Journal articles: 'Hypergraph Representation Learning'

1

Zhang, Liyan, Jingfeng Guo, Jiazheng Wang, Jing Wang, Shanshan Li, and Chunying Zhang. "Hypergraph and Uncertain Hypergraph Representation Learning Theory and Methods." Mathematics 10, no. 11 (June 3, 2022): 1921. http://dx.doi.org/10.3390/math10111921.

Full text

Abstract:

With the advent of big data and the information age, the data magnitude of various complex networks is growing rapidly. Many real-life situations cannot be portrayed by ordinary networks, while hypergraphs have the ability to describe and characterize higher order relationships, which have attracted extensive attention from academia and industry in recent years. Firstly, this paper described the development process, the application areas, and the existing review research of hypergraphs; secondly, introduced the theory of hypergraphs briefly; then, compared the learning methods of ordinary graphs and hypergraphs from three aspects: matrix decomposition, random walk, and deep learning; next, introduced the structural optimization of hypergraphs from three perspectives: dynamic hypergraphs, hyperedge weight optimization, and multimodal hypergraph generation; after that, the applicability of three uncertain hypergraph models were analyzed based on three uncertainty theories: probability theory, fuzzy set, and rough set; finally, the future research directions of hypergraphs and uncertain hypergraphs were prospected.

APA, Harvard, Vancouver, ISO, and other styles

2

Feng, Yifan, Haoxuan You, Zizhao Zhang, Rongrong Ji, and Yue Gao. "Hypergraph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3558–65. http://dx.doi.org/10.1609/aaai.v33i01.33013558.

Full text

Abstract:

In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure. Confronting the challenges of learning representation for complex data in real practice, we propose to incorporate such data structure in a hypergraph, which is more flexible on data modeling, especially when dealing with complex data. In this method, a hyperedge convolution operation is designed to handle the data correlation during representation learning. In this way, traditional hypergraph learning procedure can be conducted using hyperedge convolution operations efficiently. HGNN is able to learn the hidden layer representation considering the high-order data structure, which is a general framework considering the complex data correlations. We have conducted experiments on citation network classification and visual object recognition tasks and compared HGNN with graph convolutional networks and other traditional methods. Experimental results demonstrate that the proposed HGNN method outperforms recent state-of-theart methods. We can also reveal from the results that the proposed HGNN is superior when dealing with multi-modal data compared with existing methods.

APA, Harvard, Vancouver, ISO, and other styles

3

Shen, William, Felipe Trevizan, and Sylvie Thiébaux. "Learning Domain-Independent Planning Heuristics with Hypergraph Networks." Proceedings of the International Conference on Automated Planning and Scheduling 30 (June 1, 2020): 574–84. http://dx.doi.org/10.1609/icaps.v30i1.6754.

Full text

Abstract:

We present the first approach capable of learning domain-independent planning heuristics entirely from scratch. The heuristics we learn map the hypergraph representation of the delete-relaxation of the planning problem at hand, to a cost estimate that approximates that of the least-cost path from the current state to the goal through the hypergraph. We generalise Graph Networks to obtain a new framework for learning over hypergraphs, which we specialise to learn planning heuristics by training over state/value pairs obtained from optimal cost plans. Our experiments show that the resulting architecture, STRIPS-HGNs, is capable of learning heuristics that are competitive with existing delete-relaxation heuristics including LM-cut. We show that the heuristics we learn are able to generalise across different problems and domains, including to domains that were not seen during training.

