Relevant bibliographies by topics / Graph Pooling and Convolution

Academic literature on the topic 'Graph Pooling and Convolution'

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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 (September 10, 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 and uniformly pools neighboring nodes instead of top nodes in the score-space, resulting in a uniformly coarsened graph. In multiple graph classification tasks, including the protein graphs, the biological graphs and the brain connectivity graphs, we demonstrated that UGPool outperforms other graph pooling methods while maintaining high efficiency. Moreover, we also show that UGPool can be integrated with multiple graph convolution networks to effectively improve performance compared to no pooling.
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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 (December 13, 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 edge features from the dual graph structure. To further enhance the capturing ability of local features, we employ attention pooling, which combines max pooling and average pooling operations. Secondly, we introduce channel attention modules and spatial self-attention modules to improve the representation ability of global features and enhance semantic segmentation accuracy in our network. Experimental results on the S3DIS dataset demonstrate that compared to dynamic graph convolution alone, our proposed approach effectively utilizes both semantic and geometric relationships between point clouds using dual graph convolutions while addressing limitations related to insufficient local feature extraction. The introduction of attention mechanisms helps mitigate underutilization issues with global information, resulting in significant improvements in model performance.
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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 (March 7, 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 (SPGAU). Specifically, unlike existing GCN models that rely on fixed graphs, our model involves a spatial pooling method that emulates the region-growing process of superpixels and constructs multi-level graphs by progressively merging adjacent graph nodes. Inspired by the CNN classification framework U-net, SPGAU’s model has a U-shaped structure, realizing multi-scale feature extraction from coarse to fine and gradually fusing features from different graph levels. Additionally, the proposed graph attention convolution method adaptively aggregates adjacency information, thereby further enhancing feature extraction efficiency. Moreover, a 1D-CNN is established to extract pixel-level features, striking an optimal balance between enhancing the feature quality and reducing the computational burden. Experimental results on three representative benchmark datasets demonstrate that the proposed SPGAU outperforms other mainstream models both qualitatively and quantitatively.
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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 (December 1, 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 transition matrix that we call the maximal entropy transition (MET) matrix derived from a path integral formalism. Importantly, the diagonal entries of the MET matrix are directly related to the subgraph centrality, thus leading to a natural and adaptive pooling mechanism. PAN provides a versatile framework that can be tailored for different graph data with varying sizes and structures. We can view most existing GNN architectures as special cases of PAN. Experimental results show that PAN achieves state-of-the-art performance on various graph classification/regression tasks, including a new benchmark dataset from statistical mechanics that we propose to boost applications of GNN in physical sciences.
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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 (February 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 consider the edge attribute and the loss of physical meaning of the coarse graph obtained from pooling, a grid partition pooling layer is constructed based on the electrical distance between nodes. Finally, 10000 system samples containing different network topologies are generated based on the IEEE 14-node system and its extended system, the accuracy reaches 99.3% in the testing set after training, and the effectiveness of the improvements in graph convolution and graph pooling is verified by comparative experiments.
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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 (May 18, 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 traffic systems which is composed of the micro layers of road networks and the macro layers of region networks, in which the nodes are obtained through pooling method and could include some hot traffic regions such as downtown and CBD etc., while the current GCN is only applied on the micro graph of road networks. In this paper, we propose a novel Hierarchical Graph Convolution Networks (HGCN) for traffic forecasting by operating on both the micro and macro traffic graphs. The proposed method is evaluated on two complex city traffic speed datasets. Compared to the latest GCN based methods like Graph WaveNet, the proposed HGCN gets higher traffic forecasting precision with lower computational cost.The website of the code is https://github.com/guokan987/HGCN.git.
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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 (October 1, 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 more extensive hyperparameter optimizations. To show its applicability, we construct a convolutional graph network with partition pooling that reconstructs simulated interaction vertices for an idealized neutrino detector. The pooling network yields improved performance and is less susceptible to overfitting than a similar network without pooling. The lower resource requirements allow the construction of a deeper network with further improved performance.
