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Temporal network embedding using graph attention network
2021
Complex & Intelligent Systems
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AbstractGraph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. ...
In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes ...
Various approaches [59] [60] [61] were proposed to perform link prediction on dynamic networks. ...
doi:10.1007/s40747-021-00332-x
fatcat:jy6q2meccnbqvjhhxkdlmmhnmm
WMGCN: Weighted meta-graph Based Graph Convolutional Networks for Representation Learning in Heterogeneous Networks
2020
IEEE Access
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Network embedding has been an effective tool to analyze heterogeneous networks (HNs) by representing nodes in a low-dimensional space. ...
In addition, we improve the current convolution design by adding node self-significance. ...
The main goal of this paper is to improve feature learning from heterogenous networks, using combined techniques of metagraph and graph convolution, so that the learned features can in turn improve tasks ...
doi:10.1109/access.2020.2977332
fatcat:oinp6jfg6ffzbjbaaiai5okose
An Overview on the Application of Graph Neural Networks in Wireless Networks
[article]
2021
arXiv
pre-print
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2021 pre-print arXiv:2107.03029v2 fatcat:wzzeiicojrfqvinlcs2dsczapiOther Versions
To effectively exploit the information of graph-structured data as well as contextual information, graph neural networks (GNNs) have been introduced to address a series of optimization problems of wireless ...
In recent years, with the rapid enhancement of computing power, deep learning methods have been widely applied in wireless networks and achieved impressive performance. ...
Therefore, how to design an effective graph convolution method with more heterogeneous network topology information is very important. ...
arXiv:2107.03029v3
fatcat:2hf2gelxrjbwpiwzyjw5cat5wu
Graph Highway Networks
[article]
2020
arXiv
pre-print
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Graph Convolution Networks (GCN) are widely used in learning graph representations due to their effectiveness and efficiency. ...
To address this problem, we propose Graph Highway Networks(GHNet) which utilize gating units to automatically balance the trade-off between homogeneity and heterogeneity in the GCN learning process. ...
To improve the representation quality, recent efforts have been focused on adapting well-established deep learning archi-This work is done when the author was taking an internship in Telefonica Research ...
arXiv:2004.04635v1
fatcat:sjr4zqdikjaercrm3manlh2djm
Dynamic Origin-Destination Matrix Prediction with Line Graph Neural Networks and Kalman Filter
[article]
2020
arXiv
pre-print
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In this paper we propose a novel O-D prediction framework combining heterogeneous prediction in graph neural networks and Kalman filter to recognize spatial and temporal patterns simultaneously. ...
The underlying road network topology is converted into a corresponding line graph in the newly designed Fusion Line Graph Convolutional Networks (FL-GCNs), which provide a general framework of predicting ...
In our approach, we relax some connections in LGNN and only consider the link graph convolution and node graph convolution (Figure 2b ). ...
arXiv:1905.00406v3
fatcat:tm52mceyqzdizdoe24txfrbhcy
Graph Transformer Networks
[article]
2020
arXiv
pre-print
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Graph neural networks (GNNs) have been widely used in representation learning on graphs and achieved state-of-the-art performance in tasks such as node classification and link prediction. ...
, while learning effective node representation on the new graphs in an end-to-end fashion. ...
Introduction In recent years, Graph Neural Networks (GNNs) have been widely adopted in various tasks over graphs, such as graph classification [11, 21, 40] , link prediction [18, 30, 42] and node classification ...
arXiv:1911.06455v2
fatcat:r7eaw4s4m5acffzase6w3isjli
Heterogeneous Hyperedge Convolutional Network
2020
Computers Materials & Continua
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In this paper, we present a Heterogeneous Hyperedge Convolutional Network (HHCN), a novel graph convolutional network architecture that operates on HINs. ...
However, current methods mainly consider homogeneous networks and ignore the rich semantics and multiple types of objects that are common in heterogeneous information networks (HINs). ...
Acknowledgement: The authors sincerely acknowledge the reviewers for their suggestions which will help in improving the quality of the paper. ...
doi:10.32604/cmc.2020.011609
fatcat:4vu5vbwakze7tmxc72zi5sc2su
Applying Graph Convolution Networks to Recommender Systems based on graph topology
2022
DÜMF Mühendislik Dergisi
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The experimental results show that node similarity-based convolution matrices and GCN-based embeddings significantly improve the prediction accuracy in recommender systems compared to state-of-art approaches ...