APA, Harvard, Vancouver, ISO, and other styles

4

Bouhlel, Noura, Ghada Feki, Anis Ben Ammar, and Chokri Ben Amar. "Hypergraph learning with collaborative representation for image search reranking." International Journal of Multimedia Information Retrieval 9, no. 3 (January 22, 2020): 205–14. http://dx.doi.org/10.1007/s13735-019-00191-w.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

Ding, Deqiong, Xiaogao Yang, Fei Xia, Tiefeng Ma, Haiyun Liu, and Chang Tang. "Unsupervised feature selection via adaptive hypergraph regularized latent representation learning." Neurocomputing 378 (February 2020): 79–97. http://dx.doi.org/10.1016/j.neucom.2019.10.018.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Zhang, Ruochi, and Jian Ma. "MATCHA: Probing Multi-way Chromatin Interaction with Hypergraph Representation Learning." Cell Systems 10, no. 5 (May 2020): 397–407. http://dx.doi.org/10.1016/j.cels.2020.04.004.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Qi, Xianglong, Yang Gao, Ruibin Wang, Minghua Zhao, Shengjia Cui, and Mohsen Mortazavi. "Learning High-Order Semantic Representation for Intent Classification and Slot Filling on Low-Resource Language via Hypergraph." Mathematical Problems in Engineering 2022 (September 16, 2022): 1–16. http://dx.doi.org/10.1155/2022/8407713.

Full text

Abstract:

Representation of language is the first and critical task for Natural Language Understanding (NLU) in a dialogue system. Pretraining, embedding model, and fine-tuning for intent classification and slot-filling are popular and well-performing approaches but are time consuming and inefficient for low-resource languages. Concretely, the out-of-vocabulary and transferring to different languages are two tough challenges for multilingual pretrained and cross-lingual transferring models. Furthermore, quality-proved parallel data are necessary for the current frameworks. Stepping over these challenges, different from the existing solutions, we propose a novel approach, the Hypergraph Transfer Encoding Network “HGTransEnNet. The proposed model leverages off-the-shelf high-quality pretrained word embedding models of resource-rich languages to learn the high-order semantic representation of low-resource languages in a transductive clustering manner of hypergraph modeling, which does not need parallel data. The experiments show that the representations learned by “HGTransEnNet” for low-resource language are more effective than the state-of-the-art language models, which are pretrained on a large-scale multilingual or monolingual corpus, in intent classification and slot-filling tasks on Indonesian and English datasets.

APA, Harvard, Vancouver, ISO, and other styles

8

Li, Wang, Zhang Yong, Yuan Wei, and Shi Hongxing. "Vehicle Reidentification via Multifeature Hypergraph Fusion." International Journal of Digital Multimedia Broadcasting 2021 (March 18, 2021): 1–10. http://dx.doi.org/10.1155/2021/6641633.

Full text

Abstract:

Vehicle reidentification refers to the mission of matching vehicles across nonoverlapping cameras, which is one of the critical problems of the intelligent transportation system. Due to the resemblance of the appearance of the vehicles on road, traditional methods could not perform well on vehicles with high similarity. In this paper, we utilize hypergraph representation to integrate image features and tackle the issue of vehicles re-ID via hypergraph learning algorithms. A feature descriptor can only extract features from a single aspect. To merge multiple feature descriptors, an efficient and appropriate representation is particularly necessary, and a hypergraph is naturally suitable for modeling high-order relationships. In addition, the spatiotemporal correlation of traffic status between cameras is the constraint beyond the image, which can greatly improve the re-ID accuracy of different vehicles with similar appearances. The method proposed in this paper uses hypergraph optimization to learn about the similarity between the query image and images in the library. By using the pair and higher-order relationship between query objects and image library, the similarity measurement method is improved compared to direct matching. The experiments conducted on the image library constructed in this paper demonstrates the effectiveness of using multifeature hypergraph fusion and the spatiotemporal correlation model to address issues in vehicle reidentification.

APA, Harvard, Vancouver, ISO, and other styles

9

Guo, Lei, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. "Hierarchical Hyperedge Embedding-Based Representation Learning for Group Recommendation." ACM Transactions on Information Systems 40, no. 1 (January 31, 2022): 1–27. http://dx.doi.org/10.1145/3457949.