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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 (March 6, 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 computational requirements in both model training and inference. By construction, the ReNN-Pool also allows the definition of a symmetric un-pooling operation to recover the original dimensionality of the graphs. We also present a Variational AutoEncoder (VAE) for generating graphs, based on the defined pooling and un-pooling operations, which employs convolutional graph layers in both encoding and decoding phases. The performance of the model was tested on both the realistic use case of a cylindrical graph dataset for a radiotherapy application and the standard benchmark dataset sprite. Compared to other graph pooling techniques, ReNN-Pool proved to improve both performance and computational requirements.
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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 (November 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 (September 28, 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 been developed to encode the high-level features of the networks. Moreover, a graph convolution layer is introduced to aggregate both positive and negative information from neighbor nodes, and the concatenation of two parts generates the final embedding of the nodes. Extensive experiments on three large real-world signed network datasets demonstrate the effectiveness and excellence of the proposed method.
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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, we expand two methods for explaining neural network decisions for convolution neural networks to graph neural networks and compare them with the existing GNNExplainer. We run experiments on standard graph classification tasks using the developed framework and discuss the different pooling methods’ distinctive characteristics. Furthermore, we verify the proposed extensions of the explanation methods’ correctness and measure the agreements among the produced explanations. Finally, we explore the characteristics of different methods for explaining neural network decisions and the insights of different pooling methods by applying these explanation methods.
Med utvecklingen av grafneurala nätverk har detta nya neurala nätverk tillämpats i olika område. Ett av de svåra problemen för forskare inom detta område är hur man väljer en lämplig poolningsmetod för en specifik forskningsuppgift från en mängd befintliga poolningsmetoder. I den här arbetet, baserat på de befintliga vanliga grafpoolingsmetoderna, utvecklar vi ett riktmärke för neuralt nätverk ram som kan användas till olika diagram pooling metoders jämförelse. Genom att använda ramverket jämför vi fyra allmängiltig diagram pooling metod och utforska deras egenskaper. Dessutom utvidgar vi två metoder för att förklara beslut om neuralt nätverk från convolution neurala nätverk till diagram neurala nätverk och jämföra dem med befintliga GNNExplainer. Vi kör experiment av grafisk klassificering uppgifter under benchmarkingramverk och hittade olika egenskaper av olika diagram pooling metoder. Dessutom verifierar vi korrekthet i dessa förklarningsmetoder som vi utvecklade och mäter överenskommelserna mellan dem. Till slut, vi försöker utforska egenskaper av olika metoder för att förklara neuralt nätverks beslut och deras betydelse för att välja pooling metoder i grafisk neuralt nätverk.
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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 problème de l'attribution de catégories d'actions aux séquences vidéos. Cela peut être considéré comme un ingrédient clé pour construire la prochaine génération de systèmes visuels. Nous l'abordons avec des méthodes d'intelligence artificielle, sous le paradigme de l'apprentissage automatique et de l'apprentissage profond, notamment les réseaux de neurones convolutifs. Les réseaux de neurones convolutifs actuels sont de plus en plus profonds, plus gourmands en données et leur succès est donc tributaire de l'abondance de données d'entraînement étiquetées. Les réseaux de neurones convolutifs s'appuient également sur le pooling qui réduit la dimensionnalité des couches de sortie (et donc atténue leur sensibilité à la disponibilité de données étiquetées)
Nowadays, video contents are ubiquitous through the popular use of internet and smartphones, as well as social media. Many daily life applications such as video surveillance and video captioning, as well as scene understanding require sophisticated technologies to process video data. It becomes of crucial importance to develop automatic means to analyze and to interpret the large amount of available video data. In this thesis, we are interested in video action recognition, i.e. the problem of assigning action categories to sequences of videos. This can be seen as a key ingredient to build the next generation of vision systems. It is tackled with AI frameworks, mainly with ML and Deep ConvNets. Current ConvNets are increasingly deeper, data-hungrier and this makes their success tributary of the abundance of labeled training data. ConvNets also rely on (max or average) pooling which reduces dimensionality of output layers (and hence attenuates their sensitivity to the availability of labeled data); however, this process may dilute the information of upstream convolutional layers and thereby affect the discrimination power of the trained video representations, especially when the learned action categories are fine-grained
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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 carried out during the PhD period concerned, after a careful preliminary study on the applications of the Modern Graph Theory, the development of an innovative Convolutional Model to model brain connections at the cellular level capable of combining exponential models and power law models. This new theoretical framework has been introduced in the first instance with an aspatial graph formulation and then proposed a spatial graph model with Convolutive connectivity able to fit the degree distributions of data driven Connectome reconstructions. In order to evaluate the qualities of the Convolutional Model, theoretical graphical models capable of characterizing brain activity were taken into consideration. In the specific case, the model examined characterizes the epileptic activity through a simple Hindmarsh-Rose model system of point neurons and reproduces the functional characteristics observed in the data driven model. Such a model provides insight into the deep impact of micro connectivity in macro-scale brain activity. Other evaluations have been done in different applications, in the field of image cell segmentation with Explainable Artificial Intelligence's neuronal agents in which has been used a methodology that is not only explainable but also resistant to adversarial noise and also in the field of modelling Covid-19 outbreak in gaining insight on vaccines and role of our habits as individuals in the pandemic spread. Therefore, the core of the thesis is to introduce Modern Graph Theory with a new competitive Convolutive Model and then expose some applications to real-world problems like a characterization of Brain networks, simulation and analysis of Brain dynamics with a particular focus on Epilepsy, Immunofluorescence images segmentation with neuronal based agents and modelling of Covid-19 Epidemic spread with a specific interest in human social networks. All this takes continuously into account the whole dialogue between Graph Theory and its applications.
