Dyn-GCN: Graph Embedding via Dynamic Evolution and Graph Convolutional Network for Personal Recommendation

2021 ◽  
Author(s):  
Zhihui Wang ◽  
Jianrui Chen ◽  
Peijie Wang ◽  
Tingting Zhu
Keyword(s):  
Graph Embedding ◽  
Dynamic Evolution ◽  
Convolutional Network ◽  
Personal Recommendation

scholarly journals Use of word and graph embedding to measure semantic relatedness between Unified Medical Language System concepts

2020 ◽  
Vol 27 (10) ◽  
pp. 1538-1546 ◽  
Author(s):  
Yuqing Mao ◽  
Kin Wah Fung
Keyword(s):  
Word Sense Disambiguation ◽  
Graph Embedding ◽  
Semantic Relatedness ◽  
Word Sense ◽  
Medical Subject Headings ◽  
Network Graph ◽  
Convolutional Network ◽  
Language System ◽  
Unified Medical Language System ◽  
Medical Language

Abstract Objective The study sought to explore the use of deep learning techniques to measure the semantic relatedness between Unified Medical Language System (UMLS) concepts. Materials and Methods Concept sentence embeddings were generated for UMLS concepts by applying the word embedding models BioWordVec and various flavors of BERT to concept sentences formed by concatenating UMLS terms. Graph embeddings were generated by the graph convolutional networks and 4 knowledge graph embedding models, using graphs built from UMLS hierarchical relations. Semantic relatedness was measured by the cosine between the concepts’ embedding vectors. Performance was compared with 2 traditional path-based (shortest path and Leacock-Chodorow) measurements and the publicly available concept embeddings, cui2vec, generated from large biomedical corpora. The concept sentence embeddings were also evaluated on a word sense disambiguation (WSD) task. Reference standards used included the semantic relatedness and semantic similarity datasets from the University of Minnesota, concept pairs generated from the Standardized MedDRA Queries and the MeSH (Medical Subject Headings) WSD corpus. Results Sentence embeddings generated by BioWordVec outperformed all other methods used individually in semantic relatedness measurements. Graph convolutional network graph embedding uniformly outperformed path-based measurements and was better than some word embeddings for the Standardized MedDRA Queries dataset. When used together, combined word and graph embedding achieved the best performance in all datasets. For WSD, the enhanced versions of BERT outperformed BioWordVec. Conclusions Word and graph embedding techniques can be used to harness terms and relations in the UMLS to measure semantic relatedness between concepts. Concept sentence embedding outperforms path-based measurements and cui2vec, and can be further enhanced by combining with graph embedding.

End-to-end Relation-Enhanced Learnable Graph Self-attention Network for Knowledge Graphs Embedding

2021 ◽  
Author(s):  
Shengchen Jiang ◽  
Hongbin Wang ◽  
Xiang Hou
Keyword(s):  
Large Scale ◽  
Structural Characteristics ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Data Sets ◽  
Relevance Ranking ◽  
Convolutional Network ◽  
Attention Network ◽  
Knowledge Graphs ◽  
End To End

Abstract The existing methods ignore the adverse effect of knowledge graph incompleteness on knowledge graph embedding. In addition, the complexity and large-scale of knowledge information hinder knowledge graph embedding performance of the classic graph convolutional network. In this paper, we analyzed the structural characteristics of knowledge graph and the imbalance of knowledge information. Complex knowledge information requires that the model should have better learnability, rather than linearly weighted qualitative constraints, so the method of end-to-end relation-enhanced learnable graph self-attention network for knowledge graphs embedding is proposed. Firstly, we construct the relation-enhanced adjacency matrix to consider the incompleteness of the knowledge graph. Secondly, the graph self-attention network is employed to obtain the global encoding and relevance ranking of entity node information. Thirdly, we propose the concept of convolutional knowledge subgraph, it is constructed according to the entity relevance ranking. Finally, we improve the training effect of the convKB model by changing the construction of negative samples to obtain a better reliability score in the decoder. The experimental results based on the data sets FB15k-237 and WN18RR show that the proposed method facilitates more comprehensive representation of knowledge information than the existing methods, in terms of Hits@10 and MRR.

scholarly journals Neural Graph Embedding for Neural Architecture Search

2020 ◽  
Vol 34 (04) ◽  
pp. 4707-4714 ◽  
Author(s):  
Wei Li ◽  
Shaogang Gong ◽  
Xiatian Zhu
Keyword(s):  
Neural Networks ◽  
Graph Embedding ◽  
Semantic Segmentation ◽  
Directed Acyclic Graphs ◽  
Network Architectures ◽  
Search Performance ◽  
Convolutional Network ◽  
Neural Architecture ◽  
Acyclic Graphs ◽  
Topology Information

Existing neural architecture search (NAS) methods often operate in discrete or continuous spaces directly, which ignores the graphical topology knowledge of neural networks. This leads to suboptimal search performance and efficiency, given the factor that neural networks are essentially directed acyclic graphs (DAG). In this work, we address this limitation by introducing a novel idea of neural graph embedding (NGE). Specifically, we represent the building block (i.e. the cell) of neural networks with a neural DAG, and learn it by leveraging a Graph Convolutional Network to propagate and model the intrinsic topology information of network architectures. This results in a generic neural network representation integrable with different existing NAS frameworks. Extensive experiments show the superiority of NGE over the state-of-the-art methods on image classification and semantic segmentation.

scholarly journals Adversarial Graph Embedding for Ensemble Clustering

2019 ◽  
Author(s):  
Zhiqiang Tao ◽  
Hongfu Liu ◽  
Jun Li ◽  
Zhaowen Wang ◽  
Yun Fu
Keyword(s):  
Latent Variables ◽  
Graph Embedding ◽  
Product Layer ◽  
Inner Product ◽  
Ensemble Clustering ◽  
Clustering Methods ◽  
Convolutional Network ◽  
Graph Representations ◽  
Deep Embedding ◽  
Real World Datasets

Ensemble clustering generally integrates basic partitions into a consensus one through a graph partitioning method, which, however, has two limitations: 1) it neglects to reuse original features; 2) obtaining consensus partition with learnable graph representations is still under-explored. In this paper, we propose a novel Adversarial Graph Auto-Encoders (AGAE) model to incorporate ensemble clustering into a deep graph embedding process. Specifically, graph convolutional network is adopted as probabilistic encoder to jointly integrate the information from feature content and consensus graph, and a simple inner product layer is used as decoder to reconstruct graph with the encoded latent variables (i.e., embedding representations). Moreover, we develop an adversarial regularizer to guide the network training with an adaptive partition-dependent prior. Experiments on eight real-world datasets are presented to show the effectiveness of AGAE over several state-of-the-art deep embedding and ensemble clustering methods.

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