CoDiNMF: Co-Clustering of Directed Graphs via NMF


  • Woosang Lim Georgia Institute of Technology
  • Rundong Du Georgia Institute of Technology
  • Haesun Park Georgia Institute of Technology


Clustering, Data Mining and Knowledge Discovery


Co-clustering computes clusters of data items and the related features concurrently, and it has been used in many applications such as community detection, product recommendation, computer vision, and pricing optimization. In this paper, we propose a new co-clustering method, called CoDiNMF, which improves the clustering quality and finds directional patterns among co-clusters by using multiple directed and undirected graphs. We design the objective function of co-clustering by using min-cut criterion combined with an additional term which controls the sum of net directional flow between different co-clusters. In addition, we show that a variant of Nonnegative Matrix Factorization (NMF) can solve the proposed objective function effectively. We run experiments on the US patents and BlogCatalog data sets whose ground truth have been known, and show that CoDiNMF improves clustering results compared to other co-clustering methods in terms of average F1 score, Rand index, and adjusted Rand index (ARI). Finally, we compare CoDiNMF and other co-clustering methods on the Wikipedia data set of philosophers, and we can find meaningful directional flow of influence among co-clusters of philosophers.




How to Cite

Lim, W., Du, R., & Park, H. (2018). CoDiNMF: Co-Clustering of Directed Graphs via NMF. Proceedings of the AAAI Conference on Artificial Intelligence, 32(1). Retrieved from