p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks

• URL: http://arxiv.org/abs/2111.14837v1
• Date: Mon, 29 Nov 2021 12:21:47 GMT
• Title: p2pGNN: A Decentralized Graph Neural Network for Node Classification in Peer-to-Peer Networks
• Authors: Emmanouil Krasanakis, Symeon Papadopoulos, Ioannis Kompatsiaris
• Abstract summary: We aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. We employ decoupled Graph Neural Networks (GNNs) to solve this problem. We develop an asynchronous decentralized formulation of diffusion that converges at the same predictions linearly with respect to communication rate.
• Score: 15.164084925877624
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In this work, we aim to classify nodes of unstructured peer-to-peer networks with communication uncertainty, such as users of decentralized social networks. Graph Neural Networks (GNNs) are known to improve the accuracy of simpler classifiers in centralized settings by leveraging naturally occurring network links, but graph convolutional layers are challenging to implement in decentralized settings when node neighbors are not constantly available. We address this problem by employing decoupled GNNs, where base classifier predictions and errors are diffused through graphs after training. For these, we deploy pre-trained and gossip-trained base classifiers and implement peer-to-peer graph diffusion under communication uncertainty. In particular, we develop an asynchronous decentralized formulation of diffusion that converges at the same predictions linearly with respect to communication rate. We experiment on three real-world graphs with node features and labels and simulate peer-to-peer networks with uniformly random communication frequencies; given a portion of known labels, our decentralized graph diffusion achieves comparable accuracy to centralized GNNs.

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