decentralized-gnn

A package for implementing and simulating decentralized Graph Neural Network algorithms for classification of peer-to-peer nodes. Developed code supports the publication p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks.

⚡ Quick Start

To generate a local instance of a decentralized learning device:

from decentralized.devices import GossipDevice
from decentralized.mergers import SlowMerge
from learning.nn import MLP
node = ... # a node identifier object (can be any object)
features = ... # feature vector, should have the same length for each device
labels = ... # one hot encoding of class labels, zeroes if no label is known
predictor = MLP(features.shape[0], labels.shape[0])  # or load a pretrained model with
device = GossipDevice(node, predictor, features, labels, gossip_merge=SlowMerge)

In this code, the type of the device (GossipDevice)and the variable merge protocol (SlowMerge) work together to define a decentralized learning seting for a Graph Neural Network that runs on and takes account of unstructured peer-to-peer links of uncertain availability.

Then, when possible (e.g. at worst, whenever devices send messages to the others for other reasons) perform the following information exchange scheme between linked devices u and v:

send = u.send()
receive = v.receive(u.name, send)
u.ack(v.name, receive)

🛠️ Simulations

Simulations on many devices automatically generated by existing datasets can be easily set up and run per the following code:

from decentralized.devices import GossipDevice
from decentralized.mergers import AvgMerge
from decentralized.simulation import create_network

dataset_name = ... # "cora", "citeseer" or "pubmed"
network, test_labels = create_network(dataset_name, 
                                      GossipDevice,
                                      pretrained=False,
                                      gossip_merge=AvgMerge,
                                      gossip_pull=False,
                                      seed=0)
for epoch in range(800):
    network.round()
    accuracy_base = sum(1. if network.devices[u].predict(False) == label else 0 for u, label in test_labels.items()) / len(test_labels)
    accuracy = sum(1. if network.devices[u].predict() == label else 0 for u, label in test_labels.items()) / len(test_labels)
    print(f"Epoch {epoch} \t Acc {accuracy:.3f} \t Base acc {accuracy_base:.3f}")

In the above code, datasets are automatically downloaded using DGL's interface. Then, devices are instantiated given desired setting preferences.

⚠️ Some merge schemes take up a lot of memory to simulate.

📓 Citation

@article{krasanakis2022p2pgnn,
  title={p2pgnn: A decentralized graph neural network for node classification in peer-to-peer networks},
  author={Krasanakis, Emmanouil and Papadopoulos, Symeon and Kompatsiaris, Ioannis},
  journal={IEEE Access},
  volume={10},
  pages={34755--34765},
  year={2022},
  publisher={IEEE}
}