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159
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ICDM
2002
IEEE
178views Data Mining» more  ICDM 2002»
15 years 11 months ago
gSpan: Graph-Based Substructure Pattern Mining
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...
Xifeng Yan, Jiawei Han
158
Voted
RSA
2010
104views more  RSA 2010»
15 years 4 months ago
Random graphs with forbidden vertex degrees
We study the random graph Gn,λ/n conditioned on the event that all vertex degrees lie in some given subset S of the nonnegative integers. Subject to a certain hypothesis on S, the...
Geoffrey R. Grimmett, Svante Janson
DASFAA
2010
IEEE
169views Database» more  DASFAA 2010»
15 years 4 months ago
Fires on the Web: Towards Efficient Exploring Historical Web Graphs
Abstract. Discovery of evolving regions in large graphs is an important issue because it is the basis of many applications such as spam websites detection in the Web, community lif...
Zhenglu Yang, Jeffrey Xu Yu, Zheng Liu, Masaru Kit...
PKDD
2010
Springer
127views Data Mining» more  PKDD 2010»
15 years 4 months ago
Software-Defect Localisation by Mining Dataflow-Enabled Call Graphs
Defect localisation is essential in software engineering and is an important task in domain-specific data mining. Existing techniques building on call-graph mining can localise dif...
Frank Eichinger, Klaus Krogmann, Roland Klug, Klem...
DIALM
2008
ACM
179views Algorithms» more  DIALM 2008»
15 years 7 months ago
Distance graphs: from random geometric graphs to Bernoulli graphs and between
A random geometric graph G(n, r) is a graph resulting from placing n points uniformly at random on the unit area disk, and connecting two points iff their Euclidean distance is at ...
Chen Avin