Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Dong, Kaiwen"'
Autor:
Le, Khiem, Guo, Zhichun, Dong, Kaiwen, Huang, Xiaobao, Nan, Bozhao, Iyer, Roshni, Zhang, Xiangliang, Wiest, Olaf, Wang, Wei, Chawla, Nitesh V.
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding. However, their proficiency within the chemistry domain remains res
Externí odkaz:
http://arxiv.org/abs/2406.06777
Link prediction (LP) is a fundamental task in graph representation learning, with numerous applications in diverse domains. However, the generalizability of LP models is often compromised due to the presence of noisy or spurious information in graphs
Externí odkaz:
http://arxiv.org/abs/2404.11032
Graph structured data, specifically text-attributed graphs (TAG), effectively represent relationships among varied entities. Such graphs are essential for semi-supervised node classification tasks. Graph Neural Networks (GNNs) have emerged as a power
Externí odkaz:
http://arxiv.org/abs/2404.11019
Autor:
Guo, Zhichun, Zhao, Tong, Liu, Yozen, Dong, Kaiwen, Shiao, William, Shah, Neil, Chawla, Nitesh V.
Graph Neural Networks (GNNs) are prominent in graph machine learning and have shown state-of-the-art performance in Link Prediction (LP) tasks. Nonetheless, recent studies show that GNNs struggle to produce good results on low-degree nodes despite th
Externí odkaz:
http://arxiv.org/abs/2402.09711
Link prediction is a crucial task in graph machine learning, where the goal is to infer missing or future links within a graph. Traditional approaches leverage heuristic methods based on widely observed connectivity patterns, offering broad applicabi
Externí odkaz:
http://arxiv.org/abs/2402.07738
Message Passing Neural Networks (MPNNs) have emerged as the {\em de facto} standard in graph representation learning. However, when it comes to link prediction, they often struggle, surpassed by simple heuristics such as Common Neighbor (CN). This di
Externí odkaz:
http://arxiv.org/abs/2309.00976
Link prediction is a crucial problem in graph-structured data. Due to the recent success of graph neural networks (GNNs), a variety of GNN-based models were proposed to tackle the link prediction task. Specifically, GNNs leverage the message passing
Externí odkaz:
http://arxiv.org/abs/2211.15899
Generative self-supervised learning (SSL), especially masked autoencoders, has become one of the most exciting learning paradigms and has shown great potential in handling graph data. However, real-world graphs are always heterogeneous, which poses t
Externí odkaz:
http://arxiv.org/abs/2208.09957
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