Multidimensional scholarly citations: Characterizing and understanding scholars' citation behaviors
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the Association for Information Science and Technology"
DOI: 10.1002/asi.24709
Abstract: This study investigates scholars' citation behaviors from a fine‐grained perspective. Specifically, each scholarly citation is considered multidimensional rather than logically unidimensional (i.e., present or absent). Thirty million articles from PubMed were accessed for use in… read more here.
Keywords: citation behaviors; citation decision; scholarly citations; citation ... See more keywords
Characterizing and Understanding HGNNs on GPUs
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DOI: 10.1109/lca.2022.3198281
Abstract: Heterogeneous graph neural networks (HGNNs) deliver powerful capacity in heterogeneous graph representation learning. The execution of HGNNs is usually accelerated by GPUs. Therefore, characterizing and understanding the execution pattern of HGNNs on GPUs is important… read more here.
Keywords: execution hgnns; execution; hgnns; hgnns gpus ... See more keywords
Characterizing and Understanding End-to-End Multi-Modal Neural Networks on GPUs
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DOI: 10.1109/lca.2022.3215718
Abstract: Multi-modal neural networks have become increasingly pervasive in many machine learning application domains due to their superior accuracy by fusing various modalities. However, they present many unique characteristics such as multi-stage execution, frequent synchronization and… read more here.
Keywords: neural networks; modal neural; multi modal; understanding end ... See more keywords
Characterizing and Understanding Distributed GNN Training on GPUs
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DOI: 10.48550/arxiv.2204.08150
Abstract: Graph neural network (GNN) has been demonstrated to be a powerful model in many domains for its effectiveness in learning over graphs. To scale GNN training for large graphs, a widely adopted approach is distributed… read more here.
Keywords: distributed gnn; training; gnn training; training gpus ... See more keywords