Traditional methods of statistical analysis and Data Mining operate on individual samples or data records. However, additional knowledge can be obtained from the analysis of relationships between samples, such as co-ocurrence, relation with the same third object, or pairing of two objects based on some criterion.

The Social Network Analysis (SNA) module contains algorithms and statistics for extracting and analysing information contained in relationships between individual objects in data sets. With the use of SNA methods it is possible to calculate additional attributes for the data, which describe or inform about relationships of a given data sample with other samples. Data enriched in this manner with the use of the SNA module's algorithms can be further used with other traditional data mining algorithms, leading, for instance, to improved classification (scoring) models, which also rely on information about relationships between samples.

The SNA module is different from other modules included in the AdvancedMiner System. A typical workflow involving the SNA module does not include the model building, validating, testing and applying phases. See the chapter SNA in the AdvancedMiner in Practice chapter for information on working with various components of the SNA module.