A Network-Specific Markov Random Field Approach to Community Detection


  • Dongxiao He Tianjin University
  • Xinxin You Tianjin University
  • Zhiyong Feng Tianjin University
  • Di Jin Tianjin University
  • Xue Yang Tianjin University
  • Weixiong Zhang Washington University, St. Louis


Community Detection, Markov Random Field, Belief Propagation


Markov Random Field (MRF) is a powerful framework for developing probabilistic models of complex problems. MRF models possess rich structures to represent properties and constraints of a problem. It has been successful on many application problems, particularly those of computer vision and image processing, where data are structured, e.g., pixels are organized on grids. The problem of identifying communities in networks, which is essential for network analysis, is in principle analogous to finding objects in images. It is surprising that MRF has not yet been explored for network community detection. It is challenging to apply MRF to network analysis problems where data are organized on graphs with irregular structures. Here we present a network-specific MRF approach to community detection. The new method effectively encodes the structural properties of an irregular network in an energy function (the core of an MRF model) so that the minimization of the function gives rise to the best community structures. We analyzed the new MRF-based method on several synthetic benchmarks and real-world networks, showing its superior performance over the state-of-the-art methods for community identification.




How to Cite

He, D., You, X., Feng, Z., Jin, D., Yang, X., & Zhang, W. (2018). A Network-Specific Markov Random Field Approach to Community Detection. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/11281