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Title: Capsule Graph Neural Network

Paper content:
The high-quality node embeddings learned from the Graph Neural Networks (GNNs) have been applied to a wide range of node-based applications and some of them have achieved state-of-the-art (SOTA) performance.
However, when applying node embeddings learned from GNNs to generate graph embeddings, the scalar node representation may not suffice to preserve the node/graph properties efficiently, resulting in sub-optimal graph embeddings.
Inspired by the Capsule Neural Network (CapsNet), we propose the Capsule Graph Neural Network (CapsGNN), which adopts the concept of capsules to address the weakness in existing GNN-based graph embeddings algorithms.
By extracting node features in the form of capsules, routing mechanism can be utilized to capture important information at the graph level.
As a result, our model generates multiple embeddings for each graph to capture graph properties from different aspects.
The attention module incorporated in CapsGNN is used to tackle graphs with various sizes which also enables the model to focus on critical parts of the graphs.
Our extensive evaluations with 10 graph-structured datasets demonstrate that CapsGNN has a powerful mechanism that operates to capture macroscopic properties of the whole graph by data-driven.
It outperforms other SOTA techniques on several graph classification tasks, by virtue of the new instrument.
GNN is a general type of deep-learning architectures that can be directly applied to structured data.
These architectures are mainly generalized from other well-established deep-learning models like CNN BID9 and RNN BID12 .
In this paper, we mainly focus on Convolution-based Graph Neural Networks which attract increasing interest recently.
Convolution operation can be embedded into Graph Neural Networks from spectral or spatial perspective.
BID1 defines the convolution operation in the Fourier domain which needs to calculate the eigendecomposition of the graph Laplacian.
This method is computationally expensive and the filters they defined are non-spatially localized.
Later, BID4 introduces Chebyshev expansion of the graph Laplacian to avoid computing eigenvectors and BID8 proposes to do convolution within 1-step neighbor nodes to reduce the complexity.
From the spatial perspective, BID3 and propose to define a node receptive-field and do convolution within this field during which the information of each node as well as their neighbor nodes is gathered and new representation of each node is generated through an activation function.
Both of these two perspectives perform well in node representation learning and a number of variants BID20 are developed based on the convolution idea and some of them have proven to achieve SOTA in various tasks.The success of GNN in node representation learning has inspired many deep-learning-based approaches to leverage on node embeddings extracted from GNN to generate graph embeddings for graph-based applications.
However, during this procedure, the learned representation of each node will be considered as multiple individual scalar features instead of one vector.
For example, applies element-wise max-pooling to nodes embeddings when generating graph embeddings, BID22 generates graph embeddings by computing the element-wise covariance of all nodes.
These operations indicate that the authors capture node features in the form of scalar when they generate graph embeddings which may not suffice to preserve the node/graph properties efficiently.To build high-quality graph embeddings, it is important to not only detect the presence of different structures around each node but also preserve their detailed properties such as position, direction, connection, etc.
However, encoding these properties information in the form of scalar means activating elements in a vector one-by-one which is exponentially less efficient than encoding them with distributed representations.
This has been identified discussed in BID16 .
Inspired by CapsNet, we propose to extend scalar to vector during the procedure of applying GNN to graph representation learning.
Compared with scalar-based neural network, vector-based neural network preserves the information of node/graph properties more efficiently.
The technique for extracting features in the form of vectors is proposed in BID5 and improved in BID16 and BID6 .
This technique is mainly devised for image processing.
In their work, the extracted vector is referred to as capsule (a group of neurons in neural network), so we follow the same notation in our work.
Introducing capsules allows us to use routing mechanism to generate high-level features which we believe is a more efficient way for features encoding.
Compared with max-pooling in CNN in which all information will be dropped except for the most active one, routing preserves all the information from low-level capsules and routes them to the closest high-level capsules.
Besides, this allows to model each graph with multiple embeddings and each embedding reflects different properties of the graph.
This is more representative than only one embedding used in other scalar-based approaches.In this paper, we propose Capsule Graph Neural Network (CapsGNN), a novel deep learning architecture, which is inspired by CapsNet and uses node features extracted from GNN to generate high-quality graph embeddings.
In this architecture, each graph is represented as multiple embeddings and each embedding reflects the graph properties from different aspects.
More specifically, basic node features are extracted in the form of capsules through GNN and routing mechanism is applied to generate high-level graph capsules as well as class capsules.
In the procedure of generating graph capsules, an Attention Module can be applied to tackle graphs in various sizes.
It also assigns different weights to each capsule of each node so that this model focuses on critical parts of the graph.
We validate the performance of generated graph embeddings on classification task over 5 biological datasets and 5 social datasets.
CapsGNN achieves SOTA performance on 6 out of 10 benchmark datasets and comparable results on the rest.
T-SNE BID11 ) is used to visualize the learned graph embeddings and the results show that different graph capsules indeed capture different information of the graphs.
We have proposed CapsGNN, a novel framework that fuses capsules theory into GNN for more efficient graph representation learning.
Inspired by CapsNet, the concepts of capsules are introduced in this architecture to extract features in the form of vectors on the basis of nodes features extracted from GNN.
As a result, one graph is represented as multiple embeddings and each embedding captures different aspects of the graph properties.
The generated graph and class capsules can preserve not only the classification-related information but also other information with respect to graph properties which might be useful in the follow-up work and we leave this to be explored in the future.
We believe this is a novel, efficient and powerful data-driven method to represent high-dimensional data such as graphs.
Our model has successfully achieved better or comparable performance when compared with other SOTA algorithms on 6 out of 10 graph classification tasks especially on social datasets.
Compared with similar scalar-based architectures, CapsGNN is more efficient in encoding features and this would be very beneficial for processing large datasets.

assistant: text: Inspired by CapsNet, we propose a novel architecture for graph embeddings on the basis of node features extracted from GNN.