fastgraphml 0.0.0

Given an input graph the library generates graph embeddings using Low-Code built on top of PyG

fastgraphml

Given an input graph it generates Graph Embeddings using Low-Code framework built on top of PyG. The package supports training on both GPU and CPU enabled machines. Training jobs on GPUs results in much faster execution and increased in performance when it comes to handling large graphs as compared to CPUs. In addition, the framework provides tight integration with ArangoDB which is a scalable, fully managed graph database, document store and search engine in one place. Once Graph Embeddings are generated, they can be used for various downstream machine learning tasks like Node Classification, Link Prediction, Visualisation, Community Detection, Similartiy Search, Recommendation, etc.

Installation

Required Dependencies

1. PyTorch 1.12.* is required.
   • Install using previous version that matches your CUDA version: pytorch
     • To find your installed CUDA version run nvidia-smi in your terminal.
2. pyg
3. FAISS
   • Note: For FAISS-CPU one needs numba==0.53.0

Latest Release

pip install fastgraphml

Quickstart: Graph Embedding Generation

Use Case 1: Generates Graph Embeddings using the graphs stored inside ArangoDB:

Example Homogneous Graphs

from fastgraphml.graph_embeddings import SAGE, GAT
from fastgraphml.graph_embeddings import downstream_tasks
from fastgraphml import Datasets 
from arango import ArangoClient

# Initialize the ArangoDB client.
client = ArangoClient("http://127.0.0.1:8529")
db = client.db('_system', username='root', password='')

# Loading Amazon Computer Products dataset into ArangoDB
Datasets(db).load("AMAZON_COMPUTER_PRODUCTS")

# Optionally use arangodb graph
# arango_graph = db.graph('product_graph')

# metadata information of arango_graph
metagraph = {
    "vertexCollections": {
        "Computer_Products": {"x": "features", "y": "label"},
    },
    "edgeCollections": {
        "bought_together": {},
    },
}

# generating graph embeddings with 3 lines of code
model = SAGE(db,'product_graph', metagraph, embedding_size=64) # define graph embedding model
model._train(epochs=10) # train
embeddings = model.get_embeddings() # get embeddings

Example Heterogeneous Graphs

from fastgraphml.graph_embeddings import METAPATH2VEC, DMGI
from fastgraphml.graph_embeddings import downstream_tasks 
from fastgraphml import Datasets 

from arango import ArangoClient

# Initialize the ArangoDB client.
client = ArangoClient("http://127.0.0.1:8529")
db = client.db('_system', username='root')

# Loading IMDB Dataset into ArangoDB
Datasets(db).load("IMDB_X")

# Optionally use ArangoDB Graph
# arango_graph = db.graph("IMDB")

metagraph = {
    "vertexCollections": {

        "movie": { "x": "x", "y": "y"},  
        "director": {"x": "x"},
        "actor": {"x": "x"},
    },
    "edgeCollections": {
        "to": {},
    },
}
metapaths = [('movie', 'to','actor'),
             ('actor', 'to', 'movie'), ] # MAM # co-actor relationship

# generating graph embeddings with 3 lines of code
model = METAPATH2VEC(db, "IMDB_X", metagraph, metapaths, key_node='movie', embedding_size=128,
                     walk_length=5, context_size=6, walks_per_node=5, num_negative_samples=5,
                     sparse=True) # define model
model._train(epochs=10, lr=0.03) # train
embeddings = model.get_embeddings() # get embeddings

Use Case 2: Generates Graph Embeddings using PyG graphs:

from fastgraphml.graph_embeddings import SAGE, GAT
from fastgraphml.graph_embeddings import downstream_tasks 
from torch_geometric.datasets import Planetoid

# load pyg dataset
dataset = Planetoid(root='/tmp/Cora', name='Cora')
data = dataset[0]

# generating graph embeddings with 3 lines of code
model = SAGE(pyg_graph=data, embedding_size=64) # define graph embedding model
model._train(epochs=10) # train
embeddings = model.get_embeddings() # get embeddings

Models Supported

Model	Homogeneous	Heterogeneous	Node Features
GraphSage	✔️		✔️
GAT	✔️		✔️
Metapath2Vec		✔️
DMGI		✔️	✔️

Quickstart: Downstream Tasks

In addition, the library also provides various low-code helper methods to carry out number of downstream tasks such as visualisation, similarity search (recommendation) , and link prediction (to be added soon).

Downstream Task 1: Graph Embedding Visualisation

This method helps in visualization of generated Graph Embeddings by reducing them 2 dimensions using U-Map.

Example

# amazon computers dataset
class_names = {0: 'Desktops',1: 'Data Storage',2: 'Laptops',3: 'Monitors',4: 'Computer Components',
 5: 'Video Projectors',6: 'Routers',7: 'Tablets',8: 'Networking Products',9: 'Webcams'}
# with one line of code
downstream_tasks.visualize_embeddings(model.G, embeddings, class_mapping=class_names, emb_percent=0.1) # model.G is PyG data object

Downstream Task 2: Scalable Similarity Search with Faiss

Faiss is a tool developed by Facebook that performs similarity search in sets of vectors of any size, up to ones that possibly do not fit in RAM. We support two types of search for now:

1. exact search: For precise similarity search but at the cost of scalability.
2. approx search: For scalable similarity search but at the cost of some precision loss.

Example 1

downstream_tasks.similarity_search(embeddings, top_k_nbors=10, nlist=10, search_type='exact')

Example 2

If nearest_nbors_search=True, store_embeddings method saves generated Graph Embeddings in ArangoDB along with top_k nearest neighbors (node ids with similar embeddings) and their corresponding similarity scores (i.e. cosine distance).

model.graph_util.store_embeddings(embeddings, collection_name=None, batch_size=100, class_mapping=None, 
        nearest_nbors_search=False, top_k_nbors=10, nlist=10, search_type='exact')