Recently, graph representation learning methods based on node embedding have drawn attention in Recommender systems such as Graph Convolutional Networks (GCNs) that is powerful method for collaborative ...
So, the main goal is to predict the possible link in the user-item network by observing the link information. ...
doi:10.24012/dumf.1081137
fatcat:6v7mnjkuijc4zezzbdtlaedlpm
Deep Learning Approach on Information Diffusion in Heterogeneous Networks
[article]
2019
arXiv
pre-print
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In this paper, information diffusion is considered through a latent representation learning of the heterogeneous networks to encode in a deep learning model. ...
To this end, we propose a novel meta-path representation learning approach, Heterogeneous Deep Diffusion(HDD), to exploit meta-paths as main entities in networks. ...
Furthermore, the proposed approach is employed on different deep neural network architectures to predict information diffusion tasks in an end-to-end framework. ...
arXiv:1902.08810v1
fatcat:wqpp552pdraffh7pcp3dfzv46a
Biological network analysis with deep learning
2020
Briefings in Bioinformatics
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One major trend in the field is to use deep learning for this goal and, more specifically, to use methods that work with networks, the so-called graph neural networks (GNNs). ...
We then discuss domains in bioinformatics in which graph neural networks are frequently being applied at the moment, such as protein function prediction, protein–protein interaction prediction and in silico ...
Funding This work was supported in part from the Alfried Krupp Prize for Young University Teachers of the Alfried Krupp von Bohlen und Halbach-Stiftung (K.B.) and in part from the European Union's Horizon ...
doi:10.1093/bib/bbaa257
pmid:33169146
pmcid:PMC7986589
fatcat:x7salmmidjei3og6ripsizkbam
Deep Representation Learning for Social Network Analysis
[article]
2019
arXiv
pre-print
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Social network analysis is an important problem in data mining. ...
Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection and clustering. ...
efforts also attempt to predict missing links in heterogeneous networks . ...
arXiv:1904.08547v1
fatcat:b7ifkbs2ajggljwntruhn57l3y
Deep Representation Learning for Social Network Analysis
2019
Frontiers in Big Data
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Social network analysis is an important problem in data mining. ...
Network representation leaning facilitates further applications such as classification, link prediction, anomaly detection, and clustering. ...
efforts also attempt to predict missing links in heterogeneous networks . ...
doi:10.3389/fdata.2019.00002
pmid:33693325
pmcid:PMC7931936
fatcat:w7kfbniaf5ctzp3swdttp52h3m
Network representation learning: models, methods and applications
2019
SN Applied Sciences
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Terminologies and problem definition Definition 1 A Network is a graph is the set of vertices and e ∈ E is an edge between any two vertices. ...
Generating an efficient network representation is one important challenge in applying machine learning to network data. ...
In a network embedding approach for link prediction, the nodes are first mapped into a low dimensional space. ...
doi:10.1007/s42452-019-1044-9
fatcat:zvlbj4qozzfw3dxoyevb6wgska
Incorporating Dynamicity of Transportation Network with Multi-Weight Traffic Graph Convolution for Traffic Forecasting
[article]
2019
arXiv
pre-print
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Graph Convolutional Networks (GCN) have given the ability to model complex spatial and temporal dependencies in traffic data and improve the performance of predictions. ...
The proposed model, Multi-Weight Traffic Graph Convolutional Networks (MW-TGC) conduct convolution operation on traffic data with multiple weighted adjacency matrices and combines the features obtained ...
Graph Convolution Networks In an effort to appropriately handle the special characteristics of graph-structured data with deep learning, Graph Convolutional Networks(GCN) have been suggested, and widely ...
arXiv:1909.07105v1
fatcat:3yrpigscujfnhba5vezql5xraq
GAHNE: Graph-Aggregated Heterogeneous Network Embedding
[article]
2020
arXiv
pre-print
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results of downstream tasks based on graph convolutional neural networks. ...
To address these limitations, we propose a novel Graph-Aggregated Heterogeneous Network Embedding (GAHNE), which is designed to extract the semantics of HINs as comprehensively as possible to improve the ...
“Shine: Signed heterogeneous information net- [37] Li, Yujia, et al. “Gated graph sequence neural networks.” arXiv preprint
work embedding for sentiment link prediction.” ...
arXiv:2012.12517v1
fatcat:7efq3643fffsvdgevrpy7odfhm
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