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PeopleCompetition

我们的代码分为了几个步骤，首先是数据的预处理，放在了dataProcess文件夹，运行generate_adj.py文件可以产生接下来需要的people.mat文件 GNNs程序是社区划分的主程序，在这是我们输出并保存了划分结果，我们采用了GNNs作为模型，由于GNNs有较好的适应性，对于社区的划分是基于设定最大社区数量的软划分（不会强制产生最大数量的社区，在设置最大社区数量为2080时，GNNs的划分结果是产生了实际数量为32的社区划分） 98551b73d03395852b1e9c7f3da099c6 为此我们采用了leiden算法来寻找一个合适的最大社区数量，GNNs.py中nums_initials_GNNs_configs 是重复寻最优解的次数 89850bd07011faafb96c08918453c176 为了避免随机性的影响我们设置为2500轮，这可能需要较长的运行时间。 385ffe4802e0563aef957a9b8733bf7c 划分结果将会保存在submission文件夹中，是以最大社区数量-nums_initials_GNNs_configs-num_runs为命名的npy文件，我们在output_to_json.py文件中对该文件进行了转化，将其转化为符合提交格式的submission.json check_community.py是格式校验文件，可以在提交前进行格式检测

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