The problem of community detection receives great attention in recent years. Many methods have been proposed to discover communities in networks. In this paper, we propose a Gaussian stochastic blockmodel that uses Gaussian distributions to fit weight of edges in networks for non-overlapping community detection. The maximum likelihood estimate of this model is equivalent to label propagation wi...

Many web-based application areas must infer label distributions starting from a small set of sparse, noisy labels. Examples include searching for, recommending, and advertising against image, audio, and video content. These labeling problems must handle millions of interconnected entities (users, domains, content segments) and thousands of competing labels (interests, tags, recommendations, top...

The properties (or labels) of nodes in networks can often be predicted based on their proximity and their connections to other labeled nodes. So-called "label propagation algorithms" predict the labels of unlabeled nodes by propagating information about local label density iteratively through the network. These algorithms are fast, simple and scale to large networks but nonetheless regularly pe...

Label propagation is an effective and efficient technique to utilize local and global features in a network for semi-supervised learning. In the literature, one challenge is how to propagate information in heterogeneous networks comprising several subnetworks, each of which has its own cluster structures that need to be explored independently. In this paper, we introduce an intutitive algorithm...

Random walk plays a significant role in computer science. The popular PageRank algorithm uses random walk. Personalized random walks force random walk to “personalized views” of the graph according to users’ preferences. In this paper, we show the close relations between different preferential random walks and label propagation methods used in semi-supervised learning. We further present a maxi...

Community detection is a fundamental and important problem in network science, as community structures often reveal both topological functional relationships between different components of the complex system. In this paper, we first propose gradient descent framework modularity optimization called vector-label propagation algorithm (VLPA), where node associated with vector continuous labels in...