scholarly journals Semi-supervised Graph Embedding for Multi-label Graph Node Classification

2019 ◽  
pp. 555-567 ◽  
Author(s):  
Kaisheng Gao ◽  
Jing Zhang ◽  
Cangqi Zhou
Keyword(s):  
Graph Embedding ◽  
Label Graph ◽  
Graph Node ◽  
Node Classification

Multi-label Graph Node Classification with Label Attentive Neighborhood Convolution

2021 ◽  
pp. 115063
Author(s):  
Cangqi Zhou ◽  
Hui Chen ◽  
Jing Zhang ◽  
Qianmu Li ◽  
Dianming Hu ◽  
...  
Keyword(s):  
Label Graph ◽  
Graph Node ◽  
Node Classification

scholarly journals MeSHHeading2vec: A new method for representing MeSH headings as feature vectors based on graph embedding algorithm

2019 ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
Hai-Cheng Yi ◽  
Kai Zheng ◽  
Yan-Bin Wang
Keyword(s):  
Computational Models ◽  
Prediction Models ◽  
Graph Embedding ◽  
Computational Prediction ◽  
Additional Information ◽  
Great Hope ◽  
Node Classification ◽  
Relationship Network ◽  
The Relationship ◽  
Mesh Tree

AbstractMotivationEffectively representing the MeSH headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify.ResultsIn this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships) which can be constructed by the rule of tree num. Then, five graph embedding algorithms including DeepWalk (DW), LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed method, we carried out the node classification and relationship prediction tasks. The experimental results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the distinguishable ability of vectors. Thus, it can act as input and continue to play a significant role in any disease-, drug-, microbe- and etc.-related computational models. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network [email protected]

scholarly journals Cross-Modality Attention with Semantic Graph Embedding for Multi-Label Classification

2020 ◽  
Vol 34 (07) ◽  
pp. 12709-12716
Author(s):  
Renchun You ◽  
Zhiyao Guo ◽  
Lei Cui ◽  
Xiang Long ◽  
Yingze Bao ◽  
...  
Keyword(s):  
Computer Vision ◽  
Graph Embedding ◽  
Video Classification ◽  
Label Graph ◽  
Generalization Capability ◽  
Semantic Graph ◽  
The Arts ◽  
Challenging Tasks ◽  
Semantic Label ◽  
Similarity Graph

Multi-label image and video classification are fundamental yet challenging tasks in computer vision. The main challenges lie in capturing spatial or temporal dependencies between labels and discovering the locations of discriminative features for each class. In order to overcome these challenges, we propose to use cross-modality attention with semantic graph embedding for multi-label classification. Based on the constructed label graph, we propose an adjacency-based similarity graph embedding method to learn semantic label embeddings, which explicitly exploit label relationships. Then our novel cross-modality attention maps are generated with the guidance of learned label embeddings. Experiments on two multi-label image classification datasets (MS-COCO and NUS-WIDE) show our method outperforms other existing state-of-the-arts. In addition, we validate our method on a large multi-label video classification dataset (YouTube-8M Segments) and the evaluation results demonstrate the generalization capability of our method.

scholarly journals Joint Link Prediction and Network Alignment via Cross-graph Embedding

2019 ◽  
Author(s):  
Xingbo Du ◽  
Junchi Yan ◽  
Hongyuan Zha
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Link Prediction ◽  
Graph Embedding ◽  
Network Alignment ◽  
Initial Vertex ◽  
Information Propagation ◽  
Graph Node ◽  
Embedding Technique

Link prediction and network alignment are two important problems in social network analysis and other network related applications. Considerable efforts have been devoted to these two problems while often in an independent way to each other. In this paper we argue that these two tasks are relevant and present a joint link prediction and network alignment framework, whereby a novel cross-graph node embedding technique is devised to allow for information propagation. Our approach can either work with a few initial vertex correspondence as seeds, or from scratch. By extensive experiments on public benchmark, we show that link prediction and network alignment can benefit to each other especially for improving the recall for both tasks.

