Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
Another list highlighting Open Source Software Releases.
Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Dependent Nonparametric Trees for Dynamic Hierarchical Clustering
Proceedings of 2014 Neural Information Processing Systems (NIPS’14), December 2014.
Avinava Dubey, Qirong Ho*, Sinead Williamson^, Eric P. Xing
Machine Learning Department, Carnegie Mellon University
*Institute for Infocomm Research, A*STAR
^McCombs School of Business, University of Texas at Austin
Hierarchical clustering methods offer an intuitive and powerful way to model a wide variety of data sets. However, the assumption of a fixed hierarchy is often overly restrictive when working with data generated over a period of time: We expect both the structure of our hierarchy, and the parameters of the clusters, to evolve with time. In this paper, we present a distribution over collections of time-dependent, infinite-dimensional trees that can be used to model evolving hierarchies, and present an efficient and scalable algorithm for performing approximate inference in such a model. We demonstrate the efficacy of our model and inference algorithm on both synthetic data and real-world document corpora.
FULL PAPER: pdf