fixtures/small_graph.json

{
  "graph": {
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      {
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        "kind": "concept",
        "label": "Gradient Descent",
        "summary": "Iterative optimization that steps parameters downhill along the loss gradient."
      },
      {
        "id": "backpropagation",
        "kind": "concept",
        "label": "Backpropagation",
        "summary": "Reverse-mode autodiff that computes loss gradients through a network."
      },
      {
        "id": "chain_rule",
        "kind": "concept",
        "label": "Chain Rule",
        "summary": "Calculus rule for differentiating composed functions."
      },
      {
        "id": "neural_network",
        "kind": "concept",
        "label": "Neural Network",
        "summary": "Layered function approximator built from weighted connections."
      },
      {
        "id": "loss_function",
        "kind": "concept",
        "label": "Loss Function",
        "summary": "Scalar objective that quantifies prediction error."
      },
      {
        "id": "learning_rate",
        "kind": "concept",
        "label": "Learning Rate",
        "summary": "Step-size hyperparameter scaling each gradient update."
      },
      {
        "id": "overfitting",
        "kind": "concept",
        "label": "Overfitting",
        "summary": "When a model memorizes noise instead of generalizable signal."
      },
      {
        "id": "regularization",
        "kind": "concept",
        "label": "Regularization",
        "summary": "Techniques that penalize complexity to improve generalization."
      }
    ],
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      { "id": "e2", "source": "backpropagation", "target": "neural_network", "type": "explains", "label": "trains" },
      {
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        "label": "defines objective for"
      },
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      {
        "id": "e6",
        "source": "gradient_descent",
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      },
      { "id": "e7", "source": "neural_network", "target": "overfitting", "type": "related_to", "label": "can suffer" },
      {
        "id": "e8",
        "source": "regularization",
        "target": "overfitting",
        "type": "contradicts",
        "label": "counteracts"
      },
      {
        "id": "e9",
        "source": "regularization",
        "target": "neural_network",
        "type": "related_to",
        "label": "constrains"
      },
      {
        "id": "e10",
        "source": "loss_function",
        "target": "neural_network",
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        "label": "evaluates"
      },
      {
        "id": "e11",
        "source": "learning_rate",
        "target": "loss_function",
        "type": "related_to",
        "label": "affects descent on"
      },
      { "id": "e12", "source": "chain_rule", "target": "gradient_descent", "type": "related_to", "label": "supports" }
    ],
    "claims": [
      {
        "id": "c1",
        "claim_type": "note",
        "target_id": "gradient_descent",
        "text": "The optimization workhorse behind most deep learning training.",
        "origin": "user",
        "support_state": "verified",
        "review_state": "accepted"
      },
      {
        "id": "c2",
        "claim_type": "note",
        "target_id": "backpropagation",
        "text": "Makes gradient computation efficient via reverse-mode automatic differentiation.",
        "origin": "user",
        "support_state": "unverified",
        "review_state": "pending"
      },
      {
        "id": "c3",
        "claim_type": "note",
        "target_id": "overfitting",
        "text": "A model that overfits memorizes noise rather than the underlying signal.",
        "origin": "user",
        "support_state": "unverified",
        "review_state": "pending"
      }
    ],
    "metadata": {
      "schema_version": "0.1.0",
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      "edge_count": 12
    }
  },
  "scene": {
    "scene_id": "small_graph_initial",
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    "visible_edge_ids": ["e1", "e2", "e3", "e4", "e6", "e10", "e12"],
    "fogged_node_ids": ["learning_rate", "overfitting", "regularization"],
    "highlighted_ids": [],
    "selected_id": "",
    "callouts": [],
    "node_positions": {
      "gradient_descent": [0.0, 0.0],
      "backpropagation": [200.0, -80.0],
      "chain_rule": [400.0, -40.0],
      "neural_network": [120.0, 180.0],
      "loss_function": [-180.0, 120.0]
    }
  }
}