Datasets:

WeiChow
/

DyGraphs

@@ -18,7 +18,7 @@ The dataset is dynamic graphs for paper [CrossLink](https://arxiv.org/pdf/2402.0
 CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions.
 It employs a technique called *conditioned link generation*, which integrates both evolution and structure modeling to perform evolution-specific link prediction. This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference. CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges. Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction.  Compared to advanced baselines under the same settings, CrossLink shows an average improvement of **11.40%** in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.
-![Architecture](./assets/model.png)
 ## Format

 CrossLink learns the evolution pattern of a specific downstream graph and subsequently makes pattern-specific link predictions.
 It employs a technique called *conditioned link generation*, which integrates both evolution and structure modeling to perform evolution-specific link prediction. This conditioned link generation is carried out by a transformer-decoder architecture, enabling efficient parallel training and inference. CrossLink is trained on extensive dynamic graphs across diverse domains, encompassing 6 million dynamic edges. Extensive experiments on eight untrained graphs demonstrate that CrossLink achieves state-of-the-art performance in cross-domain link prediction.  Compared to advanced baselines under the same settings, CrossLink shows an average improvement of **11.40%** in Average Precision across eight graphs. Impressively, it surpasses the fully supervised performance of 8 advanced baselines on 6 untrained graphs.
+![Architecture](model.png)
 ## Format