Full text

Abstract:

Group recommendation aims to recommend items to a group of users. In this work, we study group recommendation in a particular scenario, namely occasional group recommendation, where groups are formed ad hoc and users may just constitute a group for the first time—that is, the historical group-item interaction records are highly limited. Most state-of-the-art works have addressed the challenge by aggregating group members’ personal preferences to learn the group representation. However, the representation learning for a group is most complex beyond the aggregation or fusion of group member representation, as the personal preferences and group preferences may be in different spaces and even orthogonal. In addition, the learned user representation is not accurate due to the sparsity of users’ interaction data. Moreover, the group similarity in terms of common group members has been overlooked, which, however, has the great potential to improve the group representation learning. In this work, we focus on addressing the aforementioned challenges in the group representation learning task, and devise a hierarchical hyperedge embedding-based group recommender, namely HyperGroup. Specifically, we propose to leverage the user-user interactions to alleviate the sparsity issue of user-item interactions, and design a graph neural network-based representation learning network to enhance the learning of individuals’ preferences from their friends’ preferences, which provides a solid foundation for learning groups’ preferences. To exploit the group similarity (i.e., overlapping relationships among groups) to learn a more accurate group representation from highly limited group-item interactions, we connect all groups as a network of overlapping sets (a.k.a. hypergraph), and treat the task of group preference learning as embedding hyperedges (i.e., user sets/groups) in a hypergraph, where an inductive hyperedge embedding method is proposed. To further enhance the group-level preference modeling, we develop a joint training strategy to learn both user-item and group-item interactions in the same process. We conduct extensive experiments on two real-world datasets, and the experimental results demonstrate the superiority of our proposed HyperGroup in comparison to the state-of-the-art baselines.

APA, Harvard, Vancouver, ISO, and other styles

10

Xu, Jinhuan, Liang Xiao, and Jingxiang Yang. "Unified Low-Rank Subspace Clustering with Dynamic Hypergraph for Hyperspectral Image." Remote Sensing 13, no. 7 (April 2, 2021): 1372. http://dx.doi.org/10.3390/rs13071372.

Full text

Abstract:

Low-rank representation with hypergraph regularization has achieved great success in hyperspectral imagery, which can explore global structure, and further incorporate local information. Existing hypergraph learning methods only construct the hypergraph by a fixed similarity matrix or are adaptively optimal in original feature space; they do not update the hypergraph in subspace-dimensionality. In addition, the clustering performance obtained by the existing k-means-based clustering methods is unstable as the k-means method is sensitive to the initialization of the cluster centers. In order to address these issues, we propose a novel unified low-rank subspace clustering method with dynamic hypergraph for hyperspectral images (HSIs). In our method, the hypergraph is adaptively learned from the low-rank subspace feature, which can capture a more complex manifold structure effectively. In addition, we introduce a rotation matrix to simultaneously learn continuous and discrete clustering labels without any relaxing information loss. The unified model jointly learns the hypergraph and the discrete clustering labels, in which the subspace feature is adaptively learned by considering the optimal dynamic hypergraph with the self-taught property. The experimental results on real HSIs show that the proposed methods can achieve better performance compared to eight state-of-the-art clustering methods.

APA, Harvard, Vancouver, ISO, and other styles

11

Bai, Junjie, Biao Gong, Yining Zhao, Fuqiang Lei, Chenggang Yan, and Yue Gao. "Multi-Scale Representation Learning on Hypergraph for 3D Shape Retrieval and Recognition." IEEE Transactions on Image Processing 30 (2021): 5327–38. http://dx.doi.org/10.1109/tip.2021.3082765.

Full text

APA, Harvard, Vancouver, ISO, and other styles

12

Zhang, Ruochi, Tianming Zhou, and Jian Ma. "Multiscale and integrative single-cell Hi-C analysis with Higashi." Nature Biotechnology 40, no. 2 (October 11, 2021): 254–61. http://dx.doi.org/10.1038/s41587-021-01034-y.

