The variety of data in real life exhibits structure or connection property in nature. Typical data includes world-wide-web data, biological data, social network data, image data, and so on. Graph provides a natural way to represent and analyze the structure in these types of data, but the related algorithms usually suffer from a high computational and/or storage complexity, and some of them are even NP-complete problems. Therefore, many graph models and optimization algorithms have been proposed to achieve a better balance between efficacy and efficiency. Some methods in machine learning and related fields can be regarded as special cases or applications of graph models/algorithms, such as (graph) clustering, graph kernel, graph based ranking, graph retrieval, energy minimization in computer vision, to name a few.
In recent years, many new applications and algorithms on graphs are emerging to cater for the needs of processing and understanding big data. The objective of the special issue is thus to provide an opportunity for researchers and engineers from both academia and industry to publish their latest and original results on graph models, algorithms and applications.
Topics of interest include, but are not limited to:
Graph based models and optimization algorithms
Graph based machine learning
Graph based ranking
Applications of graph methods in pattern analysis, computer vision, Web data mining, bioinformatics, cheminformatics, robotics
Other application of graph methods
Submission deadline: Aug 1, 2017
Acceptance notification: Feb 1, 2018
Expected publication time: Apr 30, 2018
Zhi-Yong Liu, Institute of Automation of Chinese Academy of Sciences, email@example.com
Kaizhu Huang, Xi’an Jiaotong-Liverpool University, Kaizhu.Huang@xjtlu.edu.cn
Dr. Xu Yang, Institute of Automation of Chinese Academy of Sciences, firstname.lastname@example.org
Cheng-Lin Liu, Institute of Automation of Chinese Academy of Sciences, email@example.com