  Due to the huge commercial interests behind online reviews, a
tremendousamount of spammers manufacture spam reviews for product reputation
manipulation. To further enhance the influence of spam reviews, spammers often
collaboratively post spam reviewers within a short period of time, the
activities of whom are called collective opinion spam campaign. As the goals
and members of the spam campaign activities change frequently, and some
spammers also imitate normal purchases to conceal identity, which makes the
spammer detection challenging. In this paper, we propose an unsupervised
network embedding-based approach to jointly exploiting different types of
relations, e.g., direct common behaviour relation and indirect co-reviewed
relation to effectively represent the relevances of users for detecting the
collective opinion spammers. The average improvements of our method over the
state-of-the-art solutions on dataset AmazonCn and YelpHotel are
[14.09%,12.04%] and [16.25%,12.78%] in terms of AP and AUC, respectively.
