Feature-Attention Graph Convolutional Networks for Noise Resilient Learning

• URL: http://arxiv.org/abs/1912.11755v1
• Date: Thu, 26 Dec 2019 02:51:55 GMT
• Title: Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
• Authors: Min Shi, Yufei Tang, Xingquan Zhu and Jianxun Liu
• Abstract summary: We propose FA-GCN, a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than state-of-the-art methods on both noise-free and noisy networks.
• Score: 20.059242373860013
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have been made to advance feature learning from networks, including the most recent Graph Convolutional Networks (GCN) or attention GCN, by integrating node content and topology structures. However, all existing methods consider networks as error-free sources and treat feature content in each node as independent and equally important to model node relations. The erroneous node content, combined with sparse features, provide essential challenges for existing methods to be used on real-world noisy networks. In this paper, we propose FA-GCN, a feature-attention graph convolution learning framework, to handle networks with noisy and sparse node content. To tackle noise and sparse content in each node, FA-GCN first employs a long short-term memory (LSTM) network to learn dense representation for each feature. To model interactions between neighboring nodes, a feature-attention mechanism is introduced to allow neighboring nodes learn and vary feature importance, with respect to their connections. By using spectral-based graph convolution aggregation process, each node is allowed to concentrate more on the most determining neighborhood features aligned with the corresponding learning task. Experiments and validations, w.r.t. different noise levels, demonstrate that FA-GCN achieves better performance than state-of-the-art methods on both noise-free and noisy networks.

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