Full text

Abstract:

AbstractSingle-cell Hi-C (scHi-C) can identify cell-to-cell variability of three-dimensional (3D) chromatin organization, but the sparseness of measured interactions poses an analysis challenge. Here we report Higashi, an algorithm based on hypergraph representation learning that can incorporate the latent correlations among single cells to enhance overall imputation of contact maps. Higashi outperforms existing methods for embedding and imputation of scHi-C data and is able to identify multiscale 3D genome features in single cells, such as compartmentalization and TAD-like domain boundaries, allowing refined delineation of their cell-to-cell variability. Moreover, Higashi can incorporate epigenomic signals jointly profiled in the same cell into the hypergraph representation learning framework, as compared to separate analysis of two modalities, leading to improved embeddings for single-nucleus methyl-3C data. In an scHi-C dataset from human prefrontal cortex, Higashi identifies connections between 3D genome features and cell-type-specific gene regulation. Higashi can also potentially be extended to analyze single-cell multiway chromatin interactions and other multimodal single-cell omics data.

APA, Harvard, Vancouver, ISO, and other styles

13

Hong, Chaoqun, and Jianke Zhu. "Hypergraph-based multi-example ranking with sparse representation for transductive learning image retrieval." Neurocomputing 101 (February 2013): 94–103. http://dx.doi.org/10.1016/j.neucom.2012.09.001.

Full text

APA, Harvard, Vancouver, ISO, and other styles

14

Dong, Naghedolfeizi, Aberra, and Zeng. "Spectral–Spatial Discriminant Feature Learning for Hyperspectral Image Classification." Remote Sensing 11, no. 13 (June 29, 2019): 1552. http://dx.doi.org/10.3390/rs11131552.

Full text

Abstract:

Sparse representation classification (SRC) is being widely applied to target detection in hyperspectral images (HSI). However, due to the problem in HSI that high-dimensional data contain redundant information, SRC methods may fail to achieve high classification performance, even with a large number of spectral bands. Selecting a subset of predictive features in a high-dimensional space is an important and challenging problem for hyperspectral image classification. In this paper, we propose a novel discriminant feature learning (DFL) method, which combines spectral and spatial information into a hypergraph Laplacian. First, a subset of discriminative features is selected, which preserve the spectral structure of data and the inter- and intra-class constraints on labeled training samples. A feature evaluator is obtained by semi-supervised learning with the hypergraph Laplacian. Secondly, the selected features are mapped into a further lower-dimensional eigenspace through a generalized eigendecomposition of the Laplacian matrix. The finally extracted discriminative features are used in a joint sparsity-model algorithm. Experiments conducted with benchmark data sets and different experimental settings show that our proposed method increases classification accuracy and outperforms the state-of-the-art HSI classification methods.

APA, Harvard, Vancouver, ISO, and other styles

15

Xu, You-Wei, Hong-Jun Zhang, Kai Cheng, Xiang-Lin Liao, Zi-Xuan Zhang, and Yun-Bo Li. "Knowledge graph embedding with entity attributes using hypergraph neural networks." Intelligent Data Analysis 26, no. 4 (July 11, 2022): 959–75. http://dx.doi.org/10.3233/ida-216007.

Full text

Abstract:

Knowledge graph embedding is aimed at capturing the semantic information of entities by modeling the structural information between entities. For long-tail entities which lack sufficient structural information, general knowledge graph embedding models often show relatively low performance in link prediction. In order to solve such problems, this paper proposes a general knowledge graph embedding framework to learn the structural information as well as the attribute information of the entities simultaneously. Under this framework, a H-AKRL (Hypergraph Neural Networks based Attribute-embodied Knowledge Representation Learning) model is put forward, where the hypergraph neural network is used to model the correlation between entities and attributes at a higher level. The complementary relationship between attribute information and structural information is taken full advantage of, enabling H-AKRL to finally achieve the goal of improving link prediction performance. Experiments on multiple real-world data sets show that the H-AKRL model has significantly improved the link prediction performance, especially in the embeddings of long tail entities.

APA, Harvard, Vancouver, ISO, and other styles

16

Ngo, Vuong M., Thuy-Van T. Duong, Tat-Bao-Thien Nguyen, Phuong T. Nguyen, and Owen Conlan. "An Efficient Classification Algorithm for Traditional Textile Patterns from Different Cultures Based on Structures." Journal on Computing and Cultural Heritage 14, no. 4 (December 31, 2021): 1–22. http://dx.doi.org/10.1145/3465381.

