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» Learning random walks to rank nodes in graphs
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ASUNAM
2009
IEEE
14 years 2 months ago
Social Network Discovery Based on Sensitivity Analysis
—This paper presents a novel methodology for social network discovery based on the sensitivity coefficients of importance metrics, namely the Markov centrality of a node, a metr...
Tarik Crnovrsanin, Carlos D. Correa, Kwan-Liu Ma
ICDM
2009
IEEE
211views Data Mining» more  ICDM 2009»
14 years 2 months ago
Discovering Organizational Structure in Dynamic Social Network
—Applying the concept of organizational structure to social network analysis may well represent the power of members and the scope of their power in a social network. In this pap...
Jiangtao Qiu, Zhangxi Lin, Changjie Tang, Shaojie ...
WWW
2008
ACM
14 years 8 months ago
Behavioral classification on the click graph
A bipartite query-URL graph, where an edge indicates that a document was clicked for a query, is a useful construct for finding groups of related queries and URLs. Here we use thi...
Martin Szummer, Nick Craswell
CORR
2011
Springer
199views Education» more  CORR 2011»
13 years 2 months ago
Fast Sparse Matrix-Vector Multiplication on GPUs: Implications for Graph Mining
Scaling up the sparse matrix-vector multiplication kernel on modern Graphics Processing Units (GPU) has been at the heart of numerous studies in both academia and industry. In thi...
Xintian Yang, Srinivasan Parthasarathy, Ponnuswamy...
PAMI
2007
196views more  PAMI 2007»
13 years 7 months ago
Clustering and Embedding Using Commute Times
This paper exploits the properties of the commute time between nodes of a graph for the purposes of clustering and embedding, and explores its applications to image segmentation a...
Huaijun Qiu, Edwin R. Hancock