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» Graph-Based Induction for General Graph Structured Data
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KDD
2010
ACM
274views Data Mining» more  KDD 2010»
15 years 1 months ago
Grafting-light: fast, incremental feature selection and structure learning of Markov random fields
Feature selection is an important task in order to achieve better generalizability in high dimensional learning, and structure learning of Markov random fields (MRFs) can automat...
Jun Zhu, Ni Lao, Eric P. Xing
KDD
2007
ACM
201views Data Mining» more  KDD 2007»
15 years 10 months ago
Structural and temporal analysis of the blogosphere through community factorization
The blogosphere has unique structural and temporal properties since blogs are typically used as communication media among human individuals. In this paper, we propose a novel tech...
Yun Chi, Shenghuo Zhu, Xiaodan Song, Jun'ichi Tate...
KDD
2009
ACM
200views Data Mining» more  KDD 2009»
15 years 4 months ago
Visual analysis of documents with semantic graphs
In this paper, we present a technique for visual analysis of documents based on the semantic representation of text in the form of a directed graph, referred to as semantic graph....
Delia Rusu, Blaz Fortuna, Dunja Mladenic, Marko Gr...
JMLR
2010
139views more  JMLR 2010»
14 years 4 months ago
Causal learning without DAGs
Causal learning methods are often evaluated in terms of their ability to discover a true underlying directed acyclic graph (DAG) structure. However, in general the true structure ...
David Duvenaud, Daniel Eaton, Kevin P. Murphy, Mar...
STOC
2003
ACM
188views Algorithms» more  STOC 2003»
15 years 10 months ago
Almost random graphs with simple hash functions
We describe a simple randomized construction for generating pairs of hash functions h1, h2 from a universe U to ranges V = [m] = {0, 1, . . . , m - 1} and W = [m] so that for ever...
Martin Dietzfelbinger, Philipp Woelfel