157 citations · 314 across the 13 of their papers we have counts for
15 papers
Solving Fokker-Planck equation using deep learning
Yong Xu, Hao Zhang, Yongge Li +3
The probability density function of stochastic differential equations is governed by the Fokker-Planck (FP) equation. A novel machine learning method is developed to solve the gene…
A belief combination rule for a large number of sources
Kuang Zhou, Arnaud Martin, Quan Pan
The theory of belief functions is widely used for data from multiple sources. Different evidence combination rules have been proposed in this framework according to the properties…
Evidential community detection based on density peaks
Kuang Zhou, Quan Pan, Arnaud Martin
Credal partitions in the framework of belief functions can give us a better understanding of the analyzed data set. In order to find credal community structure in graph data sets,…
Evidence combination for a large number of sources
Kuang Zhou, Arnaud Martin, Quan Pan
The theory of belief functions is an effective tool to deal with the multiple uncertain information. In recent years, many evidence combination rules have been proposed in this fra…
The Advantage of Evidential Attributes in Social Networks
Salma Ben Dhaou, Kuang Zhou, Mouloud Kharoune +2
Nowadays, there are many approaches designed for the task of detecting communities in social networks. Among them, some methods only consider the topological graph structure, while…
Semi-supervised evidential label propagation algorithm for graph data
Kuang Zhou, Arnaud Martin, Quan Pan
In the task of community detection, there often exists some useful prior information. In this paper, a Semi-supervised clustering approach using a new Evidential Label Propagation…
Evidential Label Propagation Algorithm for Graphs
Kuang Zhou, Arnaud Martin, Quan Pan +1
Community detection has attracted considerable attention crossing many areas as it can be used for discovering the structure and features of complex networks. With the increasing s…
The belief noisy-or model applied to network reliability analysis
Kuang Zhou, Arnaud Martin, Quan Pan
One difficulty faced in knowledge engineering for Bayesian Network (BN) is the quan-tification step where the Conditional Probability Tables (CPTs) are determined. The number of pa…
ECMdd: Evidential c-medoids clustering with multiple prototypes
Kuang Zhou, Arnaud Martin, Quan Pan +1
In this work, a new prototype-based clustering method named Evidential C-Medoids (ECMdd), which belongs to the family of medoid-based clustering for proximity data, is proposed as…
A similarity-based community detection method with multiple prototype representation
Kuang Zhou, Arnaud Martin, Quan Pan
Communities are of great importance for understanding graph structures in social networks. Some existing community detection algorithms use a single prototype to represent each gro…
Evidential relational clustering using medoids
Kuang Zhou, Arnaud Martin, Quan Pan +1
In real clustering applications, proximity data, in which only pairwise similarities or dissimilarities are known, is more general than object data, in which each pattern is descri…
Belief Hierarchical Clustering
Wiem Maalel, Kuang Zhou, Arnaud Martin +1
In the data mining field many clustering methods have been proposed, yet standard versions do not take into account uncertain databases. This paper deals with a new approach to clu…
Evidential Communities for Complex Networks
Kuang Zhou, Arnaud Martin, Quan Pan
Community detection is of great importance for understand-ing graph structure in social networks. The communities in real-world networks are often overlapped, i.e. some nodes may b…
Median evidential c-means algorithm and its application to community detection
Kuang Zhou, Arnaud Martin, Quan Pan +1
Median clustering is of great value for partitioning relational data. In this paper, a new prototype-based clustering method, called Median Evidential C-Means (MECM), which is an e…
Evidential-EM Algorithm Applied to Progressively Censored Observations
Kuang Zhou, Arnaud Martin, Quan Pan
Evidential-EM (E2M) algorithm is an effective approach for computing maximum likelihood estimations under finite mixture models, especially when there is uncertain information abou…