Performance of 8-layer GCN

Hi DropEdge Team,

I am running experiments on 8-layer GCN (using DropEdge) in the semi-supervised setting. I used the default hyper-parameters as 2-layer GCN and changed --nbaseblocklayer 0 to --nbaseblocklayer 6 in script/semi-supervised/cora_GCN.sh. On Cora dataset, the 2-layer GCN performance is 82.8% while 8-lay GCN is only 16%, not 75.80%. Could you please tell me how I can reproduce the results shown in readme?

Thank you.

[InfluenceFunction]

I have the same problem as you，It should be that the parameters of the multi-layer GCN are not given, and you cannot simply set --nbaseblocklayer 0 to --nbaseblocklayer 6 in script/semi-supervised/cora_GCN.sh

[klchai]

You can try to modify these parameters，such as weight_decay, sampling_percent and dropout. For multi-layers model with no parameters provided, I get good results by tuning these parameters，but there is a small gap compared with the results of the paper … ------------------ 原始邮件 ------------------ 发件人: “DropEdge/DropEdge” <[e-mail]>; 发送时间: 2020年12月8日(星期二) 晚上6:47 收件人: “DropEdge/DropEdge”<[e-mail]>; 抄送: “流觞曲水”<[e-mail]>;“Comment”<[e-mail]>; 主题: Re: [DropEdge/DropEdge] Performance of 8-layer GCN (#8) I also have the same problem. Have you guys fixed the problem? — You are receiving this because you commented. Reply to this email directly, view it on GitHub, or unsubscribe.

What are your parameters? When I try to reproduce the multi-layer GCN, the nhiddenlayer is fixed to 1 and I can only modify nbaseblocklayer. Thanks in advance.