Graph clustering

Learning Graph Embedding With Adversarial Training Methods

Graph embedding aims to transfer a graph into vectors to facilitate subsequent graph-analytics tasks like link prediction and graph clustering. Most approaches on graph embedding focus on preserving the graph structure or minimizing the …

MGAE: marginalized graph autoencoder for graph clustering

Graph clustering aims to discover community structures in networks, the task being fundamentally challenging mainly because the topology structure and the content of the graphs are dicult to represent for clustering analysis. Recently, graph …

Classifying networked text data with positive and unlabeled examples

The rapid growth in the number of networked applications that naturally generate complex text data, which contains not only inner features but also inter-dependent relations, has created the demand of efficiently classifying such data. Many …