Learning on Big Graph: Label Inference and Regularization with Anchor Hierarchy

2017 ◽  
Vol 29 (5) ◽  
pp. 1101-1114 ◽  
Author(s):  
Meng Wang ◽  
Weijie Fu ◽  
Shijie Hao ◽  
Hengchang Liu ◽  
Xindong Wu
Keyword(s):  
Big Graph ◽  
Graph Label

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 Based on Bipartite Graph Label Gene Extraction Algorithm of Network Structure

2011 ◽  
Vol 3 (4) ◽  
Author(s):  
Jiming Li ◽  
Ning Zhang ◽  
Zhaoxing Liu ◽  
Gengsheng Zhao
Keyword(s):  
Bipartite Graph ◽  
Network Structure ◽  
Extraction Algorithm ◽  
Graph Label

scholarly journals Big Graph Analytics Systems

2016 ◽  
Author(s):  
Da Yan ◽  
Yingyi Bu ◽  
Yuanyuan Tian ◽  
Amol Deshpande ◽  
James Cheng
Keyword(s):  
Graph Analytics ◽  
Big Graph

Big Graph Analytics

2018 ◽  
pp. 97-104
Author(s):  
Ahsanur Rahman ◽  
Tamanna Motahar
Keyword(s):  
Graph Analytics ◽  
Big Graph

Knowledge Big Graph Fusing Ontology with Property Graph: A Case Study of Financial Ownership Network

2021 ◽  
Vol 48 (1) ◽  
pp. 55-71
Author(s):  
Xiao-Bo Tang ◽  
Wei-Gang Fu ◽  
Yan Liu
Keyword(s):  
Big Data ◽  
Large Scale ◽  
Graph Model ◽  
Big Graph ◽  
Processing Power ◽  
Inference Algorithms ◽  
Data Environment ◽  
Fusion Framework ◽  
Contradictory Data

The scale of know­ledge is growing rapidly in the big data environment, and traditional know­ledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a know­ledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of the fusion, that is, the know­ledge representation model is called know­ledge big graph. In addition, this paper applies the know­ledge big graph model to the ownership network in the China’s financial field and builds a financial ownership know­ledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership know­ledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the know­ledge big graph could provide technical reference for big data know­ledge organization and services.

Multi-fuzzy-constrained graph pattern matching with big graph data

2020 ◽  
Vol 24 (4) ◽  
pp. 941-958
Author(s):  
Guliu Liu ◽  
Lei Li ◽  
Xindong Wu
Keyword(s):  
Pattern Matching ◽  
Graph Pattern Matching ◽  
Graph Data ◽  
Graph Pattern ◽  
Big Graph
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