De-Anonymizing Social Networks With Random Forest Classifier
نویسندگان
چکیده
منابع مشابه
De-anonymizing social networks
The problem of de-anonymizing social networks is to identify the same users between two anonymized social networks [7] (Figure 1). Network de-anonymization task is of multifold significance, with user profile enrichment as one of its most promising applications. After the deanonymization and alignment, we can aggregate and enrich user profile information from different online networking service...
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Advances in technology have made it possible to collect data about individuals and the connections between them, such as email correspondence and friendships. Agencies and researchers who have collected such social network data often have a compelling interest in allowing others to analyze the data. However, in many cases the data describes relationships that are private (e.g., email correspond...
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The original Random Forest derives the final result with respect to the number of leaf nodes voted for the corresponding class. Each leaf node is treated equally and the class with the most number of votes wins. Certain leaf nodes in the topology have better classification accuracies and others often lead to a wrong decision. Also the performance of the forest for different classes differs due ...
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ژورنال
عنوان ژورنال: IEEE Access
سال: 2018
ISSN: 2169-3536
DOI: 10.1109/access.2017.2756904