Temporal-Symmetry Graph Scorer

The problem

It is difficult to determine if a sequence of events is consistent when both the structure of the connections and the timing of the data change.

What it does

It analyzes paths in a network and assigns a score based on how well the shape of the connections matches the timeline of the data.

Why it matters

It provides a way to verify both the structure and the chronological flow of information simultaneously.

Validation

It was run in the sandbox and it failed. run output shows an error/traceback — the artifact does NOT run clean.

$ python3 scorer.py
File "/work/scorer.py", line 25
    while current != end:
IndentationError: unexpected indent

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 66 lines, one file, standard library only.

# Temporal-Symmetry Graph Scorer
import itertools
from datetime import datetime

class GraphNode:
    def __init__(self, label, timestamp):
        self.label = label
        self.timestamp = datetime.fromisoformat(timestamp)
        self.connections = {}

    def add_connection(self, node, weight=1.0):
        self.connections[node] = weight

class TemporalSymmetryScorer:
    def __init__(self, nodes):
        self.nodes = {node.label: node for node in nodes}

    def graph_path_symmetry_score(self):
        all_paths = []
        for start, end in itertools.combinations(self.nodes.values(), 2):
            if end in start.connections:
                path = [start]
                current = start
n
                    while current != end:
                    current = next(node for node in current.connections if end in current.connections)
                    path.append(current)
                all_paths.append(path)

        if not all_paths:
            return 0.0

        label_counts = {}
        for path in all_paths:
            labels = [node.label for node in path]
            label_counts['-'.join(labels)] = label_counts.get('-'.join(labels), 0) + 1

        unique_labels = len(label_counts)
        max_count = max(label_counts.values())
        return max_count / unique_labels if unique_labels else 1.0

    def temporal_decay_score(self):
        timestamps = [node.timestamp.timestamp() for node in self.nodes.values()]
        avg_time = sum(timestamps) / len(timestamps)
        time_diffs = [(t - avg_time) ** 2 for t in timestamps]
        return 1 / (sum(time_diffs) + 1e-9)

    def combined_score(self):
        symmetry = self.graph_path_symmetry_score()
        temporal = self.temporal_decay_score()
        return 0.7 * symmetry + 0.3 * temporal

if __name__ == "__main__":
    # Initialize sample graph
    node_a = GraphNode('A', '2024-01-01T00:00:00')
    node_b = GraphNode('A', '2024-01-02T00:00:00')
    node_c = GraphNode('B', '2024-01-03T00:00:00')

    node_a.add_connection(node_b)
    node_b.add_connection(node_c)
    node_a.add_connection(node_c)

    scorer = TemporalSymmetryScorer([node_a, node_b, node_c])
    print(f"Symmetry Score: {scorer.graph_path_symmetry_score():.2f}")
    print(f"Temporal Score: {scorer.temporal_decay_score():.2f}")
    print(f"Combined Score: {scorer.combined_score():.2f}")