Session details: Big graph mining 2014 workshop

2014 ◽  
Author(s):  
U Kang ◽  
Leman Akoglu ◽  
Polo Chau ◽  
Christos Faloutsos
Keyword(s):  
Graph Mining ◽  
Big Graph

scholarly journals A Review of Big Graph Mining Research

2017 ◽  
Vol 180 ◽  
pp. 012065 ◽  
Author(s):  
I Atastina ◽  
B Sitohang ◽  
G A P Saptawati ◽  
V S Moertini
Keyword(s):  
Graph Mining ◽  
Big Graph

BPGM: A big graph mining tool

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

Big graph mining

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

Big graph mining for the web and social media

2014 ◽  
Author(s):  
U. Kang ◽  
Leman Akoglu ◽  
Duen Horng Chau
Keyword(s):  
Social Media ◽  
Graph Mining ◽  
Big Graph ◽  
The Web

scholarly journals Big Graph Mining: Frameworks and Techniques

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

Dynamic Graph Mining for Multi-weight Multi-destination Route Planning with Deadlines Constraints

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

Multi-Fuzzy-Objective Graph Pattern Matching with Big Graph Data

2019 ◽  
Vol 30 (4) ◽  
pp. 24-40
Author(s):  
Lei Li ◽  
Fang Zhang ◽  
Guanfeng Liu
Keyword(s):  
Pattern Matching ◽  
Particle Swarm Optimization Algorithm ◽  
Nsga Ii ◽  
Specific Domain ◽  
Graph Pattern Matching ◽  
Graph Data ◽  
Graph Pattern ◽  
Big Graph ◽  
Complex Relationships ◽  
Pattern Graph

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.

scholarly journals Discovering configuration templates of virtualized tenant networks in multi-tenancy datacenters via graph-mining

2012 ◽  
Vol 42 (3) ◽  
pp. 13-20 ◽  
Author(s):  
Yosuke Himura ◽  
Yoshiko Yasuda
Keyword(s):  
Graph Mining

scholarly journals GiRaF: robust, computational identification of influenza reassortments via graph mining

2010 ◽  
Vol 39 (6) ◽  
pp. e34-e34 ◽  
Author(s):  
Niranjan Nagarajan ◽  
Carl Kingsford
Keyword(s):  
Graph Mining ◽  
Computational Identification
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