Full text

Abstract:

Textiles have an important role in many cultures and have been digitised. They are three-dimensional objects and have complex structures, especially archaeological fabric specimens and artifact textiles created manually by traditional craftsmen. In this article, we propose a novel algorithm for textile classification based on their structures. First, a hypergraph is used to represent the textile structure. Second, multisets of k -neighbourhoods are extracted from the hypergraph and converted to one feature vector for representation of each textile. Then, the k -neighbourhood vectors are classified using seven most popular supervised learning methods. Finally, we evaluate experimentally the different variants of our approach on a data set of 1,600 textile samples with the 4-fold cross-validation technique. The experimental results indicate that comparing the variants, the best classification accuracies are 0.999 with LR, 0.994 with LDA, 0.996 with KNN, 0.994 with CART, 0.998 with NB, 0.974 with SVM, and 0.999 with NNM.

APA, Harvard, Vancouver, ISO, and other styles

17

Wang, Xinlei, Junchang Xin, Zhongyang Wang, Chuangang Li, and Zhiqiong Wang. "An Evolving Hypergraph Convolutional Network for the Diagnosis of Alzheimer’s Disease." Diagnostics 12, no. 11 (October 30, 2022): 2632. http://dx.doi.org/10.3390/diagnostics12112632.

Full text

Abstract:

In the diagnosis of Alzheimer’s Disease (AD), the brain network analysis method is often used. The traditional network can only reflect the pairwise association between two brain regions, but ignore the higher-order relationship between them. Therefore, a brain network construction method based on hypergraph, called hyperbrain network, is adopted. The brain network constructed by the conventional static hyperbrain network cannot reflect the dynamic changes in brain activity. Based on this, the construction of a dynamic hyperbrain network is proposed. In addition, graph convolutional networks also play a huge role in AD diagnosis. Therefore, an evolving hypergraph convolutional network for the dynamic hyperbrain network is proposed, and the attention mechanism is added to further enhance the ability of representation learning, and then it is used for the aided diagnosis of AD. The experimental results show that the proposed method can effectively improve the accuracy of AD diagnosis up to 99.09%, which is a 0.3 percent improvement over the best existing methods.

APA, Harvard, Vancouver, ISO, and other styles

18

Liu, Qingshan, Yubao Sun, Renlong Hang, and Huihui Song. "Spatial–Spectral Locality-Constrained Low-Rank Representation with Semi-Supervised Hypergraph Learning for Hyperspectral Image Classification." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 10, no. 9 (September 2017): 4171–82. http://dx.doi.org/10.1109/jstars.2017.2700490.

Full text

APA, Harvard, Vancouver, ISO, and other styles

19

Wu, Hanrui, and Michael K. Ng. "Hypergraph Convolution on Nodes-Hyperedges Network for Semi-Supervised Node Classification." ACM Transactions on Knowledge Discovery from Data 16, no. 4 (August 31, 2022): 1–19. http://dx.doi.org/10.1145/3494567.

Full text

Abstract:

Hypergraphs have shown great power in representing high-order relations among entities, and lots of hypergraph-based deep learning methods have been proposed to learn informative data representations for the node classification problem. However, most of these deep learning approaches do not take full consideration of either the hyperedge information or the original relationships among nodes and hyperedges. In this article, we present a simple yet effective semi-supervised node classification method named Hypergraph Convolution on Nodes-Hyperedges network, which performs filtering on both nodes and hyperedges as well as recovers the original hypergraph with the least information loss. Instead of only reducing the cross-entropy loss over the labeled samples as most previous approaches do, we additionally consider the hypergraph reconstruction loss as prior information to improve prediction accuracy. As a result, by taking both the cross-entropy loss on the labeled samples and the hypergraph reconstruction loss into consideration, we are able to achieve discriminative latent data representations for training a classifier. We perform extensive experiments on the semi-supervised node classification problem and compare the proposed method with state-of-the-art algorithms. The promising results demonstrate the effectiveness of the proposed method.

