GANDALF

Graph Analysis Navigator for Discovery And Link Finding

Features

• Compressed Sparse Row (CSR) graph representation for memory efficiency
• Bidirectional search for optimal performance
• O(1) property lookups via hash indexing
• Predicate filtering to reduce path explosion
• Batch property enrichment for fast results
• Diagnostic tools to understand path counts

Installation

pip install -e .

Quick Start

Build a graph from JSONL

from gandalf import build_graph_from_jsonl

# Build with ontology filtering
graph = build_graph_from_jsonl(
    edges_path="data/raw/edges.jsonl",
    nodes_path="data/raw/nodes.jsonl",
    excluded_predicates={'biolink:subclass_of'}
)

# Save for fast loading
graph.save("data/processed/graph_filtered.pkl")

Query paths

from gandalf import CSRGraph, find_paths

# Load graph (takes ~1-2 seconds)
graph = CSRGraph.load("data/processed/graph.pkl")

# Find paths
paths = find_paths(
    graph,
    start_id="CHEBI:45783",
    end_id="MONDO:0004979"
)

print(f"Found {len(paths)} paths")

Filter by predicates

from gandalf import find_paths_filtered

# Only mechanistic relationships
paths = find_paths_filtered(
    graph,
    start_id="CHEBI:45783",
    end_id="MONDO:0004979",
    allowed_predicates={
        'biolink:treats',
        'biolink:affects',
        'biolink:has_metabolite'
    }
)

Architecture

The package uses a three-stage pipeline:

1. Topology Search (fast) - Find all paths using indices only
2. Filtering (medium) - Apply business logic on necessary node or edge properties
3. Enrichment (batch) - Load all properties for final paths only

This separation allows filtering millions of paths before expensive property lookups.