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muhanzhang / SEAL

SEAL (learning from Subgraphs, Embeddings, and Attributes for Link prediction). "M. Zhang, Y. Chen, Link Prediction Based on Graph Neural Networks, NeurIPS 2018 spotlight".
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是否可以拓展到有向图的比较？ #62

Open WZPBelieveFuture opened 3 years ago

WZPBelieveFuture commented 3 years ago

张博士，您好！ 有个问题想向您请教下，SEAL方法能否拓展到有向网络的链路预测？我看了下您的代码，应该是可以的，但是一个节点是基于指向该节点的邻居来修正自身节点是否合适? 是否将SEAL方法拓展到有向图与其他有向的图卷积方法计较过，该方法是否更优？ 期待您的回复！ 感谢

muhanzhang commented 3 years ago

Hi! Yes. SEAL can be applied to directed graphs. When working on directed graphs, you should include both incoming and outgoing neighbors when extracting enclosing subgraphs.

I haven't compared with GNNs specifically for directed graphs. But you may check SEAL_OGB, which achieves state-of-the-art result on ogbl-citation2, a very large directed graph. Basically SEAL is a framework for link prediction where you can apply whatever GNN in it (including directed GNNs). You may check that implementation for how SEAL handles directed edges.

WZPBelieveFuture commented 3 years ago

Thank you very much!

At 2021-10-31 18:25:06, "Muhan Zhang" @.***> wrote:

Hi! Yes. SEAL can be applied to directed graphs. When working on directed graphs, you should include both incoming and outgoing neighbors when extracting enclosing subgraphs.

I haven't compared with GNNs specifically for directed graphs. But you may check SEAL_OGB, which achieves state-of-the-art result on ogbl-citation2, a very large directed graph. Basically SEAL is a framework for link prediction where you can apply whatever GNN in it (including directed GNNs). You may check that implementation for how SEAL handles directed edges.

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