scholarly journals Understanding Negative Sampling in Knowledge Graph Embedding

2021 ◽  
Vol 12 (1) ◽  
pp. 71-81
Author(s):  
Jing Qian ◽  
Gangmin Li ◽  
Katie Atkinson ◽  
Yong Yue
Keyword(s):  
Link Prediction ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
Direct Impact ◽  
Dimensional Vector Space ◽  
Dynamic Distribution ◽  
Space Efficiency ◽  
Node Classification ◽  
Low Dimensional

Knowledge graph embedding (KGE) is to project entities and relations of a knowledge graph (KG) into a low-dimensional vector space, which has made steady progress in recent years. Conventional KGE methods, especially translational distance-based models, are trained through discriminating positive samples from negative ones. Most KGs store only positive samples for space efficiency. Negative sampling thus plays a crucial role in encoding triples of a KG. The quality of generated negative samples has a direct impact on the performance of learnt knowledge representation in a myriad of downstream tasks, such as recommendation, link prediction and node classification. We summarize current negative sampling approaches in KGE into three categories, static distribution-based, dynamic distribution-based and custom cluster-based respectively. Based on this categorization we discuss the most prevalent existing approaches and their characteristics. It is a hope that this review can provide some guidelines for new thoughts about negative sampling in KGE.

scholarly journals MeSHHeading2vec: a new method for representing MeSH headings as vectors based on graph embedding algorithm

2020 ◽  
Author(s):  
Zhen-Hao Guo ◽  
Zhu-Hong You ◽  
De-Shuang Huang ◽  
Hai-Cheng Yi ◽  
Kai Zheng ◽  
...  
Keyword(s):  
Computational Models ◽  
Prediction Models ◽  
Graph Embedding ◽  
Computational Prediction ◽  
Medical Subject Headings ◽  
Additional Information ◽  
Great Hope ◽  
Node Classification ◽  
Relationship Network ◽  
The Relationship

Abstract Effectively representing Medical Subject Headings (MeSH) headings (terms) such as disease and drug as discriminative vectors could greatly improve the performance of downstream computational prediction models. However, these terms are often abstract and difficult to quantify. In this paper, we converted the MeSH tree structure into a relationship network and applied several graph embedding algorithms on it to represent these terms. Specifically, the relationship network consisting of nodes (MeSH headings) and edges (relationships), which can be constructed by the tree num. Then, five graph embedding algorithms including DeepWalk, LINE, SDNE, LAP and HOPE were implemented on the relationship network to represent MeSH headings as vectors. In order to evaluate the performance of the proposed methods, we carried out the node classification and relationship prediction tasks. The results show that the MeSH headings characterized by graph embedding algorithms can not only be treated as an independent carrier for representation, but also can be utilized as additional information to enhance the representation ability of vectors. Thus, it can serve as an input and continue to play a significant role in any computational models related to disease, drug, microbe, etc. Besides, our method holds great hope to inspire relevant researchers to study the representation of terms in this network perspective.

scholarly journals Learning Triple Embeddings from Knowledge Graphs

2020 ◽  
Vol 34 (04) ◽  
pp. 3874-3881 ◽  
Author(s):  
Valeria Fionda ◽  
Giuseppe Pirrò
Keyword(s):  
Line Graph ◽  
Graph Embedding ◽  
Knowledge Graph ◽  
World Knowledge ◽  
Feature Vectors ◽  
Edge Weighting ◽  
Knowledge Graphs ◽  
Node Classification ◽  
A New Technique ◽  
Semantic Proximity

