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: Efficient Graph Generation with Graph Recurrent Attention Networks by Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hope, Raquel Urtasun, and Richard Zemel [1, 3].

: LiaoRL/GRAN — This is the primary source for the implementation and links to data [1, 2]. Paper Link : Read on arXiv [3, 4]. Download File GhAgain.rar

: The authors proposed GRAN , a model that generates graphs one block of nodes at a time using a Recurrent Neural Network (RNN) and an attention mechanism to capture long-range dependencies in graph structures [4, 6]. : Efficient Graph Generation with Graph Recurrent Attention

: This specific archive typically contains the pre-processed datasets (like Protein, Reddit, or Grid graphs) or pre-trained model checkpoints needed to replicate the results mentioned in the paper [2, 5]. Where to Find the Official Resources Paper Link : Read on arXiv [3, 4]