Random sampling-based collective classification method and system for sybil account detection

A non-transitory computer-readable recording medium storing instructions that, when executed by a processor, cause the processor to implement a random sampling- based collective classification method performed by a collective classification system, the random sampling-based collective classification comprising:

• performing a first collective classification using training data;
• constructing sampled new training data by randomly extracting a portion of the entire nodes based on a label assigned to each node according to a result of performing the first collective classification;
• performing a second collective classification using the constructed new training data;
• and
• applying a posterior score difference of each node computed through the first collective classification and the second collective classification to a prior score of each node to be used for training data at a next iteration.

A collective classification system comprising:

• a first classifier configured to perform a first collective classification using training data;
• a data constructor configured to construct sampled new training data by randomly extracting a portion of the entire nodes based on a label assigned to each node according to a result of performing the first collective classification;
• a second classifier configured to perform a second collective classification using the constructed new training data;
• and
• a prior score assigner configured to apply a posterior score difference of each node computed through the first collective classification and the second collective classification to a prior score of each node to be used for training data at a next iteration.