Topological Regularization for Graph Neural Networks Augmentation

• URL: http://arxiv.org/abs/2104.02478v1
• Date: Sat, 3 Apr 2021 01:37:44 GMT
• Title: Topological Regularization for Graph Neural Networks Augmentation
• Authors: Rui Song and Fausto Giunchiglia and Ke Zhao and Hao Xu
• Abstract summary: We propose a feature augmentation method for graph nodes based on topological regularization. We have carried out extensive experiments on a large number of datasets to prove the effectiveness of our model.
• Score: 12.190045459064413
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The complexity and non-Euclidean structure of graph data hinder the development of data augmentation methods similar to those in computer vision. In this paper, we propose a feature augmentation method for graph nodes based on topological regularization, in which topological structure information is introduced into end-to-end model. Specifically, we first obtain topology embedding of nodes through unsupervised representation learning method based on random walk. Then, the topological embedding as additional features and the original node features are input into a dual graph neural network for propagation, and two different high-order neighborhood representations of nodes are obtained. On this basis, we propose a regularization technique to bridge the differences between the two different node representations, eliminate the adverse effects caused by the topological features of graphs directly used, and greatly improve the performance. We have carried out extensive experiments on a large number of datasets to prove the effectiveness of our model.

Related papers

• Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation [1.1606619391009658]
  We propose a Topology-guided Hypergraph Transformer Network (THTN) In this model, we first formulate a hypergraph from a graph while retaining its structural essence to learn higher-order relations within the graph. We present a structure-aware self-attention mechanism that discovers the important nodes and hyperedges from both semantic and structural viewpoints.
  arXiv  Detail & Related papers  (2023-10-14T20:08:54Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• Interpolation-based Correlation Reduction Network for Semi-Supervised Graph Learning [49.94816548023729]
  We propose a novel graph contrastive learning method, termed Interpolation-based Correlation Reduction Network (ICRN) In our method, we improve the discriminative capability of the latent feature by enlarging the margin of decision boundaries. By combining the two settings, we extract rich supervision information from both the abundant unlabeled nodes and the rare yet valuable labeled nodes for discnative representation learning.
  arXiv  Detail & Related papers  (2022-06-06T14:26:34Z)
• Heterogeneous Graph Neural Networks using Self-supervised Reciprocally Contrastive Learning [102.9138736545956]
  Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs. We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies. In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
  arXiv  Detail & Related papers  (2022-04-30T12:57:02Z)
• Curvature Graph Generative Adversarial Networks [31.763904668737304]
  Generative adversarial network (GAN) is widely used for generalized and robust learning on graph data. Existing GAN-based graph representation methods generate negative samples by random walk or traverse in discrete space. CurvGAN consistently and significantly outperforms the state-of-the-art methods across multiple tasks.
  arXiv  Detail & Related papers  (2022-03-03T10:00:32Z)
• Learning to Learn Graph Topologies [27.782971146122218]
  We learn a mapping from node data to the graph structure based on the idea of learning to optimise (L2O) The model is trained in an end-to-end fashion with pairs of node data and graph samples. Experiments on both synthetic and real-world data demonstrate that our model is more efficient than classic iterative algorithms in learning a graph with specific topological properties.
  arXiv  Detail & Related papers  (2021-10-19T08:42:38Z)
• Reasoning Graph Networks for Kinship Verification: from Star-shaped to Hierarchical [85.0376670244522]
  We investigate the problem of facial kinship verification by learning hierarchical reasoning graph networks. We develop a Star-shaped Reasoning Graph Network (S-RGN) to exploit more powerful and flexible capacity. We also develop a Hierarchical Reasoning Graph Network (H-RGN) to exploit more powerful and flexible capacity.
  arXiv  Detail & Related papers  (2021-09-06T03:16:56Z)
• Self-Supervised Graph Representation Learning via Topology Transformations [61.870882736758624]
  We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data. In experiments, we apply the proposed model to the downstream node and graph classification tasks, and results show that the proposed method outperforms the state-of-the-art unsupervised approaches.
  arXiv  Detail & Related papers  (2021-05-25T06:11:03Z)
• Het-node2vec: second order random walk sampling for heterogeneous multigraphs embedding [0.9084022224205381]
  Het-node2vec is an extension of the node2vec algorithm for embedding heterogeneous graphs. We show that Het-node2vec achieves comparable or superior performance with respect to state-of-the-art methods for heterogeneous graphs in node label and edge prediction tasks.
  arXiv  Detail & Related papers  (2021-01-05T09:38:02Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.