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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Reference:
- [graphrag](https://github.com/microsoft/graphrag)
"""
from typing import Any
import numpy as np
import networkx as nx
from graphrag.leiden import stable_largest_connected_component
@dataclass
class NodeEmbeddings:
"""Node embeddings class definition."""
nodes: list[str]
embeddings: np.ndarray
def embed_nod2vec(
graph: nx.Graph | nx.DiGraph,
dimensions: int = 1536,
num_walks: int = 10,
walk_length: int = 40,
window_size: int = 2,
iterations: int = 3,
random_seed: int = 86,
) -> NodeEmbeddings:
"""Generate node embeddings using Node2Vec."""
# generate embedding
lcc_tensors = gc.embed.node2vec_embed( # type: ignore
graph=graph,
dimensions=dimensions,
window_size=window_size,
iterations=iterations,
num_walks=num_walks,
walk_length=walk_length,
random_seed=random_seed,
)
return NodeEmbeddings(embeddings=lcc_tensors[0], nodes=lcc_tensors[1])
def run(graph: nx.Graph, args: dict[str, Any]) -> NodeEmbeddings:
"""Run method definition."""
if args.get("use_lcc", True):
graph = stable_largest_connected_component(graph)
# create graph embedding using node2vec
embeddings = embed_nod2vec(
graph=graph,
dimensions=args.get("dimensions", 1536),
num_walks=args.get("num_walks", 10),
walk_length=args.get("walk_length", 40),
window_size=args.get("window_size", 2),
iterations=args.get("iterations", 3),
random_seed=args.get("random_seed", 86),
)
pairs = zip(embeddings.nodes, embeddings.embeddings.tolist(), strict=True)
sorted_pairs = sorted(pairs, key=lambda x: x[0])
return dict(sorted_pairs) |