On the momentum term in gradient descent learning algorithms
Neural Networks
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Neural Processing Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
Comparing fuzzy algorithms on overlapping communities in networks
ICICA'10 Proceedings of the First international conference on Information computing and applications
Comparative analysis for k-means algorithms in network community detection
ISICA'10 Proceedings of the 5th international conference on Advances in computation and intelligence
Identification of multi-resolution network structures with multi-objective immune algorithm
Applied Soft Computing
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To find the best partition of a large and complex network into a small number of clusters has been addressed in many different ways. However, the probabilistic setting in which each node has a certain probability of belonging to a certain cluster has been scarcely discussed. In this paper, a fuzzy partitioning formulation, which is extended from a deterministic framework for network partition based on the optimal prediction of a random walker Markovian dynamics, is derived to solve this problem. The algorithms are constructed to minimize the objective function under this framework. It is demonstrated by the simulation experiments that our algorithms can efficiently determine the probabilities with which a node belongs to different clusters during the learning process. Moreover, they are successfully applied to two real-world networks, including the social interactions between members of a karate club and the relationships of some books on American politics bought from Amazon.com.