Unifying Graph Convolutional Neural Networks and Label Propagation

• URL: http://arxiv.org/abs/2002.06755v1
• Date: Mon, 17 Feb 2020 03:23:13 GMT
• Title: Unifying Graph Convolutional Neural Networks and Label Propagation
• Authors: Hongwei Wang, Jure Leskovec
• Abstract summary: We study the relationship between LPA and GCN in terms of two aspects: feature/label smoothing and feature/label influence. Based on our theoretical analysis, we propose an end-to-end model that unifies GCN and LPA for node classification. Our model can also be seen as learning attention weights based on node labels, which is more task-oriented than existing feature-based attention models.
• Score: 73.82013612939507
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Label Propagation (LPA) and Graph Convolutional Neural Networks (GCN) are both message passing algorithms on graphs. Both solve the task of node classification but LPA propagates node label information across the edges of the graph, while GCN propagates and transforms node feature information. However, while conceptually similar, theoretical relation between LPA and GCN has not yet been investigated. Here we study the relationship between LPA and GCN in terms of two aspects: (1) feature/label smoothing where we analyze how the feature/label of one node is spread over its neighbors; And, (2) feature/label influence of how much the initial feature/label of one node influences the final feature/label of another node. Based on our theoretical analysis, we propose an end-to-end model that unifies GCN and LPA for node classification. In our unified model, edge weights are learnable, and the LPA serves as regularization to assist the GCN in learning proper edge weights that lead to improved classification performance. Our model can also be seen as learning attention weights based on node labels, which is more task-oriented than existing feature-based attention models. In a number of experiments on real-world graphs, our model shows superiority over state-of-the-art GCN-based methods in terms of node classification accuracy.

Related papers

• Transfer Entropy in Graph Convolutional Neural Networks [0.0]
  Graph Convolutional Networks (GCN) are Graph Neural Networks where the convolutions are applied over a graph. In this study, we address two important challenges related to GCNs: i. Oversmoothing is the degradation of the discriminative capacity of nodes as a result of repeated aggregations. We propose a new strategy for addressing these challenges in GCNs based on Transfer Entropy (TE), which measures of the amount of directed transfer of information between two time varying nodes.
  arXiv  Detail & Related papers  (2024-06-08T20:09:17Z)
• Self-Attention Empowered Graph Convolutional Network for Structure Learning and Node Embedding [5.164875580197953]
  In representation learning on graph-structured data, many popular graph neural networks (GNNs) fail to capture long-range dependencies. This paper proposes a novel graph learning framework called the graph convolutional network with self-attention (GCN-SA) The proposed scheme exhibits an exceptional generalization capability in node-level representation learning.
  arXiv  Detail & Related papers  (2024-03-06T05:00:31Z)
• Distributional Signals for Node Classification in Graph Neural Networks [36.30743671968087]
  In graph neural networks (GNNs) both node features and labels are examples of graph signals, a key notion in graph signal processing (GSP) In our framework, we work with the distributions of node labels instead of their values and propose notions of smoothness and non-uniformity of such distributional graph signals. We then propose a general regularization method for GNNs that allows us to encode distributional smoothness and non-uniformity of the model output in semi-supervised node classification tasks.
  arXiv  Detail & Related papers  (2023-04-07T06:54:42Z)
• Graph Neural Networks with Feature and Structure Aware Random Walk [7.143879014059894]
  We show that in typical heterphilous graphs, the edges may be directed, and whether to treat the edges as is or simply make them undirected greatly affects the performance of the GNN models. We develop a model that adaptively learns the directionality of the graph, and exploits the underlying long-distance correlations between nodes.
  arXiv  Detail & Related papers  (2021-11-19T08:54:21Z)
• Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classification [59.06717774425588]
  We propose the Explicit Pairwise Factorized Graph Neural Network (EPFGNN), which models the whole graph as a partially observed Markov Random Field. It contains explicit pairwise factors to model output-output relations and uses a GNN backbone to model input-output relations. We conduct experiments on various datasets, which shows that our model can effectively improve the performance for semi-supervised node classification on graphs.
  arXiv  Detail & Related papers  (2021-07-27T19:47:53Z)
• On the Equivalence of Decoupled Graph Convolution Network and Label Propagation [60.34028546202372]
  Some work shows that coupling is inferior to decoupling, which supports deep graph propagation better. Despite effectiveness, the working mechanisms of the decoupled GCN are not well understood. We propose a new label propagation method named propagation then training Adaptively (PTA), which overcomes the flaws of the decoupled GCN.
  arXiv  Detail & Related papers  (2020-10-23T13:57:39Z)
• CatGCN: Graph Convolutional Networks with Categorical Node Features [99.555850712725]
  CatGCN is tailored for graph learning when the node features are categorical. We train CatGCN in an end-to-end fashion and demonstrate it on semi-supervised node classification.
  arXiv  Detail & Related papers  (2020-09-11T09:25:17Z)
• Bilinear Graph Neural Network with Neighbor Interactions [106.80781016591577]
  Graph Neural Network (GNN) is a powerful model to learn representations and make predictions on graph data. We propose a new graph convolution operator, which augments the weighted sum with pairwise interactions of the representations of neighbor nodes. We term this framework as Bilinear Graph Neural Network (BGNN), which improves GNN representation ability with bilinear interactions between neighbor nodes.
  arXiv  Detail & Related papers  (2020-02-10T06:43:38Z)
• Graph Inference Learning for Semi-supervised Classification [50.55765399527556]
  We propose a Graph Inference Learning framework to boost the performance of semi-supervised node classification. For learning the inference process, we introduce meta-optimization on structure relations from training nodes to validation nodes. Comprehensive evaluations on four benchmark datasets demonstrate the superiority of our proposed GIL when compared against state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-01-17T02:52:30Z)

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.