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Temporal network embedding using graph attention network

COMPLEX & INTELLIGENT SYSTEMS 2022年第1期8卷 13-27页

作者： Mohan, Anuraj Pramod, K., V Cochin Univ Sci & Technol Dept Comp Applicat Artificial Intelligence Lab Cochin 682022 Kerala India Cochin Univ Sci & Technol Dept Comp Applicat Cochin 682022 Kerala India

graph convolutional network (GCN) has made remarkable progress in learning good representations from graph-structured data. The layer-wise propagation rule of conventional GCN is designed in such a way that the feature aggregation at each node depends on the features of the one-hop neighbouring nodes. Adding an attention layer over the GCN can allow the network to provide different importance within various one-hop neighbours. These methods can capture the properties of static network, but is not well suited to capture the temporal patterns in time-varying networks. In this work, we propose a temporal graph attention network (TempGAN), where the aim is to learn representations from continuous-time temporal network by preserving the temporal proximity between nodes of the network. First, we perform a temporal walk over the network to generate a positive pointwise mutual information matrix (PPMI) which denote the temporal correlation between the nodes. Furthermore, we design a TempGAN architecture which uses both adjacency and PPMI information to generate node embeddings from temporal network. Finally, we conduct link prediction experiments by designing a TempGAN autoencoder to evaluate the quality of the embedding generated, and the results are compared with other state-of-the-art methods.

关键词： Network embedding graph convolution graph autoencoder Temporal networks

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Fusion of multi-source relationships and topology to infer lncRNA-protein interactions

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MOLECULAR THERAPY NUCLEIC ACIDS 2024年第2期35卷 102187页

作者： Zhang, Xinyu Liu, Mingzhe Li, Zhen Zhuo, Linlin Fu, Xiangzheng Zou, Quan Wenzhou Univ Technol Sch Data Sci & Artificial Intelligence Wenzhou 325027 Peoples R China Guangzhou Univ Inst Computat Sci & Technol Guangzhou 510000 Peoples R China Hunan Univ Coll Comp Sci & Elect Engn Changsha 410012 Peoples R China Univ Elect Sci & Technol China Inst Fundamental & Frontier Sci Chengdu 611730 Peoples R China

Long non -coding RNAs (lncRNAs) are important factors involved in biological regulatory networks. Accurately predicting lncRNA-protein interactions (LPIs) is vital for clarifying lncRNA ' s functions and pathogenic mechanisms. Existing deep learning models have yet to yield satisfactory results in LPI prediction. Recently, graph autoencoders (GAEs) have seen rapid development, excelling in tasks like link prediction and node classi fi cation. We employed GAE technology for LPI prediction, devising the FMSRT-LPI model based on path masking and degree regression strategies and thereby achieving satisfactory outcomes. This represents the fi rst known integration of path masking and degree regression strategies into the GAE framework for potential LPI inference. The effectiveness of our FMSRT-LPI model primarily relies on four key aspects. First, within the GAE framework, our model integrates multi -source relationships of lncRNAs and proteins with LPN ' s topological data. Second, the implemented masking strategy ef fi ciently identi fi es LPN ' s key paths, reconstructs the network, and reduces the impact of redundant or incorrect data. Third, the integrated degree decoder balances degree and structural information, enhancing node representation. Fourth, the PolyLoss function we introduced is more appropriate for LPI prediction tasks. The results on multiple public datasets further demonstrate our model ' s potential in LPI prediction.

关键词： MT: Bioinformatics lncRNA-protein interactions LPI graph autoencoder GAE path masking strategy multi-source relationships degree decoder PolyLoss function

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MSLTE: multiple self-supervised learning tasks for enhancing EEG emotion recognition

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JOURNAL OF NEURAL ENGINEERING 2024年第2期21卷 024003-024003页

作者： Li, Guangqiang Chen, Ning Niu, Yixiang Xu, Zhangyong Dong, Yuxuan Jin, Jing Zhu, Hongqin East China Univ Sci & Technol Sch Informat Sci & Engn Shanghai 200237 Peoples R China

Objective. The instability of the EEG acquisition devices may lead to information loss in the channels or frequency bands of the collected EEG. This phenomenon may be ignored in available models, which leads to the overfitting and low generalization of the model. Approach. Multiple self-supervised learning tasks are introduced in the proposed model to enhance the generalization of EEG emotion recognition and reduce the overfitting problem to some extent. Firstly, channel masking and frequency masking are introduced to simulate the information loss in certain channels and frequency bands resulting from the instability of EEG, and two self-supervised learning-based feature reconstruction tasks combining masked graph autoencoders (GAE) are constructed to enhance the generalization of the shared encoder. Secondly, to take full advantage of the complementary information contained in these two self-supervised learning tasks to ensure the reliability of feature reconstruction, a weight sharing (WS) mechanism is introduced between the two graph decoders. Thirdly, an adaptive weight multi-task loss (AWML) strategy based on homoscedastic uncertainty is adopted to combine the supervised learning loss and the two self-supervised learning losses to enhance the performance further. Main results. Experimental results on SEED, SEED-V, and DEAP datasets demonstrate that: (i) Generally, the proposed model achieves higher averaged emotion classification accuracy than various baselines included in both subject-dependent and subject-independent scenarios. (ii) Each key module contributes to the performance enhancement of the proposed model. (iii) It achieves higher training efficiency, and significantly lower model size and computational complexity than the state-of-the-art (SOTA) multi-task-based model. (iv) The performances of the proposed model are less influenced by the key parameters. Significance. The introduction of the self-supervised learning task helps to enhance the generaliz

