Graph Convolutional Network (GCN) has shown strong effectiveness in graph learning tasks. However, GCN faces challenges in flexibility due to the fact of requiring the full graph Laplacian available in the training phase. Moreover, with the depth of layers increases, the computational and memory cost of GCN grows explosively on account of the recursive neighborhood expansion , which leads to a limitation in processing large graphs . To tackle these issues, we take advantage of image processing in agility and present Node2Img , a flexible architecture for large-scale graph learning. Node2Img maps the nodes to "images" (i.e. grid-like data in Euclidean space) which can be the inputs of Convolutional Neural Network (CNN) . Instead of leveraging the fixed whole network as a batch to train the model, Node2Img supports a more efficacious framework in practice, where the batch size can be set elastically and the data in the same batch can be calculated parallelly. Specifically, by ranking each node's influence through degree, Node2Img selects the most influential first-order as well as second-order neighbors with central node fusion information to construct the grid-like data. For further improving the efficiency of downstream tasks, a simple CNN-based neural network is employed to capture the significant information from the Euclidean grids. Additionally, the attention mechanism is implemented, which enables implicitly specifying the different weights for neighboring nodes with different influences. Extensive experiments on real graphs' transductive and inductive learning tasks  demonstrate the superiority of the proposed Node2Img model against the state-of-the-art GCN-based approaches.
Graph Convolutional Network (GCN) has been widely used in graph learning tasks. However, GCN-based models (GCNs) is an inherently coupled training framework repetitively conducting the complex neighboring aggregation, which leads to the limitation of flexibility in processing large-scale graph. With the depth of layers increases, the computational and memory cost of GCNs grow explosively due to the recursive neighborhood expansion . To tackle these issues, we present Node2Grids , a flexible uncoupled training framework that leverages the independent mapped data for obtaining the embedding . Instead of directly processing the coupled nodes as GCNs, Node2Grids supports a more efficacious method in practice, mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resource while achieving comparable results with the leading GCN-based models. Specifically, by ranking each node's influence through degree, Node2Grids selects the most influential first-order as well as second-order neighbors with central node fusion information to construct the grid-like data. For further improving the efficiency of downstream tasks, a simple CNN-based neural network is employed to capture the significant information from the mapped grid-like data. Moreover, the grid-level attention mechanism is implemented, which enables implicitly specifying the different weights for neighboring nodes with different influences. In addition to the typical transductive and inductive learning tasks , we also verify our framework on million-scale graphs to demonstrate the superiority of the proposed Node2Grids model against the state-of-the-art GCN-based approaches.
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shown strong effectiveness
been widely used
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GCN faces challenges in flexibility due to the fact of requiring the full graph Laplacian available in the training phase. Moreover, with
GCN-based models (GCNs) is an inherently coupled training framework repetitively conducting the complex neighboring aggregation, which leads to the limitation of flexibility in processing large-scale graph. With
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GCN grows explosively on account of
GCNs grow explosively due to
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, which leads to a limitation in processing large graphs
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take advantage of image processing in agility and present Node2Img
present Node2Grids
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architecture for large-scale graph learning. Node2Img maps the nodes to "images" (i.e. grid-like data in Euclidean space) which can be the inputs of Convolutional Neural Network (CNN)
uncoupled training framework that leverages the independent mapped data for obtaining the embedding
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leveraging the fixed whole network as a batch to train the model, Node2Img
directly processing the coupled nodes as GCNs, Node2Grids
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framework
method
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where the batch size can be set elastically and the data in the same batch can be calculated parallelly.
mapping the coupled graph data into the independent grid-like data which can be fed into the efficient Convolutional Neural Network (CNN). This simple but valid strategy significantly saves memory and computational resource while achieving comparable results with the leading GCN-based models.
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Node2Img
Node2Grids
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Euclidean grids. Additionally, the
mapped grid-like data. Moreover, the grid-level
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Extensive experiments on real graphs'
In addition to the typical
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, we also verify our framework on million-scale graphs to
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Node2Img
Node2Grids