29 research outputs found
On Filter Size in Graph Convolutional Networks
Recently, many researchers have been focusing on the definition of neural
networks for graphs. The basic component for many of these approaches remains
the graph convolution idea proposed almost a decade ago. In this paper, we
extend this basic component, following an intuition derived from the well-known
convolutional filters over multi-dimensional tensors. In particular, we derive
a simple, efficient and effective way to introduce a hyper-parameter on graph
convolutions that influences the filter size, i.e. its receptive field over the
considered graph. We show with experimental results on real-world graph
datasets that the proposed graph convolutional filter improves the predictive
performance of Deep Graph Convolutional Networks.Comment: arXiv admin note: text overlap with arXiv:1811.0693
Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks
The core operation of current Graph Neural Networks (GNNs) is the aggregation
enabled by the graph Laplacian or message passing, which filters the
neighborhood node information. Though effective for various tasks, in this
paper, we show that they are potentially a problematic factor underlying all
GNN methods for learning on certain datasets, as they force the node
representations similar, making the nodes gradually lose their identity and
become indistinguishable. Hence, we augment the aggregation operations with
their dual, i.e. diversification operators that make the node more distinct and
preserve the identity. Such augmentation replaces the aggregation with a
two-channel filtering process that, in theory, is beneficial for enriching the
node representations. In practice, the proposed two-channel filters can be
easily patched on existing GNN methods with diverse training strategies,
including spectral and spatial (message passing) methods. In the experiments,
we observe desired characteristics of the models and significant performance
boost upon the baselines on 9 node classification tasks