Asymmetric Graph Representation Learning

• URL: http://arxiv.org/abs/2110.07436v1
• Date: Thu, 14 Oct 2021 15:03:18 GMT
• Title: Asymmetric Graph Representation Learning
• Authors: Zhuo Tan, Bin Liu and Guosheng Yin
• Abstract summary: A vast amount of applications where the information flow is asymmetric, leading to directed graphs. A directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. We propose a simple yet remarkably effective framework for directed graph analysis to incorporate such one-way information passing.
• Score: 13.195785825237621
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Despite the enormous success of graph neural networks (GNNs), most existing GNNs can only be applicable to undirected graphs where relationships among connected nodes are two-way symmetric (i.e., information can be passed back and forth). However, there is a vast amount of applications where the information flow is asymmetric, leading to directed graphs where information can only be passed in one direction. For example, a directed edge indicates that the information can only be conveyed forwardly from the start node to the end node, but not backwardly. To accommodate such an asymmetric structure of directed graphs within the framework of GNNs, we propose a simple yet remarkably effective framework for directed graph analysis to incorporate such one-way information passing. We define an incoming embedding and an outgoing embedding for each node to model its sending and receiving features respectively. We further develop two steps in our directed GNN model with the first one to aggregate/update the incoming features of nodes and the second one to aggregate/update the outgoing features. By imposing the two roles for each node, the likelihood of a directed edge can be calculated based on the outgoing embedding of the start node and the incoming embedding of the end node. The log-likelihood of all edges plays a natural role of regularization for the proposed model, which can alleviate the over-smoothing problem of the deep GNNs. Extensive experiments on multiple real-world directed graphs demonstrate outstanding performances of the proposed model in both node-level and graph-level tasks.

Related papers

• Improving Graph Neural Networks by Learning Continuous Edge Directions [0.0]
  Graph Neural Networks (GNNs) traditionally employ a message-passing mechanism that resembles diffusion over undirected graphs. Our key insight is to assign fuzzy edge directions to the edges of a graph so that features can preferentially flow in one direction between nodes. We propose a general framework, called Continuous Edge Direction (CoED) GNN, for learning on graphs with fuzzy edges.
  arXiv  Detail & Related papers  (2024-10-18T01:34:35Z)
• SF-GNN: Self Filter for Message Lossless Propagation in Deep Graph Neural Network [38.669815079957566]
  Graph Neural Network (GNN) with the main idea of encoding graph structure information of graphs by propagation and aggregation has developed rapidly. It achieved excellent performance in representation learning of multiple types of graphs such as homogeneous graphs, heterogeneous graphs, and more complex graphs like knowledge graphs. For the phenomenon of performance degradation in deep GNNs, we propose a new perspective.
  arXiv  Detail & Related papers  (2024-07-03T02:40:39Z)
• 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)
• 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)
• 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)
• Feature Correlation Aggregation: on the Path to Better Graph Neural Networks [37.79964911718766]
  Prior to the introduction of Graph Neural Networks (GNNs), modeling and analyzing irregular data, particularly graphs, was thought to be the Achilles' heel of deep learning. This paper introduces a central node permutation variant function through a frustratingly simple and innocent-looking modification to the core operation of a GNN. A tangible boost in performance of the model is observed where the model surpasses previous state-of-the-art results by a significant margin while employing fewer parameters.
  arXiv  Detail & Related papers  (2021-09-20T05:04:26Z)
• 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)
• Multi-grained Semantics-aware Graph Neural Networks [13.720544777078642]
  Graph Neural Networks (GNNs) are powerful techniques in representation learning for graphs. This work proposes a unified model, AdamGNN, to interactively learn node and graph representations. Experiments on 14 real-world graph datasets show that AdamGNN can significantly outperform 17 competing models on both node- and graph-wise tasks.
  arXiv  Detail & Related papers  (2020-10-01T07:52:06Z)
• 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)
• Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning [63.97983530843762]
  Graph Neural Networks (GNNs) have achieved great success in graph representation learning. GNNs generate identical representations for graph substructures that may in fact be very different. More powerful GNNs, proposed recently by mimicking higher-order tests, are inefficient as they cannot sparsity of underlying graph structure. We propose Distance Depiction (DE) as a new class of graph representation learning.
  arXiv  Detail & Related papers  (2020-08-31T23:15:40Z)

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.