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.
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 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.
@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}
}