  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.
