Web: http://arxiv.org/abs/2206.07253

June 16, 2022, 1:12 a.m. | Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao, Jiawei Han, Lingfei Wu

cs.CL updates on arXiv.org arxiv.org

Graph Neural Networks (GNNs) have gained great popularity in tackling various
analytical tasks on graph-structured data (i.e., networks). Typical GNNs and
their variants follow a message-passing manner that obtains network
representations by the feature propagation process along network topology,
which however ignore the rich textual semantics (e.g., local word-sequence)
that exist in many real-world networks. Existing methods for text-rich networks
integrate textual semantics by mainly utilizing internal information such as
topics or phrases/words, which often suffer from an inability to …

arxiv graph graph neural networks knowledge networks neural neural networks text

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