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Relevant bibliographies by topics / Graph Pooling and Convolution

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Conference papers

Academic literature on the topic 'Graph Pooling and Convolution'

Author: Grafiati

Published: 25 May 2024

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Journal articles on the topic "Graph Pooling and Convolution"

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Qin, Jian, Li Liu, Hui Shen, and Dewen Hu. "Uniform Pooling for Graph Networks." Applied Sciences 10, no. 18 (2020): 6287. http://dx.doi.org/10.3390/app10186287.

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The graph convolution network has received a lot of attention because it extends the convolution to non-Euclidean domains. However, the graph pooling method is still less concerned, which can learn coarse graph embedding to facilitate graph classification. Previous pooling methods were based on assigning a score to each node and then pooling only the highest-scoring nodes, which might throw away whole neighbourhoods of nodes and therefore information. Here, we proposed a novel pooling method UGPool with a new point-of-view on selecting nodes. UGPool learns node scores based on node features an

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Yang, Xiaowen, Yanghui Wen, Shichao Jiao, Rong Zhao, Xie Han, and Ligang He. "Point Cloud Segmentation Network Based on Attention Mechanism and Dual Graph Convolution." Electronics 12, no. 24 (2023): 4991. http://dx.doi.org/10.3390/electronics12244991.

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To overcome the limitations of inadequate local feature representation and the underutilization of global information in dynamic graph convolutions, we propose a network that combines attention mechanisms with dual graph convolutions. Firstly, we construct a static graph based on the dynamic graph using the K-nearest neighbors algorithm and geometric distances of point clouds. This integration of dynamic and static graphs forms a dual graph structure, compensating for the underutilization of geometric positional relationships in the dynamic graph. Next, edge convolutions are applied to extract

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Diao, Qi, Yaping Dai, Jiacheng Wang, Xiaoxue Feng, Feng Pan, and Ce Zhang. "Spatial-Pooling-Based Graph Attention U-Net for Hyperspectral Image Classification." Remote Sensing 16, no. 6 (2024): 937. http://dx.doi.org/10.3390/rs16060937.

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In recent years, graph convolutional networks (GCNs) have attracted increasing attention in hyperspectral image (HSI) classification owing to their exceptional representation capabilities. However, the high computational requirements of GCNs have led most existing GCN-based HSI classification methods to utilize superpixels as graph nodes, thereby limiting the spatial topology scale and neglecting pixel-level spectral–spatial features. To address these limitations, we propose a novel HSI classification network based on graph convolution called the spatial-pooling-based graph attention U-net (SP

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Ma, Zheng, Junyu Xuan, Yu Guang Wang, Ming Li, and Pietro Liò. "Path integral based convolution and pooling for graph neural networks*." Journal of Statistical Mechanics: Theory and Experiment 2021, no. 12 (2021): 124011. http://dx.doi.org/10.1088/1742-5468/ac3ae4.

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Abstract Graph neural networks (GNNs) extend the functionality of traditional neural networks to graph-structured data. Similar to CNNs, an optimized design of graph convolution and pooling is key to success. Borrowing ideas from physics, we propose path integral-based GNNs (PAN) for classification and regression tasks on graphs. Specifically, we consider a convolution operation that involves every path linking the message sender and receiver with learnable weights depending on the path length, which corresponds to the maximal entropy random walk. It generalizes the graph Laplacian to a new tr

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Li, Shenhao, Zhichon Pan, Hongyi Li, Yue Xiao, Ming Liu, and Xiaorui Wang. "Convergence criterion of power flow calculation based on graph neural network." Journal of Physics: Conference Series 2703, no. 1 (2024): 012042. http://dx.doi.org/10.1088/1742-6596/2703/1/012042.

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Abstract In order to solve the problem of current data-driven power flow calculation methods rarely consider the divergence of power flow, which always maps a false system power flow when a divergence power flow case was given, a data-driven power flow convergence method based on DGAT-GPPool graph neural network classifier is proposed. Firstly, to solve the problem that the classical graph convolution method does not consider the edge attribute, a double-view graph attention convolution layer is constructed based on line admittance. Secondly, to solve the existing pooling method also does not

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Guo, Kan, Yongli Hu, Yanfeng Sun, Sean Qian, Junbin Gao, and Baocai Yin. "Hierarchical Graph Convolution Network for Traffic Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 1 (2021): 151–59. http://dx.doi.org/10.1609/aaai.v35i1.16088.