APA, Harvard, Vancouver, ISO, and other styles

20

Xia, Xin, Hongzhi Yin, Junliang Yu, Qinyong Wang, Lizhen Cui, and Xiangliang Zhang. "Self-Supervised Hypergraph Convolutional Networks for Session-based Recommendation." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 5 (May 18, 2021): 4503–11. http://dx.doi.org/10.1609/aaai.v35i5.16578.

Full text

Abstract:

Session-based recommendation (SBR) focuses on next-item prediction at a certain time point. As user profiles are generally not available in this scenario, capturing the user intent lying in the item transitions plays a pivotal role. Recent graph neural networks (GNNs) based SBR methods regard the item transitions as pairwise relations, which neglect the complex high-order information among items. Hypergraph provides a natural way to capture beyond-pairwise relations, while its potential for SBR has remained unexplored. In this paper, we fill this gap by modeling session-based data as a hypergraph and then propose a dual channel hypergraph convolutional network -- DHCN to improve SBR. Moreover, to enhance hypergraph modeling, we innovatively integrate self-supervised learning into the training of our network by maximizing mutual information between the session representations learned via the two channels in DHCN, serving as an auxiliary task to improve the recommendation task. Extensive experiments on three benchmark datasets demonstrate the superiority of our model over the SOTA methods, and the ablation study validates the effectiveness and rationale of hypergraph modeling and self-supervised task. The implementation of our model is available via https://github.com/xiaxin1998/DHCN.

APA, Harvard, Vancouver, ISO, and other styles

21

Tran, Huu Ngoc Tran, J. Joshua Thomas, and Nurul Hashimah Ahamed Hassain Malim. "DeepNC: a framework for drug-target interaction prediction with graph neural networks." PeerJ 10 (May 11, 2022): e13163. http://dx.doi.org/10.7717/peerj.13163.

Full text

Abstract:

The exploration of drug-target interactions (DTI) is an essential stage in the drug development pipeline. Thanks to the assistance of computational models, notably in the deep learning approach, scientists have been able to shorten the time spent on this stage. Widely practiced deep learning algorithms such as convolutional neural networks and recurrent neural networks are commonly employed in DTI prediction projects. However, they can hardly utilize the natural graph structure of molecular inputs. For that reason, a graph neural network (GNN) is an applicable choice for learning the chemical and structural characteristics of molecules when it represents molecular compounds as graphs and learns the compound features from those graphs. In an effort to construct an advanced deep learning-based model for DTI prediction, we propose Deep Neural Computation (DeepNC), which is a framework utilizing three GNN algorithms: Generalized Aggregation Networks (GENConv), Graph Convolutional Networks (GCNConv), and Hypergraph Convolution-Hypergraph Attention (HypergraphConv). In short, our framework learns the features of drugs and targets by the layers of GNN and 1-D convolution network, respectively. Then, representations of the drugs and targets are fed into fully-connected layers to predict the binding affinity values. The models of DeepNC were evaluated on two benchmarked datasets (Davis, Kiba) and one independently proposed dataset (Allergy) to confirm that they are suitable for predicting the binding affinity of drugs and targets. Moreover, compared to the results of baseline methods that worked on the same problem, DeepNC proves to improve the performance in terms of mean square error and concordance index.

APA, Harvard, Vancouver, ISO, and other styles

22

Huang, Jiahao, Fangyuan Lei, Jianjian Jiang, Xi Zeng, Ruijun Ma, and Qingyun Dai. "Multi-order hypergraph convolutional networks integrated with self-supervised learning." Complex & Intelligent Systems, January 9, 2023. http://dx.doi.org/10.1007/s40747-022-00964-7.

