GraphSig: a scalable approach to mining significant subgraphs in large graph databases

Proceedings - International Conference on Data Engineering

Volumn , Issue , 2009, Pages 844-855

  Ranu, Sayan ^a   Singh, Ambuj K ^a  

^a University of California   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION COSTS; DOMAIN KNOWLEDGE; EMPIRICAL RESULTS; FEATURE SETS; FEATURE SPACE; FEATURE VECTORS; FREQUENT PATTERNS; FREQUENT SUBGRAPH MINING; GRAPH DATABASE; LARGE GRAPHS; LOW FREQUENCY; P-VALUES; PRIOR PROBABILITY; SCALABLE APPROACH; SCIENTIFIC DATA; SEARCH SPACES; SIGNIFICANT PATTERNS; STATISTICAL SIGNIFICANCE; SUBGRAPHS;

CLASSIFIERS; LEARNING SYSTEMS; MINES;

MINING;

EID: 67649644367     PISSN: 10844627     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDE.2009.133     Document Type: Conference Paper

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