<ul><li>Accurate predictions in graph deep learning rely on positional encoding mechanisms like graph neural networks and graph transformers.</li><li>Limitations of current positional encodings include predefined functions, limited adaptability to complex graphs, and focus on structural information rather than real-world temporal evolution.</li><li>Researchers have developed Learnable Spatial-Temporal Positional Encoding (L-STEP) and a temporal link prediction model named L-STEP to address these limitations.</li><li>L-STEP demonstrates superior performance on various datasets and benchmarks, proving its effectiveness in temporal link prediction tasks.</li></ul>

Learnable Spatial-Temporal Positional Encoding for Link Prediction

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