temporal-belief-graph 0.1.3

Probabilistic temporal belief graph for uncertain narrative timelines.

temporal-belief-graph

A lightweight Python package for modeling uncertain event orderings in narrative timelines, lore databases, historical reconstructions, and worldbuilding systems.

Instead of assuming every temporal relation is fixed, temporal-belief-graph represents event ordering as probabilistic belief edges and updates them as new evidence accumulates — using log-odds Bayesian updates that never reverse direction due to weak sources.

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Why this exists

Most graph libraries treat edges as binary: A comes before B, or it does not.
Real-world narrative and lore data is messier. Multiple sources disagree. Evidence is partial. Some orderings are guesses.

temporal-belief-graph was built to handle exactly this:

• A story timeline where fan sources and official sources conflict
• A historical reconstruction where some events have uncertain sequencing
• A worldbuilding database where causality and temporal order must be separated
• Any system where "we think A happened before B, but we are not sure" needs to be expressed and updated over time

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Installation

pip install temporal-belief-graph

Requires Python 3.10 or later. No external dependencies.

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Quick start

from tbg import BeliefGraph, EventNode, PriorConfig
from tbg import BayesianUpdater, Evidence, Validator
from tbg import graph_to_json, graph_from_json
from tbg.report import graph_to_markdown, graph_to_mermaid

# 1. Configure the graph
config = PriorConfig(
    default_p=0.5,
    source_weights={"official_lore": 1.5, "fan_wiki": 0.7},
)
graph = BeliefGraph(prior_config=config)

# 2. Add events
graph.add_node(EventNode(id="kain_incident", label="Kain Incident", era="First Rift War"))
graph.add_node(EventNode(id="rift_opening",  label="Rift Opening",  era="First Rift War"))

# 3. Add an uncertain edge (p=0.5 means order is unknown)
graph.init_uniform("kain_incident", "rift_opening")

# 4. Update belief with evidence
updater = BayesianUpdater(graph)
updater.update_edge(
    "kain_incident", "rift_opening",
    Evidence(key="official_001", supports_forward=True, strength=0.9, source="official_lore"),
)
updater.update_edge(
    "kain_incident", "rift_opening",
    Evidence(key="fan_002", supports_forward=False, strength=0.6, source="fan_wiki"),
)

edge = graph.get_edge("kain_incident", "rift_opening")
print(edge.p_forward)   # updated belief: P(kain_incident → rift_opening)

# 5. Explain the current belief state
print(updater.explain_edge("kain_incident", "rift_opening"))

# 6. Validate the graph
result = Validator().validate(graph)
print(result.is_valid)
print(result.warnings)

# 7. Export
graph_to_json(graph, "graph.json")
print(graph_to_mermaid(graph))

A full working example is available in examples/basic_usage.py.

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Core concepts

EventNode

A single event in the graph. Every node carries an era — a concrete arc, chapter, or period name. Vague era labels like "present" or "past" are flagged by the validator.

EventNode(id="my_event", label="The Great Collapse", era="Third Age")

BeliefEdge

A directed edge from source_id to target_id carrying p_forward: the probability that the source event occurs before the target event.

p_forward	Meaning
0.5	No information — uniform prior
0.9	Strong belief that source comes first
0.1	Strong belief that target comes first

BayesianUpdater

Updates edge probabilities using log-odds steps:

new_logit = old_logit + direction × strength × source_weight × learning_rate
p_new     = sigmoid(new_logit)

Key property: a source with source_weight < 1.0 produces a smaller update in the same direction — it never flips the direction of evidence.

Ensemble update

When multiple sources each propose a p_forward value, ensemble_update combines them via weighted average before applying a single Bayesian step:

updater.ensemble_update(
    "event_a", "event_b",
    claims=[
        (0.9, 1.5),   # official source claims 0.9, weight 1.5
        (0.6, 0.8),   # secondary source claims 0.6, weight 0.8
        (0.3, 0.5),   # weak source claims 0.3, weight 0.5
    ],
)

Validator

Runs seven checks and returns a ValidationResult with errors and warnings:

Check	Level
pseudo_era — vague era label used	error
cycle — confirmed edges form a directed cycle	error
contradiction — two opposing edges both highly confident	error
missing_era — node has no era value	warning
missing_evidence — edge probability changed without evidence	warning
overconfident_edge — high probability with very few updates	warning
isolated_node — node has no incident edges	warning

result = Validator().validate(graph)
result.raise_if_errors()   # raises ValueError if any errors exist

JSON export / import

from tbg import graph_to_json, graph_from_json

graph_to_json(graph, "graph.json")
restored = graph_from_json("graph.json")

Markdown and Mermaid report

from tbg.report import graph_to_markdown, graph_to_mermaid, save_markdown, save_mermaid

print(graph_to_markdown(graph))   # full Markdown report
print(graph_to_mermaid(graph))    # Mermaid flowchart diagram
save_markdown(graph, "report.md")
save_mermaid(graph, "diagram.mmd")

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Package structure

tbg/
├── schema.py      EventNode, BeliefEdge, EvidenceRecord, PriorConfig
├── graph.py       BeliefGraph
├── bayesian.py    BayesianUpdater, Evidence
├── validator.py   Validator, ValidationResult
├── io.py          graph_to_json, graph_from_json
└── report.py      graph_to_markdown, graph_to_mermaid

examples/
└── basic_usage.py End-to-end example

docs/
└── model.md       Mathematical model documentation

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Design principles

Probabilistic by default. Every edge starts uncertain. Certainty is earned through accumulated evidence, not assumed.

Direction-safe updates. A weak source supporting forward always moves p_forward up — never down. Source weight controls magnitude, not direction.

Explainable history. Every probability update is recorded as an EvidenceRecord on the edge. You can always reconstruct why the current value is what it is.

No hard deletes. The graph is append-friendly. Contradictions are surfaced by the validator, not silently overwritten.

No external dependencies. Pure Python standard library only.

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Development

Install the package in editable mode with development dependencies:

pip install -e ".[dev]"

Run the test suite:

pytest

Run tests across all supported Python versions:

tox

Build the package:

python -m build

This package is currently in alpha. APIs may change before version 1.0.0.

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Contributing

Contributions, issues, and feature requests are welcome.
Please open an issue before submitting a pull request.

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Contributors

Handle	GitHub	Role
lajjadred	@lajjadred	Project lead
이채문	@CHML-real	Mathematical algorithm development
CUBE	@90cube	Idea proposal and data collection

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License

MIT License. See LICENSE for details.