"id": "model_benchmarking_suite", "label": "Model Benchmarking & Performance Evaluation", "nodes": ["judge_log_bedrock_qwen", "nvidia_judge_log_results", "gemma_judge_log_results", "meta_judge_log_results", "mistral_judge_log_results", "deepseek_judge_log_results"], "relation": "participate_in", "confidence": "INFERRED", "confidence_score": 0.9, "source_file": "model-benchmark-logs/"}, {"id": "hybrid_learning_architecture", "label": "Hybrid Policy Learning Architecture", "nodes": ["readme_hybrid_policy", "readme_reflexion", "readme_alpha_trust_weight", "readme_safety_net"], "relation": "form", "confidence": "INFERRED", "confidence_score": 0.85, "source_file": "README.md"}, {"id": "risk_assessment_framework", "label": "Risk Assessment & Detection Framework", "nodes": ["readme_signal_hierarchy", "readme_risk_scoring", "pipeline_risk_scoring", "readme_graph_detection"], "relation": "form", "confidence": "INFERRED", "confidence_score": 0.85, "source_file": "README.md"}, {"id": "risk_model_components", "label": "Fake Account Risk Model Components", "nodes": ["big_node_risk", "big_behaviour_risk", "big_graph_risk", "big_hub_legitimacy"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "images/big.png"}, {"id": "evaluation_framework", "label": "Evaluation Framework (Recall, Precision, Efficiency, Score)", "nodes": ["formulas2_recall_precision", "formulas2_efficiency", "formulas2_score_piecewise"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "assets/formulas-2.png"}, {"id": "reflexion_loop", "label": "Reflexion Learning Loop", "nodes": ["reflexion_run_episode", "reflexion_generate_lesson", "reflexion_memory_store", "reflexion_next_episode"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "assets/reflexion.png"}, {"id": "hybrid_decision_system", "label": "Hybrid Decision System (Rule + LLM blending)", "nodes": ["hybrid_rule_engine", "hybrid_qwen3_llm", "hybrid_blend_alpha"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "assets/hybrid.png"}, {"id": "episode_workflow", "label": "Episode Investigation Workflow", "nodes": ["episode_observe", "episode_investigate", "episode_flag", "episode_submit"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "assets/episode.png"}, {"id": "signal_hierarchy", "label": "Signal Hierarchy (Graph, Behavioural, Node)", "nodes": ["gs_graph_signals", "gs_behavioural_signals", "gs_node_signals"], "relation": "participate_in", "confidence": "EXTRACTED", "confidence_score": 1.0, "source_file": "assets/gs.png"}]; | |
| // afterDrawing passes ctx already transformed to network coordinate space. | |
| // Draw node positions raw — no manual pan/zoom/DPR math needed. | |
| network.on('afterDrawing', function(ctx) { | |
| hyperedges.forEach(h => { | |
| const positions = h.nodes | |
| .map(nid => network.getPositions([nid])[nid]) | |
| .filter(p => p !== undefined); | |
| if (positions.length < 2) return; | |
| ctx.save(); | |
| ctx.globalAlpha = 0.12; | |
| ctx.fillStyle = '#6366f1'; | |
| ctx.strokeStyle = '#6366f1'; | |
| ctx.lineWidth = 2; | |
| ctx.beginPath(); | |
| // Centroid and expanded hull in network coordinates | |
| const cx = positions.reduce((s, p) => s + p.x, 0) / positions.length; | |
| const cy = positions.reduce((s, p) => s + p.y, 0) / positions.length; | |
| const expanded = positions.map(p => ({ | |
| x: cx + (p.x - cx) * 1.15, | |
| y: cy + (p.y - cy) * 1.15 | |
| })); | |
| ctx.moveTo(expanded[0].x, expanded[0].y); | |
| expanded.slice(1).forEach(p => ctx.lineTo(p.x, p.y)); | |
| ctx.closePath(); | |
| ctx.fill(); | |
| ctx.globalAlpha = 0.4; | |
| ctx.stroke(); | |
| // Label | |
| ctx.globalAlpha = 0.8; | |
| ctx.fillStyle = '#4f46e5'; | |
| ctx.font = 'bold 11px sans-serif'; | |
| ctx.textAlign = 'center'; | |
| ctx.fillText(h.label, cx, cy - 5); | |
| ctx.restore(); | |
| }); | |
| }); | |
| </script> | |
| </body> | |
| </html> |