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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 leverage Twitter data to help in earlier detection of service disruptions. This work involves developing a pure Data Mining approach and a couple different approaches that use Graph Neural Networks to identify transit disruption related information in Tweets from a live Twitter stream related to the Washington Metropolitan Area Transit Authority (WMATA) metro system. After developing three different models, a Dynamic Query Expansion model, a Tweet-GCN and a Tweet-Level GCN to represent the data corpus we performed various experiments and benchmark evaluations against other existing baseline models, to justify the efficacy of our approaches. After seeing astounding results across both the Tweet-GCN and Tweet-Level GCN, with an average accuracy of approximately 87.3% and 89.9% we can conclude that not only are these two graph neural models superior for basic NLP text classification, but they also outperform other models in identifying transit disruptions.
Master of Science
Millions of people worldwide rely on public transit networks for their daily commutes and day to day movements. With the growth in the number of people using the service, there has been an increase in the number of daily passengers affected by service disruptions. This thesis and research involves proposing and developing three different approaches to help aid in the timely detection of these disruptions. In this work we have developed a pure data mining approach along with two deep learning models using neural networks and live data from Twitter to identify these disruptions. The data mining approach uses a set of dirsuption related input keywords to identify similar keywords within the live Twitter data. By collecting historical data we were able to create deep learning models that represent the vocabulary from the disruptions related Tweets in the form of a graph. A graph is a collection of data values where the data points are connected to one another based on their relationships. A longer chain of connection between two words defines a weak relationship, a shorter chain defines a stronger relationship. In our graph, words with similar contextual meanings are connected to each other over shorter distances, compared to words with different meanings. At the end we use a neural network as a classifier to scan this graph to learn the semantic relationships within our data. Afterwards, this learned information can be used to accurately classify the disruption related Tweets within a pool of random Tweets. Once all the proposed approaches have been developed, a benchmark evaluation is performed against other existing text classification techniques, to justify the effectiveness of the approaches. The final results indicate that the proposed graph based models achieved a higher accuracy, compared to the data mining model, and also outperformed all the other baseline models. Our Tweet-Level GCN had the highest accuracy of 89.9%.
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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 Convolutional Network, Graph Attention neTwork) un’applicazione che ne mostri sia il funzionamento che l’importanza. Discutiamo, ulteriormente, l’implementazione di un algoritmo di classificazione basato su due varianti dell’architettura Graph Convolutional Network, addestrato e testato sul dataset PROTEINS, capace di classificare le proteine del dataset in due categorie: enzimi e non enzimi.
