git地址:https://github.com/koukoulala/SN_essay.git 都是用DNN做的,迭代2000次 1.facebook数据集: 只看直接相邻的邻居数目的表:18列特征,18个类别,准确度0.98 只有第2种邻居结构的表:18列特征,18个类别,准确度0.90 只有第3种邻居结构的表:18列特征,18个类别,准确度0.89 3种邻居结构的表(第一列是全部邻居数目):54列特征,18个类别,准确度0.972 2.bcspwr数据集: 只看直接相邻的邻居数目的表:8列特征,8个类别,准确度0.8377 只有第2种邻居结构的表:8列特征,8个类别,准确度0.6396 只有第3种邻居结构的表:8列特征,8个类别,准确度0.2415 3种邻居结构的表(第一列是全部邻居数目):24列特征,8个类别,准确度0.8415 指定3种结构的影响度,直接测试准确率:都设置为1,准确度0.824;设置为(1,0,0)即只有直接相邻节点起作用的话,准确度0.821 使用cnn,把每个节点邻居结构看做是图,并且数目都10(为了防止梯度消失),测试集准确率0.8359,训练集最高0.86 使用cnn,把每个节点邻居结构看做是图,并且数目都10(为了防止梯度消失),第一列不是全部邻居,测试集准确率0.8613 改为无向图,第 13 次迭代时达到最大准确率为: 0.9921875,最大准确率时的权重值: 0.39824164 0.6255868 0.68378544 测试准确率为: 0.990188679245283,错误的总数目: 52 3.fpga数据集: 只看直接相邻的邻居数目的表:16列特征,16个类别,准确度0.9918 3种邻居结构的表(第一列是全部邻居数目):48列特征,16个类别,准确度0.9918
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