关键词： EEG emotion recognition mask-based self-supervised learning multi-task learning graph autoencoder weight sharing

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Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network

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PLANT METHODS 2024年第1期20卷 33页

作者： Chang, Liming Jin, Xiu Rao, Yuan Zhang, Xiaodan Anhui Agr Univ Coll Informat & Artificial Intelligence Hefei 230036 Peoples R China Anhui Agr Univ Anhui Prov Key Lab Smart Agr Technol & Equipment Hefei 230036 Peoples R China

BackgroundMore and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress *** this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold *** robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction. We propose an innovative approach based on multi-source similarity network fusion and graph autoencoder for predicting potential miRNA-abiotic stress *** pioneer the application

关键词： miRNA-abiotic stress association graph neural network Multi-source features graph autoencoder

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LEARNING AND GENERATING SPATIAL CONCEPTS OF MODERNIST ARCHITECTURE VIA graph MACHINE LEARNING 29th

LEARNING AND GENERATING SPATIAL CONCEPTS OF MODERNIST ARCHIT...

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29th International Conference of the Association-for-Computer-Aided-Architectural-Design-Research-in-Asia (CAADRIA)

作者： Bauscher, Erik Dai, Anni Elshani, Diellza Wortmann, Thomas Univ Stuttgart Chair Comp Architecture Inst Computat Design & Construct Stuttgart Germany

ISBN: (纸本)9789887891819

This project showcases a use case away from most other research in the field of generative AI in architecture. We present a workflow to generate new, three-dimensional spatial configurations of buildings by sampling the latent space of a graph autoencoder. graph representations of three-dimensional buildings can store more data and hence reduce the loss of information from building to machine learning model compared to image- and voxel-based representations. graphs do not only represent information about elements (nodes/pixels/etc.) but also the relationships between elements (edges). This is specifically helpful in architecture where we define space as an assemblage of physical elements which are all somehow connected (i.e., wall touches floor). Our method generates valuable, logical and original geometries that represent the architectural style chosen in the training data. These geometries are highly different from any image-based generation process and justify the importance of graph-based 3D geometry generation of architecture via machine learning. The method also introduces a novel conversion process from architecture to graph, an adapted decoder architecture, and a physical prototype to control the generation process, all making generative machine learning more applicable to a real-world scenario of designing a building.

关键词： generative 3D architecture generative graph machine learning graph-based architecture human-computer interaction graph autoencoder latentwalk

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Enhancing Heterophilic graph Neural Network Performance through Label Propagation in K-Nearest Neighbor graphs

Enhancing Heterophilic Graph Neural Network Performance thro...

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International Conference on Big Data and Smart Computing (BigComp)

作者： Park, Hyun Seok Park, Ha-Myung Kookmin Univ Seoul South Korea

ISBN: (纸本)9798350370027;9798350370034

How can we exploit Label Propagation (LP) to improve the performance of GNN models on heterophilic graphs? graph Neural Network (GNN) models have received a lot of attention as a powerful deep learning technology that uses graph structure and features, and has achieved an archived state-of-the-art performance for graph-related tasks. LP has been applied in various studies to improve performance of GNN models. However, LP does not perform well on heterophilic graphs, where nodes of different types are linked with each other, since LP assumes that the graphs inherently exhibits homophily, where similar nodes tend to be linked. Such heterophilic graphs are increasingly common nowadays. In this paper, we propose LPkG (Label Propagation on kNearest Neighbor graphs of graph autoencoder), a simple but effective method to engage LP to improve the performance of GNN models even on heterophilic graphs. LPkG constructs a supplementary homophilic graph, peforms LP on this graph, and uses the results together with the results of GNN models. The supplementary graph is a k-Nearest Neighbor (k-NN) graph genereated from a latent space computed by graph autoencoder (GAE). Experimental results demonstrate that LPkG consistently achieves performance improvement on various heterophilic graph datasets: 2.75% on the Wisconsin dataset, 2.23% on the Texas dataset, and 2.55% on the Cornell dataset.

关键词： Label Propagation graph Neural Network Heterophilic graph k-NN graph graph autoencoder

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Enhancing drug repurposing on graphs by integrating drug molecular structure as feature 36

Enhancing drug repurposing on graphs by integrating drug mol...

