Semi-supervised learning is one of the important topics in machine learning, concerning
with pattern classification where only a small subset of data is labeled. In this paper, a new …

Extracting knowledge from time series provides important tools for many real applications.
However, many challenging problems still open due to the stochastic nature of large amount …

Competitive learning is an important machine learning approach which is widely employed
in artificial neural networks. In this paper, we present a rigorous definition of a new type of …

The real‐world complex networks, such as biological, transportation, biomedical, web, and
social networks, are usually dynamic and change over time. The communities which reflect …

Semisupervised learning is a machine learning approach that is able to employ both labeled
and unlabeled samples in the training process. In this paper, we propose a semisupervised …

Identification and classification of overlapping nodes in networks are important topics in data
mining. In this paper, a network-based (graph-based) semi-supervised learning method is …

In machine learning study, semi-supervised learning has received increasing interests in the
last years. It is applied to classification problems where only a small portion of the data …

Recently, many network-based methods have been developed and successfully applied to
cluster data. Once the underlying network has been constructed, a clustering method can be …

Human activities are usually collective, so clustering has become an important feature of
human behavior. This paper studied the evolution of the community in the process of public …

In this paper, we present a method for determining overlapping cluster structures in a
networked environment. The technique is built upon a definition of a stochastic competitive …