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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 propriétés liées aux arrêtes. A l’aide de ces convolutions, nous proposons des extensions des réseaux convolutifs à des structures de graphes. Nos recherches nous conduisent à proposer une formulation générique de la propagation entre deux couches de neurones que nous appelons la contraction neurale. De cette formule, nous dérivons plusieurs nouveaux modèles de réseaux de neurones, applicables sur des domaines irréguliers, et qui font preuve de résultats au même niveau que l’état de l’art voire meilleurs pour certains
Convolutional neural networks have proven to be the deep learning model that performs best on regularly structured datasets like images or sounds. However, they cannot be applied on datasets with an irregular structure (e.g. sensor networks, citation networks, MRIs). In this thesis, we develop an algebraic theory of convolutions on irregular domains. We construct a family of convolutions that are based on group actions (or, more generally, groupoid actions) that acts on the vertex domain and that have properties that depend on the edges. With the help of these convolutions, we propose extensions of convolutional neural netowrks to graph domains. Our researches lead us to propose a generic formulation of the propagation between layers, that we call the neural contraction. From this formulation, we derive many novel neural network models that can be applied on irregular domains. Through benchmarks and experiments, we show that they attain state-of-the-art performances, and beat them in some cases
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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 to new users. We build our proposed recommender system for recommending the articles of Peltarion Knowledge Center. By incorporating two data sources, implicit user feedback based on pageview data as well as the content of articles, we propose a hybrid recommender solution. Throughout our experiments, we compare our proposed solution with a matrix factorization approach as well as a popularity- based and a random baseline, analyse the hyperparameters of our model, and examine the capability of our solution to give recommendations to new users who were not part of the training data set. Our model results in slightly lower, but similar Mean Average Precision and Mean Reciprocal Rank scores to the matrix factorization approach, and outperforms the popularity- based and random baselines. The main advantages of our model are computational efficiency and its ability to give relevant recommendations to new users without the need for retraining the model, which are key features for real- world use cases.
Rekommendationssystem används ofta på webbplatser och applikationer för att hjälpa användare att hitta relevant innehåll baserad på deras intressen. Med utvecklingen av grafneurala nätverk nådde toppmoderna resultat inom rekommendationssystem och representerade data i form av en graf. De flesta grafbaserade lösningar har dock svårt med beräkningskomplexitet eller att generalisera till nya användare. Därför föreslår vi ett nytt grafbaserat rekommendatorsystem genom att modifiera Simple Graph Convolution. De här tillvägagångssätt är en effektiv grafnodsklassificering och lägga till möjligheten att generalisera till nya användare. Vi bygger vårt föreslagna rekommendatorsystem för att rekommendera artiklarna från Peltarion Knowledge Center. Genom att integrera två datakällor, implicit användaråterkoppling baserad på sidvisningsdata samt innehållet i artiklar, föreslår vi en hybridrekommendatörslösning. Under våra experiment jämför vi vår föreslagna lösning med en matrisfaktoriseringsmetod samt en popularitetsbaserad och en slumpmässig baslinje, analyserar hyperparametrarna i vår modell och undersöker förmågan hos vår lösning att ge rekommendationer till nya användare som inte deltog av träningsdatamängden. Vår modell resulterar i något mindre men liknande Mean Average Precision och Mean Reciprocal Rank poäng till matrisfaktoriseringsmetoden och överträffar de popularitetsbaserade och slumpmässiga baslinjerna. De viktigaste fördelarna med vår modell är beräkningseffektivitet och dess förmåga att ge relevanta rekommendationer till nya användare utan behov av omskolning av modellen, vilket är nyckelfunktioner för verkliga användningsfall.
