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Multimodal weighted graph representation for information extraction from visually rich documents
This paper introduces a novel system for information extraction from visually rich documents
(VRD) using a weighted graph representation. The proposed method aims to improve the …
(VRD) using a weighted graph representation. The proposed method aims to improve the …
Parameter-agnostic deep graph clustering
Deep graph clustering, efficiently dividing nodes into multiple disjoint clusters in an
unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data …
unsupervised manner, has become a crucial tool for analyzing ubiquitous graph data …
Multi-view contrastive clustering for cancer subty** using fully and weakly paired multi-omics data
Y Kuang, M **e, Z Zhao, D Deng, E Bao - Methods, 2024 - Elsevier
The identification of cancer subtypes is crucial for advancing precision medicine, as it
facilitates the development of more effective and personalized treatment and prevention …
facilitates the development of more effective and personalized treatment and prevention …
Structure-enhanced Contrastive Learning for Graph Clustering
Graph clustering is a crucial task in network analysis with widespread applications, focusing
on partitioning nodes into distinct groups with stronger intra-group connections than inter …
on partitioning nodes into distinct groups with stronger intra-group connections than inter …
困难样本采样联合对比增**的深度图聚类.
朱玄烨， 孔兵， 陈红梅， 包崇明… - Application Research of …, 2024 - search.ebscohost.com
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Automated Multi-scale Contrastive Learning with Sample-Awareness for Graph Classification
Y Li, J Kang, X Li, C Jia, B Zu - Asia-Pacific Web (APWeb) and Web-Age …, 2024 - Springer
Proper sample selection can better facilitate mutual information learning. Current sample
selection methods suffer from fragile circularity, dependence on labeling information, and an …
selection methods suffer from fragile circularity, dependence on labeling information, and an …
Structure and Semantic Contrastive Learning for Nodes Clustering in Heterogeneous Information Networks
Y Yu, L Zhou, C Liu, L Wang, H Chen - International Conference on Spatial …, 2024 - Springer
Nodes clustering is an important approach to partition heterogeneous information networks
based on the features and adjacent matrices from different metapaths. Some scholars have …
based on the features and adjacent matrices from different metapaths. Some scholars have …
Information Science and Engineering, Yunnan University, Kunming 650091, China 1hzhou@ ynu. edu. cn
Y Yu, L Zhou, C Liu, L Wang… - Spatial Data and …, 2024 - books.google.com
Nodes clustering is an important approach to partition heterogeneous information networks
based on the features and adjacent matrices from different metapaths. Some scholars have …
based on the features and adjacent matrices from different metapaths. Some scholars have …