21 citations · 60 across the 20 of their papers we have counts for
6 papers · 1 filter
Learning Hidden Structures with Relational Models by Adequately Involving Rich Information in A Network
Xuhui Fan, Richard Yi Da Xu, Longbing Cao +1
Effectively modelling hidden structures in a network is very practical but theoretically challenging. Existing relational models only involve very limited information, namely the b…
Non-parametric Power-law Data Clustering
Xuhui Fan, Yiling Zeng, Longbing Cao
It has always been a great challenge for clustering algorithms to automatically determine the cluster numbers according to the distribution of datasets. Several approaches have bee…
A Convergence Theorem for the Graph Shift-type Algorithms
Xuhui Fan, Longbing Cao
Graph Shift (GS) algorithms are recently focused as a promising approach for discovering dense subgraphs in noisy data. However, there are no theoretical foundations for proving th…
Dynamic Infinite Mixed-Membership Stochastic Blockmodel
Xuhui Fan, Longbing Cao, Richard Yi Da Xu
Directional and pairwise measurements are often used to model inter-relationships in a social network setting. The Mixed-Membership Stochastic Blockmodel (MMSB) was a seminal work…
Copula Mixed-Membership Stochastic Blockmodel for Intra-Subgroup Correlations
Xuhui Fan, Longbing Cao, Richard Yi Da Xu
The \emph{Mixed-Membership Stochastic Blockmodel (MMSB)} is a popular framework for modeling social network relationships. It can fully exploit each individual node's participation…
Characterizing A Database of Sequential Behaviors with Latent Dirichlet Hidden Markov Models
Yin Song, Longbing Cao, Xuhui Fan +2
This paper proposes a generative model, the latent Dirichlet hidden Markov models (LDHMM), for characterizing a database of sequential behaviors (sequences). LDHMMs posit that each…