Ling Liu's SC13 paper "Large Graph Processing Without the Overhead" featured by HPCwire.
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Second GraphLab workshop should be even bigger than the first! GraphLab is a new programming framework for graph-style data analytics.
Clustering Service Networks with Entity, Attribute and Link Heterogeneity
IEEE 2015 International Conference on Web Services (ICWS 2015), New York, June 27-July 2, 2015.
Yang Zhou, Ling Liu, Xianqiang Bao, Kisung Lee, Calton Pu, Balaji Palanisamy*,
Emre Yigitoglu, Qi Zhang
Georgia Institute of Technology
* University of Pittsburgh
Many popular web service networks are contentrich in terms of heterogeneous types of entities and links, associated with incomplete attributes. Clustering such heterogeneous service networks demands new clustering techniques that can handle two heterogeneity challenges: (1) multiple types of entities co-exist in the same service network with multiple attributes, and (2) links between entities have diverse types and carry different semantics. Existing heterogeneous graph clustering techniques tend to pick initial centroids uniformly at random, specify the number k of clusters in advance, and fix k during the clustering process. In this paper, we propose SERVICECLUSTER, a novel heterogeneous SERVICE network CLUSTERing algorithm with four unique features. First, we incorporate various types of entity, attribute and link information into a unified distance measure. Second, we design a Discrete Steepest Descent method to naturally produce initial k and initial centroids simultaneously. Third, we propose a dynamic learning method to automatically adjust the link weights towards clustering convergence. Fourth, we develop an effective optimization strategy to identify new suitable k and k well-chosen centroids at each clustering iteration.
FULL PAPER: pdf