Full text

Abstract:

AbstractHypergraphs, as a powerful representation of information, effectively and naturally depict complex and non-pair-wise relationships in the real world. Hypergraph representation learning is useful for exploring complex relationships implicit in hypergraphs. However, most methods focus on the 1-order neighborhoods and ignore the higher order neighborhood relationships among data on the hypergraph structure. These often result in underutilization of hypergraph structure. In this paper, we exploit the potential of higher order neighborhoods in hypergraphs for representation and propose a Multi-Order Hypergraph Convolutional Network Integrated with Self-supervised Learning. We first encode the multi-channel network of the hypergraph by a high-order spectral convolution operator that captures the multi-order representation of nodes. Then, we introduce an inter-order attention mechanism to preserve the low-order neighborhood information. Finally, to extract valid embedding in the higher order neighborhoods, we incorporate a self-supervised learning strategy based on maximizing mutual information in the multi-order hypergraph convolutional network. Experiments on several hypergraph datasets show that the proposed model is competitive with state-of-the-art baselines, and ablation studies show the effectiveness of higher order neighborhood development, the inter-order attention mechanism, and the self-supervised learning strategy.

APA, Harvard, Vancouver, ISO, and other styles

23

Liu, Xiang, Huitao Feng, Jie Wu, and Kelin Xia. "Persistent spectral hypergraph based machine learning (PSH-ML) for protein-ligand binding affinity prediction." Briefings in Bioinformatics 22, no. 5 (April 9, 2021). http://dx.doi.org/10.1093/bib/bbab127.

Full text

Abstract:

Abstract Molecular descriptors are essential to not only quantitative structure activity/property relationship (QSAR/QSPR) models, but also machine learning based chemical and biological data analysis. In this paper, we propose persistent spectral hypergraph (PSH) based molecular descriptors or fingerprints for the first time. Our PSH-based molecular descriptors are used in the characterization of molecular structures and interactions, and further combined with machine learning models, in particular gradient boosting tree (GBT), for protein-ligand binding affinity prediction. Different from traditional molecular descriptors, which are usually based on molecular graph models, a hypergraph-based topological representation is proposed for protein–ligand interaction characterization. Moreover, a filtration process is introduced to generate a series of nested hypergraphs in different scales. For each of these hypergraphs, its eigen spectrum information can be obtained from the corresponding (Hodge) Laplacain matrix. PSH studies the persistence and variation of the eigen spectrum of the nested hypergraphs during the filtration process. Molecular descriptors or fingerprints can be generated from persistent attributes, which are statistical or combinatorial functions of PSH, and combined with machine learning models, in particular, GBT. We test our PSH-GBT model on three most commonly used datasets, including PDBbind-2007, PDBbind-2013 and PDBbind-2016. Our results, for all these databases, are better than all existing machine learning models with traditional molecular descriptors, as far as we know.

APA, Harvard, Vancouver, ISO, and other styles

24

Liu, Xuan, Congzhi Song, Shichao Liu, Menglu Li, Xionghui Zhou, and Wen Zhang. "Multi-way relation-enhanced hypergraph representation learning for anti-cancer drug synergy prediction." Bioinformatics, August 24, 2022. http://dx.doi.org/10.1093/bioinformatics/btac579.

Full text

Abstract:

Abstract Motivation Drug combinations have exhibited promise in treating cancers with less toxicity and fewer adverse reactions. However, in vitro screening of synergistic drug combinations is time-consuming and labour-intensive because of the combinatorial explosion. Although a number of computational methods have been developed for predicting synergistic drug combinations, the multi-way relations between drug combinations and cell lines existing in drug synergy data have not been well exploited. Results We propose a multi-way relation-enhanced hypergraph representation learning method to predict anti-cancer drug synergy, named HypergraphSynergy. HypergraphSynergy formulates synergistic drug combinations over cancer cell lines as a hypergraph, in which drugs and cell lines are represented by nodes and synergistic drug-drug-cell line triplets are represented by hyperedges, and leverages the biochemical features of drugs and cell lines as node attributes. Then, a hypergraph neural network is designed to learn the embeddings of drugs and cell lines from the hypergraph and predict drug synergy. Moreover, the auxiliary task of reconstructing the similarity networks of drugs and cell lines is considered to enhance the generalization ability of the model. In the computational experiments, HypergraphSynergy outperforms other state-of-the-art synergy prediction methods on two benchmark datasets for both classification and regression tasks, and is applicable to unseen drug combinations or cell lines. The studies revealed that the hypergraph formulation allows us to capture and explain complex multi-way relations of drug combinations and cell lines, and also provides a flexible framework to make the best use of diverse information. Availability and implementation The source data and codes of HypergraphSynergy can be freely downloaded from https://github.com/liuxuan666/HypergraphSynergy. Supplementary information Supplementary data are available at Bioinformatics online.

