Pros of Sonnet

• More general-purpose neural network library, suitable for a wider range of applications
• Offers a higher level of abstraction, making it easier to build complex models
• Provides a larger set of pre-built modules and layers

Cons of Sonnet

• Less specialized for graph-based tasks compared to Graph Nets
• May require more setup and configuration for graph-specific operations
• Potentially steeper learning curve for users focused solely on graph neural networks

Code Comparison

Sonnet example:

import sonnet as snt

linear = snt.Linear(output_size=10)
output = linear(input_tensor)

Graph Nets example:

import graph_nets as gn

graph = gn.graphs.GraphsTuple(...)
output = gn.modules.GraphNetwork()(graph)

Both libraries are built on top of TensorFlow and offer modular approaches to building neural networks. However, Graph Nets is specifically designed for graph-based machine learning tasks, while Sonnet provides a more general-purpose toolkit for building neural network components.

Pros of PyTorch Geometric

• Larger community and more frequent updates
• Extensive documentation and tutorials
• Wider range of implemented graph neural network models

Cons of PyTorch Geometric

• Steeper learning curve for beginners
• Less focus on relational graph networks

Code Comparison

PyTorch Geometric:

import torch
from torch_geometric.nn import GCNConv

class GCN(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(num_features, 16)
        self.conv2 = GCNConv(16, num_classes)

Graph Nets:

import graph_nets as gn
import sonnet as snt

class GNN(snt.Module):
    def __init__(self):
        super().__init__()
        self.graph_network = gn.modules.GraphNetwork(
            edge_model_fn=lambda: snt.nets.MLP([32, 32]),
            node_model_fn=lambda: snt.nets.MLP([32, 32]),
            global_model_fn=lambda: snt.nets.MLP([32, 32]))

Both libraries offer powerful tools for working with graph neural networks, but PyTorch Geometric provides a more extensive ecosystem with a wider range of models and applications. Graph Nets, on the other hand, offers a more specialized focus on relational graph networks and integrates well with other DeepMind libraries.

Pros of DGL

• Broader framework support (PyTorch, MXNet, TensorFlow)
• More extensive documentation and tutorials
• Larger community and more frequent updates

Cons of DGL

• Steeper learning curve for beginners
• Less focus on specific graph neural network architectures

Code Comparison

DGL:

import dgl
import torch

g = dgl.graph(([0, 1], [1, 2]))
g.ndata['h'] = torch.ones(3, 5)
g.edata['w'] = torch.ones(2, 3)

Graph Nets:

import graph_nets as gn
import tensorflow as tf

graph = gn.graphs.GraphsTuple(
    nodes=tf.ones((3, 5)),
    edges=tf.ones((2, 3)),
    globals=None,
    senders=[0, 1],
    receivers=[1, 2],
    n_node=tf.constant([3]),
    n_edge=tf.constant([2])
)

DGL offers a more concise API for graph creation and attribute assignment, while Graph Nets requires a more explicit definition of graph structure. DGL's approach may be more intuitive for users familiar with PyTorch, while Graph Nets aligns closely with TensorFlow conventions.

Pros of OGB

• Provides a comprehensive benchmark suite for graph machine learning tasks
• Offers a wide range of real-world datasets across various domains
• Includes standardized evaluation protocols for fair comparisons

Cons of OGB

• Focuses primarily on benchmarking rather than providing a flexible framework for building graph neural networks
• May have a steeper learning curve for users new to graph machine learning

Code Comparison

OGB example:

from ogb.nodeproppred import NodePropPredDataset

dataset = NodePropPredDataset(name = "ogbn-arxiv")
graph, label = dataset[0]

Graph Nets example:

import graph_nets as gn

graph = gn.graphs.GraphsTuple(
    nodes=tf.constant([[1.0], [2.0], [3.0]]),
    edges=tf.constant([[1.0], [2.0]]),
    globals=tf.constant([[1.0]]),
    senders=tf.constant([0, 1]),
    receivers=tf.constant([1, 2]),
    n_node=tf.constant([3]),
    n_edge=tf.constant([2])
)

The OGB code focuses on loading pre-defined datasets, while Graph Nets provides a more flexible approach for creating custom graph structures.

Pros of Spektral

• Built on top of Keras, offering seamless integration with TensorFlow
• Provides a wider range of graph neural network layers and models
• Includes built-in datasets and data loading utilities for graph-based tasks

Cons of Spektral

• Less focus on relational reasoning and more on traditional graph neural networks
• May have a steeper learning curve for users not familiar with Keras/TensorFlow

Code Comparison

Spektral:

from spektral.layers import GCNConv
from tensorflow.keras.models import Model

class GCN(Model):
    def __init__(self):
        super().__init__()
        self.conv1 = GCNConv(16, activation='relu')
        self.conv2 = GCNConv(10, activation='softmax')

Graph Nets:

import graph_nets as gn
import sonnet as snt

class GNN(snt.Module):
    def __init__(self):
        super().__init__()
        self.graph_network = gn.modules.GraphNetwork(
            edge_model_fn=lambda: snt.nets.MLP([16, 16]),
            node_model_fn=lambda: snt.nets.MLP([16, 16]),
            global_model_fn=lambda: snt.nets.MLP([16, 16]))