A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs

• URL: http://arxiv.org/abs/2211.16689v1
• Date: Wed, 30 Nov 2022 02:20:19 GMT
• Title: A Node-collaboration-informed Graph Convolutional Network for Precise Representation to Undirected Weighted Graphs
• Authors: Ying Wang, Ye Yuan, Xin Luo
• Abstract summary: A graph convolutional network (GCN) is widely adopted to perform representation learning to a weighted graph (UWG) This study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas.
• Score: 10.867583522217473
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: An undirected weighted graph (UWG) is frequently adopted to describe the interactions among a solo set of nodes from real applications, such as the user contact frequency from a social network services system. A graph convolutional network (GCN) is widely adopted to perform representation learning to a UWG for subsequent pattern analysis tasks such as clustering or missing data estimation. However, existing GCNs mostly neglects the latent collaborative information hidden in its connected node pairs. To address this issue, this study proposes to model the node collaborations via a symmetric latent factor analysis model, and then regards it as a node-collaboration module for supplementing the collaboration loss in a GCN. Based on this idea, a Node-collaboration-informed Graph Convolutional Network (NGCN) is proposed with three-fold ideas: a) Learning latent collaborative information from the interaction of node pairs via a node-collaboration module; b) Building the residual connection and weighted representation propagation to obtain high representation capacity; and c) Implementing the model optimization in an end-to-end fashion to achieve precise representation to the target UWG. Empirical studies on UWGs emerging from real applications demonstrate that owing to its efficient incorporation of node-collaborations, the proposed NGCN significantly outperforms state-of-the-art GCNs in addressing the task of missing weight estimation. Meanwhile, its good scalability ensures its compatibility with more advanced GCN extensions, which will be further investigated in our future studies.

Related papers

• Global Context Enhanced Anomaly Detection of Cyber Attacks via Decoupled Graph Neural Networks [0.0]
  We deploy decoupled GNNs to overcome the issue of capturing nonlinear network information. For node representation learning, we develop a GNN architecture with two modules for aggregating node feature information. The findings demonstrate that decoupled training along with the global context enhanced representation of the nodes is superior to the state-of-the-art models in terms of AUC.
  arXiv  Detail & Related papers  (2024-09-04T21:54:07Z)
• DGNN: Decoupled Graph Neural Networks with Structural Consistency between Attribute and Graph Embedding Representations [62.04558318166396]
  Graph neural networks (GNNs) demonstrate a robust capability for representation learning on graphs with complex structures. A novel GNNs framework, dubbed Decoupled Graph Neural Networks (DGNN), is introduced to obtain a more comprehensive embedding representation of nodes. Experimental results conducted on several graph benchmark datasets verify DGNN's superiority in node classification task.
  arXiv  Detail & Related papers  (2024-01-28T06:43:13Z)
• Network Alignment with Transferable Graph Autoencoders [79.89704126746204]
  We propose a novel graph autoencoder architecture designed to extract powerful and robust node embeddings. We prove that the generated embeddings are associated with the eigenvalues and eigenvectors of the graphs. Our proposed framework also leverages transfer learning and data augmentation to achieve efficient network alignment at a very large scale without retraining.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• 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)
• Neighborhood Convolutional Network: A New Paradigm of Graph Neural Networks for Node Classification [12.062421384484812]
  Graph Convolutional Network (GCN) decouples neighborhood aggregation and feature transformation in each convolutional layer. In this paper, we propose a new paradigm of GCN, termed Neighborhood Convolutional Network (NCN) In this way, the model could inherit the merit of decoupled GCN for aggregating neighborhood information, at the same time, develop much more powerful feature learning modules.
  arXiv  Detail & Related papers  (2022-11-15T02:02:51Z)
• Flattened Graph Convolutional Networks For Recommendation [18.198536511983452]
  This paper proposes the flattened GCN(FlatGCN) model, which is able to achieve superior performance with remarkably less complexity compared with existing models. First, we propose a simplified but powerful GCN architecture which aggregates the neighborhood information using one flattened GCN layer. Second, we propose an informative neighbor-infomax sampling method to select the most valuable neighbors by measuring the correlation among neighboring nodes. Third, we propose a layer ensemble technique which improves the expressiveness of the learned representations by assembling the layer-wise neighborhood representations at the final layer.
  arXiv  Detail & Related papers  (2022-09-25T12:53:50Z)
• SStaGCN: Simplified stacking based graph convolutional networks [2.556756699768804]
  Graph convolutional network (GCN) is a powerful model studied broadly in various graph structural data learning tasks. We propose a novel GCN called SStaGCN (Simplified stacking based GCN) by utilizing the ideas of stacking and aggregation. We show that SStaGCN can efficiently mitigate the over-smoothing problem of GCN.
  arXiv  Detail & Related papers  (2021-11-16T05:00:08Z)
• Node Similarity Preserving Graph Convolutional Networks [51.520749924844054]
  Graph Neural Networks (GNNs) explore the graph structure and node features by aggregating and transforming information within node neighborhoods. We propose SimP-GCN that can effectively and efficiently preserve node similarity while exploiting graph structure. We validate the effectiveness of SimP-GCN on seven benchmark datasets including three assortative and four disassorative graphs.
  arXiv  Detail & Related papers  (2020-11-19T04:18:01Z)
• Self-supervised Graph Learning for Recommendation [69.98671289138694]
  We explore self-supervised learning on user-item graph for recommendation. An auxiliary self-supervised task reinforces node representation learning via self-discrimination. Empirical studies on three benchmark datasets demonstrate the effectiveness of SGL.
  arXiv  Detail & Related papers  (2020-10-21T06:35:26Z)
• RGCF: Refined Graph Convolution Collaborative Filtering with concise and expressive embedding [42.46797662323393]
  We develop a new GCN-based Collaborative Filtering model, named Refined Graph convolution Collaborative Filtering(RGCF) RGCF is more capable for capturing the implicit high-order connectivities inside the graph and the resultant vector representations are more expressive. We conduct extensive experiments on three public million-size datasets, demonstrating that our RGCF significantly outperforms state-of-the-art models.
  arXiv  Detail & Related papers  (2020-07-07T12:26:10Z)
• Cross-GCN: Enhancing Graph Convolutional Network with $k$-Order Feature Interactions [153.6357310444093]
  Graph Convolutional Network (GCN) is an emerging technique that performs learning and reasoning on graph data. We argue that existing designs of GCN forgo modeling cross features, making GCN less effective for tasks or data where cross features are important. We design a new operator named Cross-feature Graph Convolution, which explicitly models the arbitrary-order cross features with complexity linear to feature dimension and order size.
  arXiv  Detail & Related papers  (2020-03-05T13:05:27Z)

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.