%20environments.%20To%20handle%20the%20changes%20of%20graph%20structures%20after%20the%20IoT%20devices%20have%20been%20deployed%20in%20edge%20environments%2C%20in%20this%20paper%20we%20propose%20to%20combine%20an%20online%20sequential%20training%20algorithm%20with%20node2vec.%20The%20proposed%20sequentially-trainable%20model%20is%20implemented%20on%20an%20FPGA%20(Field-Programmable%20Gate%20Array)%20device%20to%20demonstrate%20the%20benefits%20of%20our%20approach.%20The%20proposed%20FPGA%20implementation%20achieves%20up%20to%20205.25%20times%20speedup%20compared%20to%20the%20original%20model%20on%20ARM%20Cortex-A53%20CPU.%20Evaluation%20results%20using%20dynamic%20graphs%20show%20that%20although%20the%20accuracy%20is%20decreased%20in%20the%20original%20model%2C%20the%20proposed%20sequential%20model%20can%20obtain%20better%20graph%20embedding%20that%20achieves%20a%20higher%20accuracy%20even%20when%20the%20graph%20structure%20is%20changed.)
![[2208.13699] Graph Exploration with Embedding-Guided Layouts](https://ar5iv.labs.arxiv.org/html/2208.13699/assets/figures/pipeline/pipeline.png)
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