Social network mining

Social network is based on human interactions, from the most classical definition. The data instances collected in the social network have graph-like and temporal characteristics. There are two major strategies for data mining tasks for social networks: one is linkage-based or structure-based, and the other is content-based. The data instances collected in the social network also have two kinds of data instances: static and dynamic or times-series data, such as the tweets on Twitter. Due to the characteristics of the data instance of graphs, there are vast versatile algorithms developed to solve the challenges.

Community detection and the shingling algorithm

Community detection and the shingling algorithm
Community detection and the shingling algorithm
Community detection and the shingling algorithm
Community detection and the shingling algorithm

The node classification and iterative classification algorithms

The node classification and iterative classification algorithms

The second-order algorithm to reduce the number of iterations is as follows:

The node classification and iterative classification algorithms

The R implementation

Please take a look at the R codes file ch_09_shingling.R from the bundle of R codes for previously mentioned algorithms. The codes can be tested with the following command:

> source("ch_09_shingling.R")
The R implementation
The R implementation
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