An overlapping network community partition algorithm based on semi-supervised matrix factorization and random walk

- We combine the prior content information in the network to construct a must-link matrix and a cannot-link matrix.
- We propose an algorithm based on semi-supervised matrix factorization and random walk.
- The random walk model based on node convergence degree is combined with nonnegative matrix factorization.

The discovery of community structure is the basis of understanding the topology structure and social function of the network. It is also an important factor for recommendation technology, information dissemination, event prediction, and more. In this paper, we consider the structure and characteristics of the social network and propose an algorithm based on semi-supervised matrix factorization and random walk. The proposed method first calculates the transition probability between nodes through the topology of the network. The random walk model is then used to obtain the final walk probability, and the feature matrix is constructed. At the same time, we combine a priori content information in the network to build a must-link matrix and a cannot-link matrix. We then merge them into the feature matrix of the random walk to form a new feature matrix. Finally, the expectation of the number of edges is defined according to the factorized membership matrix. Results demonstrate the effectiveness and better performance of our method.