Nowadays, some e-advice websites and social media like e-commerce businesses, provide not only their goods but a new way that their customers can give their opinions about products. Meanwhile, there are some review spammers who try to promote or demote some specific products by writing fraud reviews. There have been several types of researches and studies toward detecting these review spammers, but most studies are based on individual review spammers and few of them studied group review spammers, nevertheless it should be mentioned that review spammers can increase their effects by cooperating and working together. More words, there have been many features introduced in order to detect review spammers and it is better to use the efficient ones. In this paper we propose a novel framework, named Network Based Group Review Spammers which tries to identify and classify group review spammers with the usage of the heterogeneous information network. In addition to eight basic features for detecting group review spammers, three efficient new features from previous studies were modified and added in order to improve detecting group review spammers. Then with the definition of Meta-path, features are ranked. Results showed that by using the importance of features and adding three new features in the suggested framework, group review spammers detection is improved on Amazon dataset.