Session details: Big graph mining 2014 workshop

Author(s):  
U Kang ◽  
Leman Akoglu ◽  
Polo Chau ◽  
Christos Faloutsos
Keyword(s):  

Author(s):  
I Atastina ◽  
B Sitohang ◽  
G A P Saptawati ◽  
V S Moertini
Keyword(s):  


2014 ◽  
Vol 19 (1) ◽  
pp. 33-38 ◽  
Author(s):  
Yang Liu ◽  
Bin Wu ◽  
Hongxu Wang ◽  
Pengjiang Ma
Keyword(s):  


2013 ◽  
Vol 14 (2) ◽  
pp. 29-36 ◽  
Author(s):  
U. Kang ◽  
Christos Faloutsos
Keyword(s):  




2016 ◽  
Vol 6 ◽  
pp. 1-10 ◽  
Author(s):  
Sabeur Aridhi ◽  
Engelbert Mephu Nguifo
Keyword(s):  


2021 ◽  
Vol 15 (1) ◽  
pp. 1-32
Author(s):  
Yu Huang ◽  
Josh Jia-Ching Ying ◽  
Philip S. Yu ◽  
Vincent S. Tseng


2019 ◽  
Vol 30 (4) ◽  
pp. 24-40
Author(s):  
Lei Li ◽  
Fang Zhang ◽  
Guanfeng Liu

Big graph data is different from traditional data and they usually contain complex relationships and multiple attributes. With the help of graph pattern matching, a pattern graph can be designed, satisfying special personal requirements and locate the subgraphs which match the required pattern. Then, how to locate a graph pattern with better attribute values in the big graph effectively and efficiently becomes a key problem to analyze and deal with big graph data, especially for a specific domain. This article introduces fuzziness into graph pattern matching. Then, a genetic algorithm, specifically an NSGA-II algorithm, and a particle swarm optimization algorithm are adopted for multi-fuzzy-objective optimization. Experimental results show that the proposed approaches outperform the existing approaches effectively.





2010 ◽  
Vol 39 (6) ◽  
pp. e34-e34 ◽  
Author(s):  
Niranjan Nagarajan ◽  
Carl Kingsford


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