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pro vyhledávání: '"Li, Jintang"'
Autor:
Liu, Yunfei, Li, Jintang, Chen, Yuehe, Wu, Ruofan, Wang, Ericbk, Zhou, Jing, Tian, Sheng, Shen, Shuheng, Fu, Xing, Meng, Changhua, Wang, Weiqiang, Chen, Liang
Graph clustering, a fundamental and challenging task in graph mining, aims to classify nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning (GCL) has emerged as a dominant line of research in graph clustering a
Externí odkaz:
http://arxiv.org/abs/2406.14288
Recent studies have highlighted fairness issues in Graph Neural Networks (GNNs), where they produce discriminatory predictions against specific protected groups categorized by sensitive attributes such as race and age. While various efforts to enhanc
Externí odkaz:
http://arxiv.org/abs/2406.13544
Over the past few years, research on deep graph learning has shifted from static graphs to temporal graphs in response to real-world complex systems that exhibit dynamic behaviors. In practice, temporal graphs are formalized as an ordered sequence of
Externí odkaz:
http://arxiv.org/abs/2406.00943
Group fairness for Graph Neural Networks (GNNs), which emphasizes algorithmic decisions neither favoring nor harming certain groups defined by sensitive attributes (e.g., race and gender), has gained considerable attention. In particular, the objecti
Externí odkaz:
http://arxiv.org/abs/2405.07011
Graph Transformers (GTs) with powerful representation learning ability make a huge success in wide range of graph tasks. However, the costs behind outstanding performances of GTs are higher energy consumption and computational overhead. The complex s
Externí odkaz:
http://arxiv.org/abs/2403.17656
Graph contrastive learning (GCL) has emerged as a representative paradigm in graph self-supervised learning, where negative samples are commonly regarded as the key to preventing model collapse and producing distinguishable representations. Recent st
Externí odkaz:
http://arxiv.org/abs/2312.02619
Autor:
Li, Jintang, Wei, Zheng, Dan, Jiawang, Zhou, Jing, Zhu, Yuchang, Wu, Ruofan, Wang, Baokun, Zhen, Zhang, Meng, Changhua, Jin, Hong, Zheng, Zibin, Chen, Liang
Real-world graphs are typically complex, exhibiting heterogeneity in the global structure, as well as strong heterophily within local neighborhoods. While a growing body of literature has revealed the limitations of common graph neural networks (GNNs
Externí odkaz:
http://arxiv.org/abs/2310.11664
Graph Neural Networks (GNNs) have achieved state-of-the-art performance in representation learning for graphs recently. However, the effectiveness of GNNs, which capitalize on the key operation of message propagation, highly depends on the quality of
Externí odkaz:
http://arxiv.org/abs/2308.06801
Oversmoothing is a common phenomenon observed in graph neural networks (GNNs), in which an increase in the network depth leads to a deterioration in their performance. Graph contrastive learning (GCL) is emerging as a promising way of leveraging vast
Externí odkaz:
http://arxiv.org/abs/2306.02117
Autor:
Li, Jintang, Zhang, Huizhe, Wu, Ruofan, Zhu, Zulun, Wang, Baokun, Meng, Changhua, Zheng, Zibin, Chen, Liang
While contrastive self-supervised learning has become the de-facto learning paradigm for graph neural networks, the pursuit of higher task accuracy requires a larger hidden dimensionality to learn informative and discriminative full-precision represe
Externí odkaz:
http://arxiv.org/abs/2305.19306