<ul><li>Graph contrastive learning often suffers from false negatives, impacting downstream task performance.</li><li>A novel Negative Metric Learning (NML) enhanced GCL (NML-GCL) is proposed to address the false negative issue.</li><li>NML-GCL employs a learnable Negative Metric Network (NMN) to create a negative metric space for better distinction between false negatives and true negatives.</li><li>A joint training scheme with bi-level optimization objective is suggested to optimize the encoder and the negative metric network effectively, showing superior performance in experiments.</li></ul>

Negative Metric Learning for Graphs

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