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36th IEEE International Symposium on Computer-Based Medical Systems (CBMS)

作者： Ayilso-Munoz, Adrian Prieto-Santamaria, Lucia Alverez-Perez, Andrea Otero-Carrasco, Beldn Serrano, Emilio Rodriguez-Gonzalez, Alejandro Univ Politecn Madrid ETS Ingenieros Informat Madrid Spain Univ Politecn Madrid Ctr Tecnol Biorned Madrid Spain

ISBN: (纸本)9798350312249

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

关键词： Drug repurposing graph deep learning Drug molecular structure graph Neural Networks graph autoencoder DISNET knowledge base

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GAE- ISUMM: Unsupervised graph-based Summarization for Indian Languages

GAE- ISUMM: Unsupervised Graph-based Summarization for India...

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International Joint Conference on Neural Networks (IJCNN)

作者： Vakada, Lakshmi Sireesha Ch, Anudeep Marreddy, Mounika Oota, Subba Reddy Mamidi, Radhika IIIT Hyderabad Hyderabad Telangana India Inria Bordeaux Bordeaux France

ISBN: (纸本)9781665488679

Document summarization aims to create a precise and coherent summary of a text document. Many deep learning summarization models are developed mainly for English, often requiring a large training corpus and efficient pre-trained language models and tools. However, English summarization models for low-resource Indian languages are often limited by rich morphological variation, syntax, and semantic differences. In this paper, we propose GAE-ISUMM, an unsupervised Indic summarization model that extracts summaries from text documents. In particular, our proposed model, GAE-ISUMM uses graph autoencoder (GAE) to learn text representations and a document summary jointly. We also provide a manually-annotated Telugu summarization dataset TELSUM, to experiment with our model GAE-ISUMM. Further, we benchmark with the most publicly available Indian language summarization datasets to investigate the effectiveness of GAE- ISUMM. Our experiments of GAE-ISUMM on seven Indian languages make the following observations: (i) it is competitive or better than state-of-the-art results on all datasets, (ii) it reports benchmark results on TELSUM, and (iii) the inclusion of positional and cluster information in the proposed model improved the performance of summaries. We open-source our dataset and code (1).

关键词： Document summarization graph autoencoder low-resource Indian languages

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MinerFinder: A GAE-LSTM method for predicting location of miners in underground 22

MinerFinder: A GAE-LSTM method for predicting location of mi...

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30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL GIS)

作者： Goyal, Abhay Madria, Sanjay Frimpong, Samuel Missouri S&T Dept CS Rolla MO 65409 USA

ISBN: (纸本)9781450395298

Recent reports by the Mine Safety and Health Administration suggest that several injuries and fatalities could be attributed to the inability to accurately locate miners in case of disasters. Since underground mines have a complicated geometrical landscape and technological constraints such as no GPS information available, it is difficult to predict the location of a miner and hence may cause delays and inefficiencies in rescue operations during a disaster. A significant amount of research has been done to capture complex spatio-temporal relationships of movement of the nodes/people/things with time, spatial and temporal features to separately extract these relationships for location prediction. Although Markov Chains (MC) and Recurrent Neural Network (RNN) based methods have been used to predict locations, not all of them specifically mention the spatial locations, their connections and the aggregation techniques which would allow for the actual representations of the trajectory of miners. Addressing these concerns, we develop a first-of-its-kind end-to-end system entitled MinerFinder to predict the future location of the miners by incorporating Long Short Term Memory (LSTM) for trajectory information with graph autoencoder (GAE) for spatial environmental information representing the node connectivity. In addition, our approach will combine the miners' previous trajectories and daily repetitive patterns enhancing the prediction robustness. We evaluated MinerFinder over synthetic dataset to analyze the structure and location topology of an underground mine compared with foreground locations. Our model outperforms state of the art models and achieves an AP score ranging from (0.62 - 0.68) and Receiver Operating Characteristics (ROC) ranging from (0.63-0.68) with increasing percentage of prominent locations (most visited) to 50%.

关键词： graph autoencoder LSTM Location Prediction underground mine

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An Improved graph Convolutional Neural Network based on graph Auto-encoder 16

An Improved Graph Convolutional Neural Network based on Grap...

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16th International Conference on Computer and Automation Engineering (ICCAE)

作者： Wang, Dongqi Du, Tianqi Liu, Zhongwu Chen, Dongming Ren, Tao Northeastern Univ Software Coll Shenyang Peoples R China

ISBN: (纸本)9798350370058;9798350370164

graph Convolutional Neural Networks (GCN) is a rapidly advancing deep learning algorithm for learning graph representations. One limitation of GCN is that it cannot guarantee optimal low-pass characteristics, thus struggling to effectively filter out high-frequency noises. In this study, we propose a GCN -based autoencoder that addresses this issue by incorporating Laplace-based filters for high-frequency noise reduction. To address potential overfitting caused by the mean-square error loss function used for reconstructing feature matrices, which is sensitive to the number of vector paradigms and dimensions, we introduce a cosine error loss function to mitigate this impact. Additionally, we employ a feature enhancement strategy during training. Through experiments conducted on three real-world datasets, we demonstrate that our proposed autoencoder clustering algorithm outperforms baseline graph representation learning algorithms in node clustering tasks. Furthermore, we assess the parameter sensitivity of our algorithm through extensive experiments.

关键词： Complex Network graph autoencoder graph Clustering graph Convolutional Neural Network

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