Graph embedding techniques allow to learn high-quality feature vectors from graph structures and are useful in a variety of tasks, from node classification to clustering. Existing approaches have only focused on learning feature vectors for the nodes and predicates in a knowledge graph. To the best of our knowledge, none of them has tackled the problem of directly learning triple embeddings. The approaches that are closer to this task have focused on homogeneous graphs involving only one type of edge and obtain edge embeddings by applying some operation (e.g., average) on the embeddings of the endpoint nodes. The goal of this paper is to introduce Triple2Vec, a new technique to directly embed knowledge graph triples. We leverage the idea of line graph of a graph and extend it to the context of knowledge graphs. We introduce an edge weighting mechanism for the line graph based on semantic proximity. Embeddings are finally generated by adopting the SkipGram model, where sentences are replaced with graph walks. We evaluate our approach on different real-world knowledge graphs and compared it with related work. We also show an application of triple embeddings in the context of user-item recommendations.

scholarly journals Survey on graph embeddings and their applications to machine learning problems on graphs

2021 ◽  
Vol 7 ◽  
pp. e357
Author(s):  
Ilya Makarov ◽  
Dmitrii Kiselev ◽  
Nikita Nikitinsky ◽  
Lovro Subelj
Keyword(s):  
Machine Learning ◽  
Link Prediction ◽  
Graph Embedding ◽  
Learning Problems ◽  
Feature Engineering ◽  
Graph Embeddings ◽  
Feature Spaces ◽  
Wide Range ◽  
Depth Analysis ◽  
Node Classification

Dealing with relational data always required significant computational resources, domain expertise and task-dependent feature engineering to incorporate structural information into a predictive model. Nowadays, a family of automated graph feature engineering techniques has been proposed in different streams of literature. So-called graph embeddings provide a powerful tool to construct vectorized feature spaces for graphs and their components, such as nodes, edges and subgraphs under preserving inner graph properties. Using the constructed feature spaces, many machine learning problems on graphs can be solved via standard frameworks suitable for vectorized feature representation. Our survey aims to describe the core concepts of graph embeddings and provide several taxonomies for their description. First, we start with the methodological approach and extract three types of graph embedding models based on matrix factorization, random-walks and deep learning approaches. Next, we describe how different types of networks impact the ability of models to incorporate structural and attributed data into a unified embedding. Going further, we perform a thorough evaluation of graph embedding applications to machine learning problems on graphs, among which are node classification, link prediction, clustering, visualization, compression, and a family of the whole graph embedding algorithms suitable for graph classification, similarity and alignment problems. Finally, we overview the existing applications of graph embeddings to computer science domains, formulate open problems and provide experiment results, explaining how different networks properties result in graph embeddings quality in the four classic machine learning problems on graphs, such as node classification, link prediction, clustering and graph visualization. As a result, our survey covers a new rapidly growing field of network feature engineering, presents an in-depth analysis of models based on network types, and overviews a wide range of applications to machine learning problems on graphs.

scholarly journals Enhanced Unsupervised Graph Embedding via Hierarchical Graph Convolution Network

2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
H. Zhang ◽  
J. J. Zhou ◽  
R. Li
Keyword(s):  
Graph Embedding ◽  
Network Node ◽  
Dimensional Representation ◽  
Undirected Graphs ◽  
Node Label ◽  
Structure Information ◽  
Initial Node ◽  
Hierarchical Graph ◽  
Node Classification ◽  
Low Dimensional

Graph embedding aims to learn the low-dimensional representation of nodes in the network, which has been paid more and more attention in many graph-based tasks recently. Graph Convolution Network (GCN) is a typical deep semisupervised graph embedding model, which can acquire node representation from the complex network. However, GCN usually needs to use a lot of labeled data and additional expressive features in the graph embedding learning process, so the model cannot be effectively applied to undirected graphs with only network structure information. In this paper, we propose a novel unsupervised graph embedding method via hierarchical graph convolution network (HGCN). Firstly, HGCN builds the initial node embedding and pseudo-labels for the undirected graphs, and then further uses GCNs to learn the node embedding and update labels, finally combines HGCN output representation with the initial embedding to get the graph embedding. Furthermore, we improve the model to match the different undirected networks according to the number of network node label types. Comprehensive experiments demonstrate that our proposed HGCN and HGCN∗ can significantly enhance the performance of the node classification task.

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