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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 les cardinalités de classes sont hautement déséquilibrées. Pour atténuer ces limitations, le transfer learning et la pondération de la fonction de coûts ont été employés. Des méthodes basées graphes sont également proposées et testées. La première consiste en la conception d'un classificateur de graphes de type perceptron. Le second travail basé sur les graphes de cette thèse est la définition d'un opérateur de convolution pour construire un modèle de réseaux de neurones convolutifs s'appliquant sur les graphes (GCNN.) Le dernier chapitre de la thèse s'applique à utiliser les méthodes mentionnées précédemment à des problèmes de reconnaissance d'images d'insectes. Deux bases d'images sont ici proposées. Là première est constituée d'images prises en laboratoire sur arrière-plan constant. La seconde base est issue de la base ImageNet. Cette base est composée d'images prises en contexte naturel. Les CNN entrainés avec transfer learning sont les plus performants sur ces bases d'images
The goal of this thesis is to investigate insect recognition as an image-based pattern recognition problem. Although this problem has been extensively studied along the previous three decades, an element is to the best of our knowledge still to be experimented as of 2017: deep approaches. Therefore, a contribution is about determining to what extent deep convolutional neural networks (CNNs) can be applied to image-based insect recognition. Graph-based representations and methods have also been tested. Two attempts are presented: The former consists in designing a graph-perceptron classifier and the latter graph-based work in this thesis is on defining convolution on graphs to build graph convolutional neural networks. The last chapter of the thesis deals with applying most of the aforementioned methods to insect image recognition problems. Two datasets are proposed. The first one consists of lab-based images with constant background. The second one is generated by taking a ImageNet subset. This set is composed of field-based images. CNNs with transfer learning are the most successful method applied on these datasets
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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, 86–98. Cham: 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, 473–83. Cham: 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, 1–16. Cham: 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, 147–56. Singapore: 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, 426–37. Singapore: 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, 294–306. Cham: 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, 377–93. Cham: 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, 558–65. Cham: 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, 114–26. Cham: 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, 105–17. Cham: 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 solve such problems, this article proposes a Maximum Variance graph feature Multistructure graph classification method (MVM), which extracts graph information from the perspective of graph nodes feature and graph topology. From the nodes feature perspective, we enlarge the variance between different classes while maintaining the variance between the same class of data. Then the hierarchical graph convolution and pooling are performed from a topological perspective and combined with a CNN readout mechanism to preserve more graph information to obtain a graph-level representation with strong discrimination. Experiments demonstrate that our method outperforms several number of state-of-the-art graph classification methods on multiple publicly available datasets.
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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}. California: 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-channel Graph Pooling method named MuchPool, which captures the local structure, the global structure, and node feature simultaneously in graph pooling. Specifically, we use two channels to conduct dropping pooling based on the local topology and node features respectively, and one channel to conduct coarsening pooling. Then a cross-channel convolution operation is designed to refine the graph representations of different channels. Finally, the pooling results are aggregated as the final pooled graph. Extensive experiments on six benchmark datasets present the superior performance of MuchPool. The code of this work is publicly available at Github.
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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}. California: 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 behaviors) by converting interaction sequence data into multiple graphs (i.e., a user-item graph, an item-item graph, and a user-subseq graph). We design a novel graph convolutional network (PGCN) to learn shared representations of users and items with the three heterogeneous graphs. In our approach, a neighbor pooling and a convolution operation are designed to aggregate features of neighbors. Extensive experiments on two real-world datasets demonstrate that our graph convolution approaches outperform various competitive methods in terms of two metrics, and the heterogeneous graphs are proved effective for improving recommendation performance.
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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}. California: 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 fixed clustering ignores the inherent multiple characteristics of nodes. To compensate the shrinking loss and learn the various nodes’ characteristics, we propose the multi-channel graph neural networks (MuchGNN). Motivated by the underlying mechanisms developed in convolutional neural networks, we define the tailored graph convolutions to learn a series of graph channels at each layer, and shrink the graphs hierarchically to en-code the pooled structures. Experimental results on real-world datasets demonstrate the superiority of MuchGNN over the state-of-the-art methods.
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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}. California: 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, Graph Atrous Convolution (GAC) and Graph Transformer Layer (GTL), respectively for the extraction of local multi-scale and global long-range information, are combined and stacked in an encoder-decoder structure, where graph pooling and unpooling are adopted for the interaction of multi-scale information from local to global (e.g., part-scale and body-scale). Extensive experiments on the Human3.6M and MPI-INF-3DHP datasets demonstrate that the proposed PoseGTAC model exceeds all previous methods and achieves state-of-the-art performance.
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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}. California: 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 individual CNN layers are implemented by GCN and utilized to synthesize comprehensive channel information and determine the pruning scheme for the overall CNN model. The training process of each agent is carried out using Reinforcement Learning (RL) to ensure their convergence and adaptability to various network architectures. The proposed solution is assessed based on a range of image classification datasets i.e., CIFAR and Tiny-ImageNet. The numerical results indicate that the proposed pruning method outperforms the pure magnitude-based or similarity-based pruning solutions and other SOTA methods (e.g., HRank and SCP). For example, the proposed method can prune VGG16 by removing 93% of the model parameters without any accuracy reduction in the CIFAR10 dataset.
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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. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3308558.3313395.

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