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1
gSpan: Graph-Based Substructure Pattern Mining
In: http://www-faculty.cs.uiuc.edu/~hanj/pdf/icdm02_gspan.pdf (2002)
Abstract: We investigate new approaches for frequent graph-based pattern mining in graph datasets and propose a novel algorithm called gSpan (graph-based Substructure pattern mining) , which discovers frequent substructures without candidate generation. gSpan builds a new lexicographic order among graphs, and maps each graph to a unique minimum DFS code as its canonical label. Based on this lexicographic order, gSpan adopts the depth-first search strategy to mine frequent connected subgraphs efficiently. Our performance study shows that gSpan substantially outperforms previous algorithms, sometimes by an order of magnitude.
URL: http://www-faculty.cs.uiuc.edu/~hanj/pdf/icdm02_gspan.pdf
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.6963
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2
on the paper ”OPINOSIS- A Graph-Based Approach to Abstractive Summarization of Highly Redundant Opinions” by
In: http://www.mpi-inf.mpg.de/departments/d5/teaching/ws10_11/hir/reports/CosminaCroitoru.pdf
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� Problems
In: http://kavita-ganesan.com/sites/default/files/opinosis-presentation.ppt.pdf
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4
Semantic Frame-Based Document Representation for Comparable Corpora
In: http://web.engr.illinois.edu/~hanj/pdf/icdm13_hkim.pdf
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5
iTopicModel: Information Network-Integrated Topic Modeling
In: http://www.cs.uiuc.edu/homes/hanj/pdf/icdm09_ysun.pdf
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6
Content Coverage Maximization on Word Networks for Hierarchical Topic Summarization
In: http://www.cs.uiuc.edu/homes/hanj/pdf/cikm13_cwang.pdf
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7
The Wisdom of Minority: Unsupervised Slot Filling Validation based on Multi-dimensional Truth-Finding
In: http://aclweb.org/anthology/C/C14/C14-1149.pdf
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8
SCENE: Structural Conversation Evolution NEtwork
In: http://www.cs.uiuc.edu/%7Ehanj/pdf/asonam11_mdanilevsky.pdf
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9
Exploiting Background Information Networks to Enhance Bilingual Event Extraction Through Topic Modeling
In: http://www.thinkmind.org/download.php?articleid=immm_2011_1_40_20203
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