Facebook, Inc.

Gaurav Singh Thakur

Komal Kapoor

Haotian Wang

An online system maintains pages and accesses a graph of nodes representing the pages. Each node is labeled to indicate that a corresponding page is for a real-world entity, an imposter of the real-world entity, or a derived entity complying with or violating a policy. The online system retrieves machine-learning models, each of which is trained based on labels for a set of the nodes and features of corresponding pages. A first model predicts whether a page is for a derived entity based on features of the page. Responsive to predicting the page is not for a derived entity, a second model predicts whether the page is for a real-world entity or an imposter based on features of the page. Responsive to predicting the page is for a derived entity, a third model predicts whether the derived entity complies with or violates the policy based on features of the page.

Detecting a page for a real-world entity, an imposter of a real-world entity, or a non-real-world entity that complies with or violates a policy of an online system

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Seattle, WA, United States of America

Gaurav Singh Thakur