Graph-based Methods for Large Scale Financial and Business Data Analysis

  in Special Issue   Posted on June 3, 2020

Information for the Special Issue

Submission Deadline: Tue 30 Jun 2020
Journal Impact Factor : 4.582
Journal Name : Pattern Recognition
Journal Publisher:
Website for the Special Issue:
Journal & Submission Website:

Special Issue Call for Papers:

Aims and Scope

Machine learning and pattern recognition techniques have had a significant impact on the analysis of large-scale datasets in the financial domain. However, to date most of the analysis techniques used have focused on the use of standard vectorial methods and time series data. Recently though, interest has turned to the use of relational and similarity-based representations of financial data. This is largely due to improvements in the maturity of the available methods, including graph embedding, graph kernels and deep graph convolutional networks. This has resulted in a number of impressive applications of graph-based methods for data analysis in the finance and business sectors. Because of the timeliness of this topic, this special issue will focus on the recent advances in graph-based pattern recognition approaches in the finance domain. Over the past decade or so, the effectiveness of graph-based methods has been repeatedly demonstrated for modeling the complex structural relationships that exist in high volume and high dimensional data. In the meantime, the size and dimension of data encountered in the finance and business sectors that need to be analyzed have grown dramatically. Despite their attractive features, graph-based pattern recognition methods are still far from being a panacea for extracting or mining relevant information from financial and business data. In addition, because financial data is often time-varying, high-dimensional, unstable, and often noisy or imbalanced, it brings with additional challenges for developing efficient and effective graph-based pattern recognition techniques. Provided these problems can be controlled, graph-based pattern recognition holds out the potential as a powerful tool for modelling complex structural data relationships, and also mining both useful information and temporal patterns which could be used for building powerful analytics for use by financial and commercial organizations. These approaches will thus significantly benefit financial market analysis, banking, and e-commerce, not only for predicting factors such as accurate financial behavior prediction and risk management, but also fraud and anomalous behavior detection.


  • Graph Kernel Methods
  • Graph Embedding Methods
  • Graph-based Deep Learning
  • Graph-based Feature Selection
  • Complex Networks
  • Structural Time Series Analysis
  • Graph Matching
  • Graph-based Optimization
  • Graph Neural Networks
  • Representation Learning on Graph Structured Data
  • Graph Classification and Vertex Classification
  • Adversarial and Generative Graph Learning
  • Transductive and Inductive Learning on Graphs
  • Survey Papers Regarding the Topic of Learning with Graphs
  • Other Graph-based Methods


  • Financial Market Simulation
  • Trading Strategies
  • Stock Price Forecasting
  • Derivatives Modelling and Pricing
  • Credit Scoring & Credit Rating
  • Bankruptcy Prediction
  • Fraud Detection
  • Contagion Modelling and Analysis
  • Analyzing Financial Crisis
  • Systematic Risk Prediction
  • Portfolio Optimization
  • E-commerce Applications
  • Block Chain Applications
  • Other Important Aspects, Issues and Progress associated with Graph-based methods in Financial and Business Applications

Submission Guidelines

Papers should be formatted in a single column with double spacing and be no more than 40 pages in length. Before submitting the manuscript, please read the Instructions for Authors for Pattern Recognition journal ( The manuscript should be submitted via the official website If you are not sure whether your work is suitable to the special issue, please feel free to contact the guest editors before the submission. To ensure that all manuscripts are correctly identified for inclusion into the special issue, it is important that authors select “SI: Graph-based Finance” when they reach the “Article Type Name” step in the submission process. We are happy to receive extensions of works presented in top conferences but with a substantial revision (30 percent is generally considered “substantial”). Please visit for more detailed information.

Important Dates

  • Submission system starts receiving submissions: Dec. 15th, 2019
  • Manuscript submissions due: June. 30th, 2020
  • First review completed: Sep. 30th, 2020
  • Revised manuscripts due: Nov. 31rd, 2020
  • Second review completed: Feb. 28th, 2021
  • Final manuscript due: April. 30th, 2021
  • Targeted issue of Pattern Recognition (tentative): May. 30th 2021

Guest Editors

Lu BaiSchool of Information, Central University of Finance and Economics, Beijing, China. E-mail:

Lixin Cui, School of Information, Central University of Finance and Economics, Beijing, China. E-mail:

Guandong Xu, School of Computer Science and Advanced Analytics, University of Technology Sydney, Australia. Email:

Junchi Yan, Department of Computer Science and Engineering, Shanghai Jiao Tong University, China. Email:

Zhihong Zhang, School of Information, Xiamen University, Fujian, China. Email:

Luca Rossi, Department of Computer Science and Engineering, Southern University of Science and Technology, Guangdong, China. E-mail:

Jian Tang, HEC Montreal & Montreal Institute for Learning Algorithms, Canada. E-mail:

Philip S. Yu, Department of Computer Science, University of Illinois at Chicago, USA. E-mail:

Primary Contacts

Lu Bai, and Lixin Cui

School of Information, Central University of Finance and Economics

39 South College Road, Haidian District

Beijing, China




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