We tackle the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers, for users connected by a social network. Standard label propagation fails...

Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Speaker-Listener Label Prop...

We consider the problem of inferring node labels in a partially labeled graph where each node in the graph has multiple label types and each label type has a large number of possible labels. Our primary example, and the focus of this paper, is the joint inference of label types such as hometown, current city, and employers for people connected by a social network; by predicting these user profi...

Semi-supervised learning aims at discovering spatial structures in high-dimensional input spaces when insufficient background information about clusters is available. A particulary interesting approach is based on propagation of class labels through proximity graphs. The Emergent Self-Organizing Map (ESOM) itself can be seen as such a proximity graph that is suitable for label propagation. It t...

The fastest deterministic algorithms for connected components take logarithmic time and perform superlinear work on a Parallel Random Access Machine (PRAM). These maintain spanning forest by merging compressing trees, which requires pointer-chasing operations that increase memory access latency are limited to shared-memory systems. Many of these PRAM also very complicated implement. Another pop...