Graph Elimination Networks

• URL: http://arxiv.org/abs/2401.01233v1
• Date: Tue, 2 Jan 2024 14:58:59 GMT
• Title: Graph Elimination Networks
• Authors: Shuo Wang, Ge Cheng, Yun Zhang
• Abstract summary: Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers. We show that the root cause of GNNs' performance degradation in deep layers lies in ineffective neighborhood feature propagation. We introduce Graph Elimination Networks (GENs), which employ a specific algorithm to eliminate redundancies during neighborhood propagation.
• Score: 8.806990624643333
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) are widely applied across various domains, yet they perform poorly in deep layers. Existing research typically attributes this problem to node over-smoothing, where node representations become indistinguishable after multiple rounds of propagation. In this paper, we delve into the neighborhood propagation mechanism of GNNs and discover that the real root cause of GNNs' performance degradation in deep layers lies in ineffective neighborhood feature propagation. This propagation leads to an exponential growth of a node's current representation at every propagation step, making it extremely challenging to capture valuable dependencies between long-distance nodes. To address this issue, we introduce Graph Elimination Networks (GENs), which employ a specific algorithm to eliminate redundancies during neighborhood propagation. We demonstrate that GENs can enhance nodes' perception of distant neighborhoods and extend the depth of network propagation. Extensive experiments show that GENs outperform the state-of-the-art methods on various graph-level and node-level datasets.

Related papers

• Robust Node Representation Learning via Graph Variational Diffusion Networks [7.335425547621226]
  In recent years, compelling evidence has revealed that GNN-based node representation learning can be substantially deteriorated by perturbations in a graph structure. To learn robust node representation in the presence of perturbations, various works have been proposed to safeguard GNNs. We propose the Graph Variational Diffusion Network (GVDN), a new node encoder that effectively manipulates Gaussian noise to safeguard robustness on perturbed graphs.
  arXiv  Detail & Related papers  (2023-12-18T03:18:53Z)
• Degree-based stratification of nodes in Graph Neural Networks [66.17149106033126]
  We modify the Graph Neural Network (GNN) architecture so that the weight matrices are learned, separately, for the nodes in each group. This simple-to-implement modification seems to improve performance across datasets and GNN methods.
  arXiv  Detail & Related papers  (2023-12-16T14:09:23Z)
• Over-Squashing in Riemannian Graph Neural Networks [1.6317061277457001]
  Most graph neural networks (GNNs) are prone to the phenomenon of over-squashing. Recent works have shown that the topology of the graph has the greatest impact on over-squashing. We explore whether over-squashing can be mitigated through the embedding space of the GNN.
  arXiv  Detail & Related papers  (2023-11-27T15:51:07Z)
• Collaborative Graph Neural Networks for Attributed Network Embedding [63.39495932900291]
  Graph neural networks (GNNs) have shown prominent performance on attributed network embedding. We propose COllaborative graph Neural Networks--CONN, a tailored GNN architecture for network embedding.
  arXiv  Detail & Related papers  (2023-07-22T04:52:27Z)
• 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)
• DEGREE: Decomposition Based Explanation For Graph Neural Networks [55.38873296761104]
  We propose DEGREE to provide a faithful explanation for GNN predictions. By decomposing the information generation and aggregation mechanism of GNNs, DEGREE allows tracking the contributions of specific components of the input graph to the final prediction. We also design a subgraph level interpretation algorithm to reveal complex interactions between graph nodes that are overlooked by previous methods.
  arXiv  Detail & Related papers  (2023-05-22T10:29:52Z)
• 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)
• Tree Decomposed Graph Neural Network [11.524511007436791]
  We propose a tree decomposition method to disentangle neighborhoods in different layers to alleviate feature smoothing. We also characterize the multi-hop dependency via graph diffusion within our tree decomposition formulation to construct Tree Decomposed Graph Neural Network (TDGNN) Comprehensive experiments demonstrate the superior performance of TDGNN on both homophily and heterophily networks.
  arXiv  Detail & Related papers  (2021-08-25T02:47:16Z)
• Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations [7.3136594018091134]
  Graph neural networks are emerging as continuation of deep learning success w.r.t. graph data. We propose to enhance information propagation among GNN layers by combining heterogeneous aggregations. We empirically validate the effectiveness of HAG-Net on a number of graph classification benchmarks.
  arXiv  Detail & Related papers  (2021-02-08T08:57:56Z)
• Graph Highway Networks [77.38665506495553]
  Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency. They suffer from the notorious over-smoothing problem, in which the learned representations converge to alike vectors when many layers are stacked. We propose Graph Highway Networks (GHNet) which utilize gating units to balance the trade-off between homogeneity and heterogeneity in the GCN learning process.
  arXiv  Detail & Related papers  (2020-04-09T16:26:43Z)
• EdgeNets:Edge Varying Graph Neural Networks [179.99395949679547]
  This paper puts forth a general framework that unifies state-of-the-art graph neural networks (GNNs) through the concept of EdgeNet. An EdgeNet is a GNN architecture that allows different nodes to use different parameters to weigh the information of different neighbors. This is a general linear and local operation that a node can perform and encompasses under one formulation all existing graph convolutional neural networks (GCNNs) as well as graph attention networks (GATs)
  arXiv  Detail & Related papers  (2020-01-21T15:51:17Z)

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.