Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

• URL: http://arxiv.org/abs/2011.05301v1
• Date: Tue, 10 Nov 2020 18:36:33 GMT
• Title: Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation
• Authors: Eda Bayram and Alberto Garcia-Duran and Robert West
• Abstract summary: Our approach, denoted as MrAP, imputes the values of missing attributes by propagating information across a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes.
• Score: 14.58440933068
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MrAP, imputes the values of missing attributes by propagating information across the multi-relational structure of a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes. The propagation mechanism operates iteratively in a message passing scheme that collects the predictions at every iteration and updates the value of the node attributes. Experiments over two benchmark datasets show the effectiveness of our approach.

Related papers

• AGHINT: Attribute-Guided Representation Learning on Heterogeneous Information Networks with Transformer [4.01252998015631]
  We investigate the impact of inter-node attribute disparities on HGNNs performance within a benchmark task. We propose a novel Attribute-Guided heterogeneous Information Networks representation learning model with Transformer (AGHINT) AGHINT transcends the constraints of the original graph structure by directly integrating higher-order similar neighbor features into the learning process.
  arXiv  Detail & Related papers  (2024-04-16T10:30:48Z)
• 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)
• Structure and inference in hypergraphs with node attributes [1.024113475677323]
  We show how node attributes can be used to improve our understanding of the structure resulting from higher-order interactions. We develop a principled model that combines higher-order interactions and node attributes to better represent the observed interactions.
  arXiv  Detail & Related papers  (2023-11-07T10:16:20Z)
• Recovering Missing Node Features with Local Structure-based Embeddings [34.79801041888119]
  We present a framework to recover completely missing node features for a set of graphs. Our approach incorporates prior information from both graph topology and existing nodal values.
  arXiv  Detail & Related papers  (2023-09-16T18:23:14Z)
• Revisiting Initializing Then Refining: An Incomplete and Missing Graph Imputation Network [42.68291773745948]
  We develop a novel network termed Revisiting Initializing Then refining (RITR) We complete both attribute-incomplete and attribute-missing samples under the guidance of a novel initializing-then-refining imputation criterion. To the best of our knowledge, this newly designed method is the first unsupervised framework dedicated to handling hybrid-absent graphs.
  arXiv  Detail & Related papers  (2023-02-15T08:38:06Z)
• GrannGAN: Graph annotation generative adversarial networks [72.66289932625742]
  We consider the problem of modelling high-dimensional distributions and generating new examples of data with complex relational feature structure coherent with a graph skeleton. The model we propose tackles the problem of generating the data features constrained by the specific graph structure of each data point by splitting the task into two phases. In the first it models the distribution of features associated with the nodes of the given graph, in the second it complements the edge features conditionally on the node features.
  arXiv  Detail & Related papers  (2022-12-01T11:49:07Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Learning on Attribute-Missing Graphs [66.76561524848304]
  There is a graph where attributes of only partial nodes could be available and those of the others might be entirely missing. Existing graph learning methods including the popular GNN cannot provide satisfied learning performance. We develop a novel distribution matching based GNN called structure-attribute transformer (SAT) for attribute-missing graphs.
  arXiv  Detail & Related papers  (2020-11-03T11:09:52Z)
• 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)
• Joint Item Recommendation and Attribute Inference: An Adaptive Graph Convolutional Network Approach [61.2786065744784]
  In recommender systems, users and items are associated with attributes, and users show preferences to items. As annotating user (item) attributes is a labor intensive task, the attribute values are often incomplete with many missing attribute values. We propose an Adaptive Graph Convolutional Network (AGCN) approach for joint item recommendation and attribute inference.
  arXiv  Detail & Related papers  (2020-05-25T10:50:01Z)
• Predicting Attributes of Nodes Using Network Structure [0.34998703934432673]
  We propose an approach to represent a node by a feature map with respect to an attribute $a_i$ using all attributes of neighbors to predict attributes values for $a_i$. We perform extensive experimentation on ten real-world datasets and show that the proposed feature map significantly improves the prediction accuracy as compared to baseline approaches on these datasets.
  arXiv  Detail & Related papers  (2019-12-27T17:59:33Z)

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.