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Traffic forecasting is attracting considerable interest due to its widespread application in intelligent transportation systems. Given the complex and dynamic traffic data, many methods focus on how to establish a spatial-temporal model to express the non-stationary traffic patterns. Recently, the latest Graph Convolution Network (GCN) has been introduced to learn spatial features while the time neural networks are used to learn temporal features. These GCN based methods obtain state-of-the-art performance. However, the current GCN based methods ignore the natural hierarchical structure of tra

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Bachlechner, M., T. Birkenfeld, P. Soldin, A. Stahl, and C. Wiebusch. "Partition pooling for convolutional graph network applications in particle physics." Journal of Instrumentation 17, no. 10 (2022): P10004. http://dx.doi.org/10.1088/1748-0221/17/10/p10004.

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Abstract Convolutional graph networks are used in particle physics for effective event reconstructions and classifications. However, their performances can be limited by the considerable amount of sensors used in modern particle detectors if applied to sensor-level data. We present a pooling scheme that uses partitioning to create pooling kernels on graphs, similar to pooling on images. Partition pooling can be used to adopt successful image recognition architectures for graph neural network applications in particle physics. The reduced computational resources allow for deeper networks and mor

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Arsini, Lorenzo, Barbara Caccia, Andrea Ciardiello, Stefano Giagu, and Carlo Mancini Terracciano. "Nearest Neighbours Graph Variational AutoEncoder." Algorithms 16, no. 3 (2023): 143. http://dx.doi.org/10.3390/a16030143.

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Graphs are versatile structures for the representation of many real-world data. Deep Learning on graphs is currently able to solve a wide range of problems with excellent results. However, both the generation of graphs and the handling of large graphs still remain open challenges. This work aims to introduce techniques for generating large graphs and test the approach on a complex problem such as the calculation of dose distribution in oncological radiotherapy applications. To this end, we introduced a pooling technique (ReNN-Pool) capable of sampling nodes that are spatially uniform without c

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Cheung, Mark, John Shi, Oren Wright, Lavendar Y. Jiang, Xujin Liu, and Jose M. F. Moura. "Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology." IEEE Signal Processing Magazine 37, no. 6 (2020): 139–49. http://dx.doi.org/10.1109/msp.2020.3014594.

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Chen, Jiawang, and Zhenqiang Wu. "Learning Embedding for Signed Network in Social Media with Hierarchical Graph Pooling." Applied Sciences 12, no. 19 (2022): 9795. http://dx.doi.org/10.3390/app12199795.

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Signed network embedding concentrates on learning fixed-length representations for nodes in signed networks with positive and negative links, which contributes to many downstream tasks in social media, such as link prediction. However, most signed network embedding approaches neglect hierarchical graph pooling in the networks, limiting the capacity to learn genuine signed graph topology. To overcome this limitation, this paper presents a unique deep learning-based Signed network embedding model with Hierarchical Graph Pooling (SHGP). To be more explicit, a hierarchical pooling mechanism has be

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Dissertations / Theses on the topic "Graph Pooling and Convolution"

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Wu, Jindong. "Pooling strategies for graph convolution neural networks and their effect on classification." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-288953.

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With the development of graph neural networks, this novel neural network has been applied in a broader and broader range of fields. One of the thorny problems researchers face in this field is selecting suitable pooling methods for a specific research task from various existing pooling methods. In this work, based on the existing mainstream graph pooling methods, we develop a benchmark neural network framework that can be used to compare these different graph pooling methods. By using the framework, we compare four mainstream graph pooling methods and explore their characteristics. Furthermore

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Mazari, Ahmed. "Apprentissage profond pour la reconnaissance d’actions en vidéos." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS171.

