Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information

• URL: http://arxiv.org/abs/2009.13734v2
• Date: Fri, 13 Nov 2020 19:18:33 GMT
• Title: Semi-Supervised Node Classification by Graph Convolutional Networks and Extracted Side Information
• Authors: Mohammad Esmaeili, and Aria Nosratinia
• Abstract summary: This paper revisits the node classification task in a semi-supervised scenario by graph convolutional networks (GCNs) The goal is to benefit from the flow of information that circulates around the revealed node labels.
• Score: 18.07347677181108
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The nodes of a graph existing in a cluster are more likely to connect to each other than with other nodes in the graph. Then revealing some information about some nodes, the structure of the graph (graph edges) provides this opportunity to know more information about other nodes. From this perspective, this paper revisits the node classification task in a semi-supervised scenario by graph convolutional networks (GCNs). The goal is to benefit from the flow of information that circulates around the revealed node labels. The contribution of this paper is twofold. First, this paper provides a method for extracting side information from a graph realization. Then a new GCN architecture is presented that combines the output of traditional GCN and the extracted side information. Another contribution of this paper is relevant to non-graph observations (independent side information) that exists beside a graph realization in many applications. Indeed, the extracted side information can be replaced by a sequence of side information that is independent of the graph structure. For both cases, the experiments on synthetic and real-world datasets demonstrate that the proposed model achieves a higher prediction accuracy in comparison to the existing state-of-the-art methods for the node classification task.

Related papers

• 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)
• 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)
• 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)
• Semi-Supervised Hierarchical Graph Classification [54.25165160435073]
  We study the node classification problem in the hierarchical graph where a 'node' is a graph instance. We propose the Hierarchical Graph Mutual Information (HGMI) and present a way to compute HGMI with theoretical guarantee. We demonstrate the effectiveness of this hierarchical graph modeling and the proposed SEAL-CI method on text and social network data.
  arXiv  Detail & Related papers  (2022-06-11T04:05:29Z)
• SHGNN: Structure-Aware Heterogeneous Graph Neural Network [77.78459918119536]
  This paper proposes a novel Structure-Aware Heterogeneous Graph Neural Network (SHGNN) to address the above limitations. We first utilize a feature propagation module to capture the local structure information of intermediate nodes in the meta-path. Next, we use a tree-attention aggregator to incorporate the graph structure information into the aggregation module on the meta-path. Finally, we leverage a meta-path aggregator to fuse the information aggregated from different meta-paths.
  arXiv  Detail & Related papers  (2021-12-12T14:18:18Z)
• Asymmetric Graph Representation Learning [13.195785825237621]
  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.
  arXiv  Detail & Related papers  (2021-10-14T15:03:18Z)
• 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)
• Schema-Aware Deep Graph Convolutional Networks for Heterogeneous Graphs [10.526065883783899]
  Graph convolutional network (GCN) based approaches have achieved significant progress for solving complex, graph-structured problems. We propose our GCN framework 'Deep Heterogeneous Graph Convolutional Network (DHGCN)' It takes advantage of the schema of a heterogeneous graph and uses a hierarchical approach to effectively utilize information many hops away.
  arXiv  Detail & Related papers  (2021-05-03T06:24:27Z)
• SPAGAN: Shortest Path Graph Attention Network [187.75441278910708]
  Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs. We propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN)
  arXiv  Detail & Related papers  (2021-01-10T03:18:34Z)
• Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning [12.592903558338444]
  We propose a graph representation learning method called Graph InfoClust. It seeks to additionally capture cluster-level information content. This optimization leads the node representations to capture richer information and nodal interactions, which improves their quality.
  arXiv  Detail & Related papers  (2020-09-15T09:33:20Z)
• 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)

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.