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Temporal network embedding using graph attention network

Anuraj Mohan, K V Pramod
2021 Complex & Intelligent Systems  
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

Jinli Zhang, Zongli Jiang, Zheng Chen, Xiaohua Hu
2020 IEEE Access  
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]

S. He, S. Xiong, Y. Ou, J. Zhang, J. Wang, Y. Huang, Y. Zhang
2021 arXiv   pre-print
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]

Xin Xin, Alexandros Karatzoglou, Ioannis Arapakis, Joemon M.Jose
2020 arXiv   pre-print
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]

Xi Xiong, Kaan Ozbay, Li Jin, Chen Feng
2020 arXiv   pre-print
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]

Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, Hyunwoo J. Kim
2020 arXiv   pre-print
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

Yong Wu, Binjun Wang, Wei Li
2020 Computers Materials & Continua  
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

Alper ÖZCAN
2022 DÜMF Mühendislik Dergisi  
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]

Soheila Molaei, Hadi Zare, Hadi Veisi
2019 arXiv   pre-print
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

Giulia Muzio, Leslie O'Bray, Karsten Borgwardt
2020 Briefings in Bioinformatics  
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]

Qiaoyu Tan, Ninghao Liu, Xia Hu
2019 arXiv   pre-print
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

Qiaoyu Tan, Ninghao Liu, Xia Hu
2019 Frontiers in Big Data  
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

Anuraj Mohan, K. V. Pramod
2019 SN Applied Sciences  
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]

Yu Yol Shin, Yoonjin Yoon
2019 arXiv   pre-print
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]

Xiaohe Li, Lijie Wen, Chen Qian, Jianmin Wang
2020 arXiv   pre-print
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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