APA, Harvard, Vancouver, ISO, and other styles

25

Li, Mengran, Yong Zhang, Xiaoyong Li, Yuchen Zhang, and Baocai Yin. "Hypergraph Transformer Neural Networks." ACM Transactions on Knowledge Discovery from Data, September 27, 2022. http://dx.doi.org/10.1145/3565028.

Full text

Abstract:

Graph neural networks (GNNs) have been widely used for graph structure learning and achieved excellent performance in tasks such as node classification and link prediction. Real-world graph networks imply complex and various semantic information and are often referred to as heterogeneous information networks (HINs). Previous GNNs have laboriously modeled heterogeneous graph networks with pairwise relations, in which the semantic information representation for learning is incomplete and severely hinders node embedded learning. Therefore, the conventional graph structure cannot satisfy the demand for information discovery in HINs. In this paper, we propose an end-to-end hypergraph transformer neural network (HGTN) that exploits the communication abilities between different types of nodes and hyperedges to learn higher-order relations and discover semantic information. Specifically, attention mechanisms weigh the importance of semantic information hidden in original HINs to generate useful meta-paths. Meanwhile, our method develops a multi-scale attention module to aggregate node embeddings in higher-order neighborhoods. We evaluate the proposed model with node classification tasks on six datasets: DBLP, ACM, IBDM, Reuters, STUD-BJUT and, Citeseer. Experiments on a large number of benchmarks show the advantages of HGTN.

APA, Harvard, Vancouver, ISO, and other styles

26

Li, Menghang, Min Qiu, Li Zhu, and Wanzeng Kong. "Feature hypergraph representation learning on spatial-temporal correlations for EEG emotion recognition." Cognitive Neurodynamics, October 10, 2022. http://dx.doi.org/10.1007/s11571-022-09890-3.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Heydari, Sajjad, Stefano Raniolo, Lorenzo Livi, and Vittorio Limongelli. "Transferring chemical and energetic knowledge between molecular systems with machine learning." Communications Chemistry 6, no. 1 (January 13, 2023). http://dx.doi.org/10.1038/s42004-022-00790-5.

Full text

Abstract:

AbstractPredicting structural and energetic properties of a molecular system is one of the fundamental tasks in molecular simulations, and it has applications in chemistry, biology, and medicine. In the past decade, the advent of machine learning algorithms had an impact on molecular simulations for various tasks, including property prediction of atomistic systems. In this paper, we propose a novel methodology for transferring knowledge obtained from simple molecular systems to a more complex one, endowed with a significantly larger number of atoms and degrees of freedom. In particular, we focus on the classification of high and low free-energy conformations. Our approach relies on utilizing (i) a novel hypergraph representation of molecules, encoding all relevant information for characterizing multi-atom interactions for a given conformation, and (ii) novel message passing and pooling layers for processing and making free-energy predictions on such hypergraph-structured data. Despite the complexity of the problem, our results show a remarkable Area Under the Curve of 0.92 for transfer learning from tri-alanine to the deca-alanine system. Moreover, we show that the same transfer learning approach can also be used in an unsupervised way to group chemically related secondary structures of deca-alanine in clusters having similar free-energy values. Our study represents a proof of concept that reliable transfer learning models for molecular systems can be designed, paving the way to unexplored routes in prediction of structural and energetic properties of biologically relevant systems.

APA, Harvard, Vancouver, ISO, and other styles

Journal articles on the topic 'Hypergraph Representation Learning'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles