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czjdsg/LMBot - GitHub
Official implementation of LMBot: Distilling Graph Knowledge into Language Model for Graph-less Deployment in Twitter Bot Detection. Run following command to create environment for reproduction (for cuda 10.2): For pyg_lib, torch_cluster, torch_scatter, torch_sparse and torch_spline_conv, please download here and install locally.
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LMBot: Distilling Graph Knowledge into Language Model for …
Jun 30, 2023 · Inspired by this finding, we propose a novel bot detection framework LMBot that distills the knowledge of graph neural networks (GNNs) into language models (LMs) for graph-less deployment in Twitter bot detection to combat the challenge of data dependency.
Zhaoxuan Tan
We propose LMBot, which utilizes a language model with graph-aware knowledge distillation to act as a proxy for graph-less Twitter bot detection inference. This approach effectively resolves graph data dependency and sampling bias issues.
Inspired by this finding, we propose a novel bot detection framework LMBot1that distills the graph knowledge into language models (LMs) for graph-less deployment in Twitter bot detection to combat data dependency challenge. Moreover, LMBot is compati- ble with graph-based and graph-less datasets.
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LMBot: Distilling Graph Knowledge into Language Model for …
Inspired by this finding, we propose a novel bot detection framework LMBot that distills the graph knowledge into language models (LMs) for graph-less deployment in Twitter bot detection to combat data dependency challenge. Moreover, LMBot is …