| 摘要 |
A computing system receives input data having both graph and unstructured data and computes a current log likelihood of the input data. The computing system compares the current log likelihood with a previous log likelihood of the input data. If the current log likelihood is larger than the previous log likelihood, the computing system update topic modeling parameters, community modeling parameters, and the link generation parameter until the computing system obtains a maximal value of the log likelihood of the input data. Then, the computing system creates a graph indicating topic similarity between the input data based on the topic modeling parameters, creates another graph indicating community similarity between entities associated with the input data based on the community modeling parameters, and predicts a link existence between input data or entities based on the link generation parameter, the topic modeling parameter and the community modeling parameter. |
