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NEAR: Neighborhood Edge AggregatoR for Graph Classification
Learning graph-structured data with graph neural networks (GNNs) has bee...
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Pre-train and Learn: Preserve Global Information for Graph Neural Networks
Graph neural networks (GNNs) have shown great power in learning on attri...
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Graph Neural Network with Automorphic Equivalence Filters
Graph neural network (GNN) has recently been established as an effective...
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GraLSP: Graph Neural Networks with Local Structural Patterns
It is not until recently that graph neural networks (GNNs) are adopted t...
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Scaling Graph Neural Networks with Approximate PageRank
Graph neural networks (GNNs) have emerged as a powerful approach for sol...
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Adaptive Graph Diffusion Networks with Hop-wise Attention
Graph Neural Networks (GNNs) have received much attention recent years a...
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Spectral Graph Wavelets for Structural Role Similarity in Networks
Nodes residing in different parts of a graph can have similar structural...
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Beyond Localized Graph Neural Networks: An Attributed Motif Regularization Framework
We present InfoMotif, a new semi-supervised, motif-regularized, learning framework over graphs. We overcome two key limitations of message passing in popular graph neural networks (GNNs): localization (a k-layer GNN cannot utilize features outside the k-hop neighborhood of the labeled training nodes) and over-smoothed (structurally indistinguishable) representations. We propose the concept of attributed structural roles of nodes based on their occurrence in different network motifs, independent of network proximity. Two nodes share attributed structural roles if they participate in topologically similar motif instances over co-varying sets of attributes. Further, InfoMotif achieves architecture independence by regularizing the node representations of arbitrary GNNs via mutual information maximization. Our training curriculum dynamically prioritizes multiple motifs in the learning process without relying on distributional assumptions in the underlying graph or the learning task. We integrate three state-of-the-art GNNs in our framework, to show significant gains (3-10 gains for nodes with sparse training labels and diverse attributes in local neighborhood structures.
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