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De nos jours, les contenus vidéos sont omniprésents grâce à Internet et les smartphones, ainsi que les médias sociaux. De nombreuses applications de la vie quotidienne, telles que la vidéo surveillance et la description de contenus vidéos, ainsi que la compréhension de scènes visuelles, nécessitent des technologies sophistiquées pour traiter les données vidéos. Il devient nécessaire de développer des moyens automatiques pour analyser et interpréter la grande quantité de données vidéo disponibles. Dans cette thèse, nous nous intéressons à la reconnaissance d'actions dans les vidéos, c.a.d au pr

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GIACOPELLI, Giuseppe. "An Original Convolution Model to analyze Graph Network Distribution Features." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/553177.

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Modern Graph Theory is a newly emerging field that involves all of those approaches that study graphs differently from Classic Graph Theory. The main difference between Classic and Modern Graph Theory regards the analysis and the use of graph's structures (micro/macro). The former aims to solve tasks hosted on graph nodes, most of the time with no insight into the global graph structure, the latter aims to analyze and discover the most salient features characterizing a whole network of each graph, like degree distributions, hubs, clustering coefficient and network motifs. The activities carrie

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Zulfiqar, Omer. "Detecting Public Transit Service Disruptions Using Social Media Mining and Graph Convolution." Thesis, Virginia Tech, 2021. http://hdl.handle.net/10919/103745.

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In recent years we have seen an increase in the number of public transit service disruptions due to aging infrastructure, system failures and the regular need for maintenance. With the fleeting growth in the usage of these transit networks there has been an increase in the need for the timely detection of such disruptions. Any types of disruptions in these transit networks can lead to delays which can have major implications on the daily passengers. Most current disruption detection systems either do not operate in real-time or lack transit network coverage. The theme of this thesis was to lev

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Pappone, Francesco. "Graph neural networks: theory and applications." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23893/.

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Le reti neurali artificiali hanno visto, negli ultimi anni, una crescita vertiginosa nelle loro applicazioni e nelle architetture dei modelli impiegati. In questa tesi introduciamo le reti neurali su domini euclidei, in particolare mostrando l’importanza dell’equivarianza di traslazione nelle reti convoluzionali, e introduciamo, per analogia, un’estensione della convoluzione a dati strutturati come grafi. Inoltre presentiamo le architetture dei principali Graph Neural Network ed esponiamo, per ognuna delle tre architetture proposte (Spectral graph Convolutional Network, Graph Co

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Vialatte, Jean-Charles. "Convolution et apprentissage profond sur graphes." Thesis, Ecole nationale supérieure Mines-Télécom Atlantique Bretagne Pays de la Loire, 2018. http://www.theses.fr/2018IMTA0118/document.

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Pour l’apprentissage automatisé de données régulières comme des images ou des signaux sonores, les réseaux convolutifs profonds s’imposent comme le modèle de deep learning le plus performant. En revanche, lorsque les jeux de données sont irréguliers (par example : réseaux de capteurs, de citations, IRMs), ces réseaux ne peuvent pas être utilisés. Dans cette thèse, nous développons une théorie algébrique permettant de définir des convolutions sur des domaines irréguliers, à l’aide d’actions de groupe (ou, plus généralement, de groupoïde) agissant sur les sommets d’un graphe, et possédant des pr

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Bereczki, Márk. "Graph Neural Networks for Article Recommendation based on Implicit User Feedback and Content." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300092.

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Recommender systems are widely used in websites and applications to help users find relevant content based on their interests. Graph neural networks achieved state- of-the- art results in the field of recommender systems, working on data represented in the form of a graph. However, most graph- based solutions hold challenges regarding computational complexity or the ability to generalize to new users. Therefore, we propose a novel graph- based recommender system, by modifying Simple Graph Convolution, an approach for efficient graph node classification, and add the capability of generalizing t

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Lamma, Tommaso. "A mathematical introduction to geometric deep learning." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2021. http://amslaurea.unibo.it/23886/.

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Lo scopo del geometric deep learning è quello di estendere l'algoritmo di deep learning sviluppato per la classificazione di immagini a domini non euclidei come grafi e complessi simpliciali.In questa tesi ci proponiamo di dare una definizione matematica dei concetti cardine utilizzati nel geometric deep learning quali equivarianza e convoluzione sui grafi. Vedremo inoltre come definire una rete convoluzionale invariante rispetto all'azione di gruppi.

