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Graph Contrastive Learning Automated
, 2021 Self-supervised learning on graph-structured data has drawn recent interest for learning generalizable, transferable and robust representations from unlabeled graphs. Among many, graph contrastive learning (GraphCL) has emerged with promising representation learning performance.
You, Yuning +3 more
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Contrastive learning and neural oscillations [PDF]
, 1991 The concept of Contrastive Learning (CL) is developed as a family of possible learning algorithms for neural networks. CL is an extension of Deterministic Boltzmann Machines to more general dynamical systems.
Baldi, Pierre, Pineda, Fernando
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IEEE Access, 2023 Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task.
Sanghun Kim, Hyeryung Jang
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Subgraph Adaptive Structure-Aware Graph Contrastive Learning
Mathematics, 2022 Graph contrastive learning (GCL) has been subject to more attention and been widely applied to numerous graph learning tasks such as node classification and link prediction. Although it has achieved great success and even performed better than supervised
Zhikui Chen +4 more
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Multi-view Graph Clustering Algorithm Based on Dual Contrastive Learning and Hard Sample Mining [PDF]
Jisuanji gongchengAs a key research direction in the field of graph mining, graph clustering aims to discover substructures or node groups with similarities from graph data and classify them into the same cluster.
QIAN Lifeng, LI Jing, ZOU Xuxi, CHEN Yu, GU Yalin, WEI Xunhu
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Learning Robust Representation Through Graph Adversarial Contrastive Learning
, 2022 Existing studies show that node representations generated by graph neural networks (GNNs) are vulnerable to adversarial attacks, such as unnoticeable perturbations of adjacent matrix and node features. Thus, it is requisite to learn robust representations in graph neural networks. To improve the robustness of graph representation learning, we propose a
Guo, Jiayan +3 more
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Better Document-level Sentiment Analysis from RST Discourse Parsing [PDF]
, 2015 Discourse structure is the hidden link between surface features and document-level properties, such as sentiment polarity. We show that the discourse analyses produced by Rhetorical Structure Theory (RST) parsers can improve document-level sentiment ...
Bhatia, Parminder +2 more
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GCL-ALG: graph contrastive learning with adaptive learnable view generators [PDF]
PeerJ Computer ScienceData augmentation is a pivotal part of graph contrastive learning, which can mine implicit graph data information to improve the quality of representation learning.
Yafang Li +3 more
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Graph Clustering with High-Order Contrastive Learning
Entropy, 2023 Graph clustering is a fundamental and challenging task in unsupervised learning. It has achieved great progress due to contrastive learning. However, we find that there are two problems that need to be addressed: (1) The augmentations in most graph ...
Wang Li, En Zhu, Siwei Wang, Xifeng Guo
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Words are Malleable: Computing Semantic Shifts in Political and Media Discourse [PDF]
, 2017 Recently, researchers started to pay attention to the detection of temporal shifts in the meaning of words. However, most (if not all) of these approaches restricted their efforts to uncovering change over time, thus neglecting other valuable dimensions ...
Gallie W. B. +7 more
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