Non-Recursive Graph Convolutional Networks
- URL: http://arxiv.org/abs/2105.03868v1
- Date: Sun, 9 May 2021 08:12:18 GMT
- Title: Non-Recursive Graph Convolutional Networks
- Authors: Hao Chen, Zengde Deng, Yue Xu, Zhoujun Li
- Abstract summary: We propose a novel architecture named Non-Recursive Graph Convolutional Network (NRGCN) to improve both the training efficiency and the learning performance of GCNs.
NRGCN represents different hops of neighbors for each node based on inner-layer aggregation and layer-independent sampling.
In this way, each node can be directly represented by concatenating the information extracted independently from each hop of its neighbors.
- Score: 33.459371861932574
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Convolutional Networks (GCNs) are powerful models for node
representation learning tasks. However, the node representation in existing GCN
models is usually generated by performing recursive neighborhood aggregation
across multiple graph convolutional layers with certain sampling methods, which
may lead to redundant feature mixing, needless information loss, and extensive
computations. Therefore, in this paper, we propose a novel architecture named
Non-Recursive Graph Convolutional Network (NRGCN) to improve both the training
efficiency and the learning performance of GCNs in the context of node
classification. Specifically, NRGCN proposes to represent different hops of
neighbors for each node based on inner-layer aggregation and layer-independent
sampling. In this way, each node can be directly represented by concatenating
the information extracted independently from each hop of its neighbors thereby
avoiding the recursive neighborhood expansion across layers. Moreover, the
layer-independent sampling and aggregation can be precomputed before the model
training, thus the training process can be accelerated considerably. Extensive
experiments on benchmark datasets verify that our NRGCN outperforms the
state-of-the-art GCN models, in terms of the node classification performance
and reliability.
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