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Karimi, Ahmad Maroof. "DATA SCIENCE AND MACHINE LEARNING TO PREDICT DEGRADATION AND POWER OF PHOTOVOLTAIC SYSTEMS: CONVOLUTIONAL AND SPATIOTEMPORAL GRAPH NEURAL NETWORK." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1601082841477951.

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Martineau, Maxime. "Deep learning onto graph space : application to image-based insect recognition." Thesis, Tours, 2019. http://www.theses.fr/2019TOUR4024.

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Le but de cette thèse est d'étudier la reconnaissance d'insectes comme un problème de reconnaissance des formes basé images. Bien que ce problème ait été étudié en profondeur au long des trois dernières décennies, un aspect reste selon nous toujours à expérimenter à ce jour : les approches profondes (deep learning). À cet effet, la première contribution de cette thèse consiste à déterminer la faisabilité de l'application des réseaux de neurones convolutifs profonds (CNN) au problème de reconnaissance d'images d'insectes. Les limitations majeures ont les suivantes: les images sont très rares et

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Book chapters on the topic "Graph Pooling and Convolution"

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Gopinath, Karthik, Christian Desrosiers, and Herve Lombaert. "Adaptive Graph Convolution Pooling for Brain Surface Analysis." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20351-1_7.

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Corcoran, Padraig. "Function Space Pooling for Graph Convolutional Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-57321-8_26.

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Wendlinger, Lorenz, Michael Granitzer, and Christofer Fellicious. "Pooling Graph Convolutional Networks for Structural Performance Prediction." In Machine Learning, Optimization, and Data Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-25891-6_1.

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Sai Prasanna, M. S., and A. Senthil Thilak. "Diagnosis of Autism Spectrum Disorder Using Context-Based Pooling and Cluster-Graph Convolution Networks." In Proceedings of the 2nd International Conference on Cognitive and Intelligent Computing. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-2746-3_15.

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Liu, Wenya, Zhi Yang, Haitao Gan, Zhongwei Huang, Ran Zhou, and Ming Shi. "Hierarchical Pooling Graph Convolutional Neural Network for Alzheimer’s Disease Diagnosis." In PRICAI 2023: Trends in Artificial Intelligence. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-7019-3_39.

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Bacciu, Davide, and Luigi Di Sotto. "A Non-negative Factorization Approach to Node Pooling in Graph Convolutional Neural Networks." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-35166-3_21.

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Liu, Chuang, Yibing Zhan, Xueqi Ma, Dapeng Tao, Bo Du, and Wenbin Hu. "Masked Graph Auto-Encoder Constrained Graph Pooling." In Machine Learning and Knowledge Discovery in Databases. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-26390-3_23.

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Zhang, Yu, Dajiang Liu, and Yongkang Xing. "Dynamic Convolution Pruning Using Pooling Characteristic in Convolution Neural Networks." In Communications in Computer and Information Science. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-92307-5_65.

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Guo, Yanwen, and Yu Cao. "Multi-subspace Attention Graph Pooling." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2022. http://dx.doi.org/10.1007/978-3-031-20865-2_9.

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Islam, Muhammad Ifte Khairul, Max Khanov, and Esra Akbas. "MPool: Motif-Based Graph Pooling." In Advances in Knowledge Discovery and Data Mining. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33377-4_9.

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Conference papers on the topic "Graph Pooling and Convolution"

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Wang, Lingang, and Lei Sun. "MVMNET: Graph Classification Pooling Method with Maximum Variance Mapping." In 12th International Conference on Advanced Information Technologies and Applications. Academy and Industry Research Collaboration Center (AIRCC), 2023. http://dx.doi.org/10.5121/csit.2023.130613.

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Graph Neural Networks (GNNs) have been shown to effectively model graph-structured data for tasks such as graph node classification, link prediction, and graph classification. The graph pooling method is an indispensable structure in the graph neural network model. The traditional graph neural network pooling methods all employ downsampling or node aggregating to reduce graph nodes. However, these methods do not fully consider spatial distribution of nodes of different classes of graphs, and making it difficult to distinguish the class of graphs with spatial locations close to each other. To s

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Du, Jinlong, Senzhang Wang, Hao Miao, and Jiaqiang Zhang. "Multi-Channel Pooling Graph Neural Networks." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/199.

