Social Network Extraction Unsupervised
This work addresses the extraction of social networks from big data, which is an incremental contribution to data science and AI.
The paper tackles the problem of extracting social networks from big data sources using an unsupervised approach, aiming to simplify, enrich, and emphasize the results through the integration of various methods.
In the era of information technology, the two developing sides are data science and artificial intelligence. In terms of scientific data, one of the tasks is the extraction of social networks from information sources that have the nature of big data. Meanwhile, in terms of artificial intelligence, the presence of contradictory methods has an impact on knowledge. This article describes an unsupervised as a stream of methods for extracting social networks from information sources. There are a variety of possible approaches and strategies to superficial methods as a starting concept. Each method has its advantages, but in general, it contributes to the integration of each other, namely simplifying, enriching, and emphasizing the results.