6 citations · 13 across the 6 of their papers we have counts for
7 papers
Testing Central Structure for Complex Networks
Qilong Wu, Yuxiao Li, Simeng Zhang +3
Centrality measures rank nodes even when the observed differences can be explained by ordinary structural variation. We therefore study a preceding question: whether a network cont…
Clusterability test for categorical data
Lianyu Hu, Junjie Dong, Mudi Jiang +2
The objective of clusterability evaluation is to check whether a clustering structure exists within the data set. As a crucial yet often-overlooked issue in cluster analysis, it is…
Personalized Interpretable Classification
Zengyou He, Pengju Li, Yifan Tang +3
How to interpret a data mining model has received much attention recently, because people may distrust a black-box predictive model if they do not understand how the model works. H…
Significance-Based Categorical Data Clustering
Lianyu Hu, Mudi Jiang, Yan Liu +1
Although numerous algorithms have been proposed to solve the categorical data clustering problem, how to access the statistical significance of a set of categorical clusters remain…
The statistical nature of h-index of a network node
Yan Liu, Mudi Jiang, Lianyu Hu +1
Evaluating the importance of a network node is a crucial task in network science and graph data mining. H-index is a popular centrality measure for this task, however, there is sti…
Correlation-Based Community Detection
Zheng Chen, Zengyou He, Hao Liang +2
Mining community structures from the complex network is an important problem across a variety of fields. Many existing community detection methods detect communities through optimi…
Instance-Based Classification through Hypothesis Testing
Zengyou He, Chaohua Sheng, Yan Liu +1
Classification is a fundamental problem in machine learning and data mining. During the past decades, numerous classification methods have been presented based on different princip…