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Graph pooling is a critical operation to downsample a graph in graph neural networks. Existing coarsening pooling methods (e.g. DiffPool) mostly focus on capturing the global topology structure by assigning the nodes into several coarse clusters, while dropping pooling methods (e.g. SAGPool) try to preserve the local topology structure by selecting the top-k representative nodes. However, there lacks an effective method to integrate the two types of methods so that both the local and the global topology structure of a graph can be well captured. To address this issue, we propose a Multi-channe

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Xu, Yanan, Yanmin Zhu, Yanyan Shen, and Jiadi Yu. "Learning Shared Vertex Representation in Heterogeneous Graphs with Convolutional Networks for Recommendation." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/642.

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Collaborative Filtering (CF) is among the most successful techniques in recommendation tasks. Recent works have shown a boost of performance of CF when introducing the pairwise relationships between users and items or among items (users) using interaction data. However, these works usually only utilize one kind of information, i.e., user preference in a user-item interaction matrix or item dependency in interaction sequences which can limit the recommendation performance. In this paper, we propose to mine three kinds of information (user preference, item dependency, and user similarity on beha

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Qi, Zhang, and Ryosuke Saga. "Pooling Method Based on Edge Contraction for Graph Convolution Networks." In 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, 2022. http://dx.doi.org/10.1109/smc53654.2022.9945438.

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Zhou, Kaixiong, Qingquan Song, Xiao Huang, Daochen Zha, Na Zou, and Xia Hu. "Multi-Channel Graph Neural Networks." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/188.

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The classification of graph-structured data has be-come increasingly crucial in many disciplines. It has been observed that the implicit or explicit hierarchical community structures preserved in real-world graphs could be useful for downstream classification applications. A straightforward way to leverage the hierarchical structure is to make use the pooling algorithms to cluster nodes into fixed groups, and shrink the input graph layer by layer to learn the pooled graphs.However, the pool shrinking discards the graph details to make it hard to distinguish two non-isomorphic graphs, and the f

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Wang, Ziyun, Yang Ding, Shiyu Lu, and Cheng Han. "Mesh Model Codec Based on Fusion Graph Convolution and Parallel Pooling." In 2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML). IEEE, 2023. http://dx.doi.org/10.1109/icicml60161.2023.10424758.

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Cheung, Mark, John Shi, Lavender Jiang, Oren Wright, and Jose M. F. Moura. "Pooling in Graph Convolutional Neural Networks." In 2019 53rd Asilomar Conference on Signals, Systems, and Computers. IEEE, 2019. http://dx.doi.org/10.1109/ieeeconf44664.2019.9048796.

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Zhu, Yiran, Xing Xu, Fumin Shen, Yanli Ji, Lianli Gao, and Heng Tao Shen. "PoseGTAC: Graph Transformer Encoder-Decoder with Atrous Convolution for 3D Human Pose Estimation." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/188.

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Graph neural networks (GNNs) have been widely used in the 3D human pose estimation task, since the pose representation of a human body can be naturally modeled by the graph structure. Generally, most of the existing GNN-based models utilize the restricted receptive ﬁelds of ﬁlters and single-scale information, while neglecting the valuable multi-scale contextual information. To tackle this issue, we propose a novel Graph Transformer Encoder-Decoder with Atrous Convolution, named PoseGTAC, to effectively extract multi-scale context and long-range information. In our proposed PoseGTAC model, Gra

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Jiang, Di, Yuan Cao, and Qiang Yang. "On the Channel Pruning using Graph Convolution Network for Convolutional Neural Network Acceleration." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/431.

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Network pruning is considered efficient for sparsification and acceleration of Convolutional Neural Network (CNN) based models that can be adopted in re-source-constrained environments. Inspired by two popular pruning criteria, i.e. magnitude and similarity, this paper proposes a novel structural pruning method based on Graph Convolution Network (GCN) to further promote compression performance. The channel features are firstly extracted by Global Average Pooling (GAP) from a batch of samples, and a graph model for each layer is generated based on the similarity of features. A set of agents for

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Gao, Hongyang, Yongjun Chen, and Shuiwang Ji. "Learning Graph Pooling and Hybrid Convolutional Operations for Text Representations." In WWW '19: The Web Conference. ACM, 2019. http://dx.doi.org/10.1145/3308558.3313395.

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