UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure | Paper Citation Record

Observation 05a83cfe-bb46-4c04-9532-02d179eaf11f · outbound

This paper cites Biases in electronic health record data due to processes within the healthcare system: retrospec- tive observational study.Bmj, 361,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Biases in electronic health record data due to processes within the healthcare system: retrospec- tive observational study.Bmj, 361,

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This paper cites an unresolved cited work.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Unresolved cited work

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This paper cites Graph neural networks are dynamic program- mers.ArXiv,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph neural networks are dynamic program- mers.ArXiv,

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This paper cites A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges

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This paper cites Inductive representation learning on large graphs.Advances in neural information processing sys- tems, 30,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Inductive representation learning on large graphs.Advances in neural information processing sys- tems, 30,

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This paper cites Evaluating build- ing performance in healthcare facilities using entropy and graph heuristic theories.Scientific Reports, 12(1):8973,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Evaluating build- ing performance in healthcare facilities using entropy and graph heuristic theories.Scientific Reports, 12(1):8973,

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This paper cites Approximation capabilities of multilayer feedforward networks.Neural networks, 4(2):251–257,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Approximation capabilities of multilayer feedforward networks.Neural networks, 4(2):251–257,

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This paper cites A review of content-based and context-based recommendation systems.International Journal of Emerging Technologies in Learning (iJET), 16(3):274–306,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A review of content-based and context-based recommendation systems.International Journal of Emerging Technologies in Learning (iJET), 16(3):274–306,

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This paper cites Semi-Supervised Classification with Graph Convolutional Networks.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Semi-Supervised Classification with Graph Convolutional Networks

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This paper cites Ontological user profiling in recommender systems.ACM Transactions on Information Systems (TOIS), 22(1):54–88,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Ontological user profiling in recommender systems.ACM Transactions on Information Systems (TOIS), 22(1):54–88,

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This paper cites Sentence-bert: Sentence embeddings using siamese bert-networks.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Sentence-bert: Sentence embeddings using siamese bert-networks

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Observation f3b551f5-c6b5-45f0-8850-3c6029ede553 · outbound

This paper cites Temporal Graph Networks for Deep Learning on Dynamic Graphs.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Temporal Graph Networks for Deep Learning on Dynamic Graphs

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Observation 21e59ffc-f1e8-45b7-b4f4-b118f2072882 · outbound

This paper cites Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Freedyg: Frequency enhanced continuous-time dynamic graph model for link prediction

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Observation d25a084d-840f-4dfc-9daa-33826cb98c3d · outbound

This paper cites Graph Attention Networks.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph Attention Networks

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Observation 276a50eb-0ee7-4f17-8ead-aa2447b6f14b · outbound

This paper cites Review on mining data from multiple data sources.Pattern Recognition Letters, 109:120–128,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Review on mining data from multiple data sources.Pattern Recognition Letters, 109:120–128,

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This paper cites Graph Learning based Recommender Systems: A Review.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph Learning based Recommender Systems: A Review

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This paper cites A com- prehensive survey on graph neural networks.IEEE trans- actions on neural networks and learning systems, 32(1):4– 24,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A com- prehensive survey on graph neural networks.IEEE trans- actions on neural networks and learning systems, 32(1):4– 24,

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Observation 0d9a39fe-76c3-49ab-9a32-87c5cbb55d13 · outbound

This paper cites A survey of data representation for multi- modality event detection and evolution.Applied Sciences, 12(4):2204,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A survey of data representation for multi- modality event detection and evolution.Applied Sciences, 12(4):2204,

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This paper cites Inductive Representation Learning on Temporal Graphs.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Inductive Representation Learning on Temporal Graphs

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This paper cites How neural networks extrapolate: From feedforward to graph neural networks.ICLR,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure How neural networks extrapolate: From feedforward to graph neural networks.ICLR,

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This paper cites Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph Neural Networks are Inherently Good Generalizers: Insights by Bridging GNNs and MLPs

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This paper cites How graph neural networks learn: Lessons from training dynamics in function space.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure How graph neural networks learn: Lessons from training dynamics in function space

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This paper cites Dynamic graph representation learning with neural networks: A survey.Ieee Access, 12:43460–43484,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Dynamic graph representation learning with neural networks: A survey.Ieee Access, 12:43460–43484,

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This paper cites Towards better dynamic graph learning: New architec- ture and unified library.Advances in Neural Information Processing Systems, 36:67686–67700,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Towards better dynamic graph learning: New architec- ture and unified library.Advances in Neural Information Processing Systems, 36:67686–67700,

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This paper cites Financial knowledge graph based financial report query system.IEEE Access, 9:69766– 69782,.

UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Financial knowledge graph based financial report query system.IEEE Access, 9:69766– 69782,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A dy- namic attributes-driven graph attention network modeling on behavioral finance for stock prediction.ACM Trans- actions on Knowledge Discovery from Data, 18(1):1–29,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph neural networks: A review of methods and applications.AI open, 1:57–81,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Numerics of gram-schmidt or- thogonalization.Linear Algebra and Its Applications, 197:297–316,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Socialrec: User activ- ity based post weighted dynamic personalized post recom- mendation system in social media

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure RelGNN: Composite Message Passing for Relational Deep Learning

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Classifier ensembles: Select real-world applications.In- formation fusion, 9(1):4–20,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Asset trees and asset graphs in financial markets

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure A survey on graph database man- agement techniques for huge unstructured data.Interna- tional Journal of Electrical and Computer Engineering, 8(2):1140,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Foundations and modeling of dy- namic networks using dynamic graph neural networks: A survey.iEEE Access, 9:79143–79168,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Making a graph database from unstruc- tured text

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Lightgbm: A highly efficient gradient boost- ing decision tree.Advances in neural information process- ing systems, 30,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Patient access to medical records and healthcare outcomes: a systematic review.Jour- nal of the American Medical Informatics Association, 21(4):737–741,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Gram-schmidt orthogonalization: 100 years and more.Numerical Linear Algebra with Applications, 20(3):492–532,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Solving linear least squares problems by gram-schmidt orthogonalization.BIT Numer- ical Mathematics, 7(1):1–21,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Graph- based semantic web service composition for healthcare data integration.Journal of healthcare engineering, 2017(1):4271273,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Relbench: A benchmark for deep learning on relational databases.Advances in Neural Information Processing Systems, 37:21330–21341,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Transaction logs.Applications of social research methods to questions in information and library science, pages 166–177,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Towards structured log analysis

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Exiting the risk as- sessment maze: A meta-survey.ACM Computing Surveys (CSUR), 51(1):1–30,

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Relational Deep Learning: Graph Representation Learning on Relational Databases

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UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

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