sandx-graph 0.1.1

Graph intelligence engine — knowledge graph construction, neighborhood consensus, semantic linkage

sandx-graph

Graph intelligence engine — knowledge graph construction, neighborhood consensus, semantic linkage.

Part of the SandX Lab computational infrastructure ecosystem.

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What It Does

sandx-graph is the graph reasoning layer that operates downstream of sandx-er. It constructs knowledge graphs from resolved entity clusters and computes neighborhood consensus — a measure of how strongly each node's local neighborhood agrees.

sandx-er clusters  →  GraphBuilder  →  KnowledgeGraph  →  ConsensusEngine  →  consensus scores

Status

v0.1 — Working

Component	Status
GraphBuilder — construct graphs from clusters, DataFrames, similarity matrices	Working
KnowledgeGraph — undirected weighted graph with adjacency traversal	Working
ConsensusEngine — BFS neighborhood consensus computation	Working
NetworkX export	Working (optional dep)
PyPI package	Working

Installation

pip install sandx-graph

Or from source:

git clone https://github.com/sandxlab/sandx-graph
cd sandx-graph
pip install -e ".[dev]"

For NetworkX export:

pip install "sandx-graph[networkx]"

Demo

pip install sandx-graph
python -m examples.graph_consensus

Constructs a 5-node knowledge graph of tech companies, scores neighborhood consensus, and prints the weighted edge list — no external data required.

Quick Start

From sandx-er resolution output

import pandas as pd
from sandx_er import EntityResolver
from sandx_graph import GraphBuilder, ConsensusEngine

# Resolve records into entity clusters
records = pd.DataFrame({
    "name": ["Acme Corp", "Acme Corp.", "GlobalTech Inc", "Global Tech"],
    "city": ["Boston", "Boston", "New York", "New York"],
})
er = EntityResolver(blocking="lsh", similarity="jaccard", threshold=0.4)
result = er.resolve(records)

# Build knowledge graph from resolved clusters
builder = GraphBuilder()
graph = builder.from_clusters(result.clusters)
print(graph)  # KnowledgeGraph(n_nodes=2, n_edges=0)

# Add relationship edges (here via similarity matrix)
import numpy as np
ids = [c.canonical_id for c in result.clusters]
sim = np.array([[1.0, 0.3], [0.3, 1.0]])
graph = builder.from_similarity_matrix(ids, sim, threshold=0.5)

From DataFrames

import pandas as pd
from sandx_graph import GraphBuilder, ConsensusEngine

nodes_df = pd.DataFrame({"node_id": ["e1", "e2", "e3"], "label": ["Acme", "GlobalTech", "Initech"]})
edges_df = pd.DataFrame({"source": ["e1", "e2"], "target": ["e2", "e3"], "weight": [0.85, 0.62]})

builder = GraphBuilder()
graph = builder.from_dataframe(nodes_df, edges_df)

# Compute neighborhood consensus
engine = ConsensusEngine(graph)
score = engine.compute("e1", depth=2)
print(score)
# ConsensusScore(node='e1', score=0.735, support=2, conflict=0)

# Batch over all nodes
all_scores = engine.compute_all(depth=1)
stats = engine.summary(depth=1)
print(stats)
# {'mean': 0.735, 'median': 0.735, 'std': 0.115, 'min': 0.620, 'max': 0.850}

Consensus Score

ConsensusEngine runs BFS from a node up to a given depth, collecting all edge weights encountered. The consensus score is the weighted mean of those edges.

Score	Interpretation
→ 1.0	Node connected to high-confidence, strongly agreeing neighbors
→ 0.5	Mixed neighborhood — some support, some conflict
→ 0.0	Weak or conflicting edges throughout the neighborhood

Isolated nodes (degree 0) return score 1.0 by convention.

API Reference

GraphBuilder

Method	Description
from_clusters(clusters)	One node per sandx-er EntityCluster; no edges
from_dataframe(nodes_df, edges_df, ...)	Build from node/edge DataFrames
from_similarity_matrix(ids, similarity, threshold)	Build from pairwise similarity matrix

KnowledgeGraph

Attribute / Method	Description
n_nodes, n_edges	Graph size
nodes	Dict of node_id → attribute dict
edges	List of (source, target, weight) triples
neighbors(node_id)	Adjacent node IDs
neighbors_weighted(node_id)	(neighbor_id, weight) pairs
degree(node_id)	Number of incident edges
has_node(node_id), has_edge(a, b)	Membership checks
to_dataframe()	Edge list as pandas DataFrame
to_networkx()	Export to NetworkX Graph

ConsensusEngine

Method	Description
compute(node_id, depth=2)	Consensus score for one node
compute_all(depth=2)	Scores for all nodes
summary(depth=1)	Mean/median/std/min/max over all nodes

Related

• sandx-er — upstream entity resolution (primary input)
• sandx-embed — shared embedding infrastructure
• sandx.io — project home

License

Apache 2.0 — see LICENSE