GIPA: A General Information Propagation Algorithm for Graph Learning
Graph neural networks (GNNs) have been widely used in graph-structured data computation, showing promising performance in various applications such as node classification, link prediction, and network recommendation. Existing works mainly focus on node-wise correlation when doing weighted aggregatio...
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Main Authors | , , , , , |
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Format | Journal Article |
Language | English |
Published |
19.01.2023
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Subjects | |
Online Access | Get full text |
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Summary: | Graph neural networks (GNNs) have been widely used in graph-structured data
computation, showing promising performance in various applications such as node
classification, link prediction, and network recommendation. Existing works
mainly focus on node-wise correlation when doing weighted aggregation of
neighboring nodes based on attention, such as dot product by the dense vectors
of two nodes. This may cause conflicting noise in nodes to be propagated when
doing information propagation. To solve this problem, we propose a General
Information Propagation Algorithm (GIPA in short), which exploits more
fine-grained information fusion including bit-wise and feature-wise
correlations based on edge features in their propagation. Specifically, the
bit-wise correlation calculates the element-wise attention weight through a
multi-layer perceptron (MLP) based on the dense representations of two nodes
and their edge; The feature-wise correlation is based on the one-hot
representations of node attribute features for feature selection. We evaluate
the performance of GIPA on the Open Graph Benchmark proteins (OGBN-proteins for
short) dataset and the Alipay dataset of Alibaba. Experimental results reveal
that GIPA outperforms the state-of-the-art models in terms of prediction
accuracy, e.g., GIPA achieves an average ROC-AUC of $0.8901\pm 0.0011$, which
is better than that of all the existing methods listed in the OGBN-proteins
leaderboard. |
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DOI: | 10.48550/arxiv.2301.08209 |