Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks

• URL: http://arxiv.org/abs/2105.03178v2
• Date: Tue, 11 May 2021 12:29:48 GMT
• Title: Graph Entropy Guided Node Embedding Dimension Selection for Graph Neural Networks
• Authors: Gongxu Luo, Jianxin Li, Hao Peng, Carl Yang, Lichao Sun, Philip S. Yu, Lifang He
• Abstract summary: We propose a novel Minimum Graph Entropy (MinGE) algorithm for Node Embedding Dimension Selection (NEDS) MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. Experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE.
• Score: 74.26734952400925
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph representation learning has achieved great success in many areas, including e-commerce, chemistry, biology, etc. However, the fundamental problem of choosing the appropriate dimension of node embedding for a given graph still remains unsolved. The commonly used strategies for Node Embedding Dimension Selection (NEDS) based on grid search or empirical knowledge suffer from heavy computation and poor model performance. In this paper, we revisit NEDS from the perspective of minimum entropy principle. Subsequently, we propose a novel Minimum Graph Entropy (MinGE) algorithm for NEDS with graph data. To be specific, MinGE considers both feature entropy and structure entropy on graphs, which are carefully designed according to the characteristics of the rich information in them. The feature entropy, which assumes the embeddings of adjacent nodes to be more similar, connects node features and link topology on graphs. The structure entropy takes the normalized degree as basic unit to further measure the higher-order structure of graphs. Based on them, we design MinGE to directly calculate the ideal node embedding dimension for any graph. Finally, comprehensive experiments with popular Graph Neural Networks (GNNs) on benchmark datasets demonstrate the effectiveness and generalizability of our proposed MinGE.

Related papers

• Deep Manifold Graph Auto-Encoder for Attributed Graph Embedding [51.75091298017941]
  This paper proposes a novel Deep Manifold (Variational) Graph Auto-Encoder (DMVGAE/DMGAE) for attributed graph data. The proposed method surpasses state-of-the-art baseline algorithms by a significant margin on different downstream tasks across popular datasets.
  arXiv  Detail & Related papers  (2024-01-12T17:57:07Z)
• Saliency-Aware Regularized Graph Neural Network [39.82009838086267]
  We propose the Saliency-Aware Regularized Graph Neural Network (SAR-GNN) for graph classification. We first estimate the global node saliency by measuring the semantic similarity between the compact graph representation and node features. Then the learned saliency distribution is leveraged to regularize the neighborhood aggregation of the backbone.
  arXiv  Detail & Related papers  (2024-01-01T13:44:16Z)
• GraphRARE: Reinforcement Learning Enhanced Graph Neural Network with Relative Entropy [21.553180564868306]
  GraphRARE is a framework built upon node relative entropy and deep reinforcement learning. An innovative node relative entropy is used to measure mutual information between node pairs. A deep reinforcement learning-based algorithm is developed to optimize the graph topology.
  arXiv  Detail & Related papers  (2023-12-15T11:30:18Z)
• GRAN is superior to GraphRNN: node orderings, kernel- and graph embeddings-based metrics for graph generators [0.6816499294108261]
  We study kernel-based metrics on distributions of graph invariants and manifold-based and kernel-based metrics in graph embedding space. We compare GraphRNN and GRAN, two well-known generative models for graphs, and unveil the influence of node orderings.
  arXiv  Detail & Related papers  (2023-07-13T12:07:39Z)
• NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification [70.51126383984555]
  We introduce a novel all-pair message passing scheme for efficiently propagating node signals between arbitrary nodes. The efficient computation is enabled by a kernerlized Gumbel-Softmax operator. Experiments demonstrate the promising efficacy of the method in various tasks including node classification on graphs.
  arXiv  Detail & Related papers  (2023-06-14T09:21:15Z)
• Seq-HGNN: Learning Sequential Node Representation on Heterogeneous Graph [57.2953563124339]
  We propose a novel heterogeneous graph neural network with sequential node representation, namely Seq-HGNN. We conduct extensive experiments on four widely used datasets from Heterogeneous Graph Benchmark (HGB) and Open Graph Benchmark (OGB)
  arXiv  Detail & Related papers  (2023-05-18T07:27:18Z)
• GraphSVX: Shapley Value Explanations for Graph Neural Networks [81.83769974301995]
  Graph Neural Networks (GNNs) achieve significant performance for various learning tasks on geometric data. In this paper, we propose a unified framework satisfied by most existing GNN explainers. We introduce GraphSVX, a post hoc local model-agnostic explanation method specifically designed for GNNs.
  arXiv  Detail & Related papers  (2021-04-18T10:40:37Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)
• Optimal Transport Graph Neural Networks [31.191844909335963]
  Current graph neural network (GNN) architectures naively average or sum node embeddings into an aggregated graph representation. We introduce OT-GNN, a model that computes graph embeddings using parametric prototypes.
  arXiv  Detail & Related papers  (2020-06-08T14:57:39Z)
• Geom-GCN: Geometric Graph Convolutional Networks [15.783571061254847]
  We propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs.
  arXiv  Detail & Related papers  (2020-02-13T00:03:09Z)

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.