Edgeless-GNN: Unsupervised Representation Learning for Edgeless Nodes

We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our pro...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on emerging topics in computing 2024-01, Vol.12 (1), p.1-14
Main Authors: Shin, Yong-Min, Tran, Cong, Shin, Won-Yong, Cao, Xin
Format: Article
Language:English
Subjects:
Cognitive tasks
Computation
Computation graph
Computational modeling
Computer architecture
edgeless node
Embedding
graph neural network (GNN)
Graph neural networks
Graphical representations
Image edge detection
inductive network embedding
Machine learning
Message passing
Network topologies
Nodes
Representation learning
Task analysis
Training
Unsupervised learning
Citations: Items that this one cites
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We study the problem of embedding edgeless nodes such as users who newly enter the underlying network, while using graph neural networks (GNNs) widely studied for effective representation learning of graphs. Our study is motivated by the fact that GNNs cannot be straightforwardly adopted for our problem since message passing to such edgeless nodes having no connections is impossible. To tackle this challenge, we propose Edgeless-GNN, a novel inductive framework that enables GNNs to generate node embeddings even for edgeless nodes through unsupervised learning . Specifically, we start by constructing a proxy graph based on the similarity of node attributes as the GNN's computation graph defined by the underlying network. The known network structure is used to train model parameters, whereas a topology-aware loss function is established such that our model judiciously learns the network structure by encoding positive, negative, and second-order relations between nodes. For the edgeless nodes, we inductively infer embeddings by expanding the computation graph. By evaluating the performance of various downstream machine learning tasks, we empirically demonstrate that Edgeless-GNN exhibits (a) superiority over state-of-the-art inductive network embedding methods for edgeless nodes, (b) effectiveness of our topology-aware loss function, (c) robustness to incomplete node attributes, and (d) a linear scaling with the graph size.
ISSN:2168-6750
2168-6750
DOI:10.1109/TETC.2023.3292240