Results 31 to 40 of about 533,476 (267)
Scientific Paper Heterogeneous Graph Node Representation Learning Method Based onUnsupervised Clustering Level [PDF]
Jisuanji kexue, 2022 Knowledge representation of scientific paper data is a problem to be solved,and how to learn the representation of paper nodes in scientific paper heterogeneous network is the core to solve this problem.This paper proposes an unsupervised cluster-level ...
SONG Jie, LIANG Mei-yu, XUE Zhe, DU Jun-ping, KOU Fei-fei
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Temporal network embedding framework with causal anonymous walks representations [PDF]
PeerJ Computer Science, 2022 Many tasks in graph machine learning, such as link prediction and node classification, are typically solved using representation learning. Each node or edge in the network is encoded via an embedding.
Ilya Makarov +7 more
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FunQG: Molecular Representation Learning via Quotient Graphs
Journal of Chemical Information and Modeling, 2023 Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, and over-squashing.
Hossein Hajiabolhassan +3 more
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CAAI Transactions on Intelligence Technology, 2023 Contrastive self‐supervised representation learning on attributed graph networks with Graph Neural Networks has attracted considerable research interest recently. However, there are still two challenges.
Beibei Han +3 more
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Deep Inductive Graph Representation Learning [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2020 This paper presents a general inductive graph representation learning framework called $\text{DeepGL}$ DeepGL for learning deep node and edge features that generalize across-networks. In particular, $\text{DeepGL}$ DeepGL begins by deriving a set of base features from the graph (e.g., graphlet features) and automatically learns a ...
Ryan A. Rossi +2 more
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Heterogeneous Hypernetwork Representation Learning Based on Set Constraints [PDF]
Jisuanji gongcheng, 2023 Unlike ordinary networks, which only have pairwise relationships between the nodes, hypernetworks exhibit more intricate tuple relationships among their nodes.
Zhenguo LIU, Yu ZHU, Xiaoying WANG, Jianqiang HUANG, Tengfei CAO
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Knowledge Management & E-Learning: An International JournalSound waves are one of the important topics studied in physics. However, students’ graph representation is still low, leading to their low concept understanding of physics learning.
Pramudya Wahyu Pradana, Supahar
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Graph-Based Text Representation and Matching: A Review of the State of the Art and Future Challenges
IEEE Access, 2020 Graph-based text representation is one of the important preprocessing steps in data and text mining, Natural Language Processing (NLP), and information retrieval approaches. The graph-based methods focus on how to represent text documents in the shape of
Ahmed Hamza Osman, Omar Mohammed Barukub
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Frontiers in Genetics, 2021 It is a vital task to design an integrated machine learning model to discover cancer subtypes and understand the heterogeneity of cancer based on multiple omics data.
Jian Liu +7 more
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Unsupervised Graph Representation Learning With Variable Heat Kernel
IEEE Access, 2020 Graph representation learning aims to learn a low-dimension latent representation of nodes, and the learned representation is used for downstream graph analysis tasks. However, most of the existing graph embedding models focus on how to aggregate all the
Yongjun Jing +4 more
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