1. Aim and Scope
Existing works on graph machine learning, especially on graph neural network (GNN), are typically for conventional graphs. In many emerging applications (such as in ecommerce, social networks and bioinformatics), however, the graph interactions are beyond general attributed graphs and are more complex. For example, the e-commerce search interactions can be better modelled as text-rich graphs where nodes information is semantic text rather than features; the sequence dependence data (such as click-streams where the choice of the next page depends not only on the current page but also on previous pages) can be better modelled as higher-order dependency graphs rather than the conventional first-order graphs. This brings a big challenge and new research topics in graph-based pattern recognition. So, in this special issue we will focus on graph-based pattern recognition with machine learning on more complex and heterogeneous graphs, such as text-rich graphs, multi-relational graphs, heterophilic graphs, higher-order dependency graphs, spatio-temporal graphs, bipartite graphs, signed graphs and hypergraphs [1, 2], as well as their emerging applications in ecommerce, biometrics /bioinformatics, CV and NLP [3].
[1] Di Jin, Cuiying Huo, Chundong Liang, and Liang Yang, Heterogeneous Graph Neural Network via Attribute Completion. The Web Conference (WWW 2021)
[2] Zhizhi Yu, Di Jin, Ziyang Liu, Dongxiao He, Xiao Wang, Hanghang Tong, and Jiawei Han, AS-GCN: Adaptive Semantic Architecture of Graph Convolutional Networks for Text-Rich Networks, ICDM 2021
[3] Di Jin, Zhizhi Yu, Pengfei Jiao, Shirui Pan, Dongxiao He, Jia Wu, Philip S. Yu, and Weixiong Zhang, A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning, TKDE 2021
2. Themes
Papers are invited on but not limited to new models, algorithms, theories and applications, and bring both researchers from academia and practitioners from industry to discuss the latest progress, new topics and advanced applications in Graph Machine Learning for Pattern Recognition on Complex Graphs, which are listed below.
Graph-based Pattern Recognition with Machine Learning on Text-rich Graphs
Graph-based Pattern Recognition with Machine Learning on Multi-relational Graphs
Graph-based Pattern Recognition with Machine Learning on Graphs with Heterophily
Graph-based Pattern Recognition with Machine Learning on Higher-order Dependency Graphs
Graph-based Pattern Recognition with Machine Learning on Spatio-Temporal Graphs
Graph-based Pattern Recognition with Machine Learning on Bipartite Graphs
Graph-based Pattern Recognition with Machine Learning on Signed Graphs
Graph-based Pattern Recognition with Machine Learning on Hypergraphs
Graph-based Pattern Recognition with Machine Learning on other types of complex and heterogeneous graphs
Their emerging applications such as Network Regularities Recognition (e.g., community detection), Ecommerce Search / Recommendation, Traffic Flow Prediction, Biometrics / Bioinformatics, and so on.
3. Submissions
The manuscripts should be prepared according to: https://www.journals.elsevier.com/pattern-recognition/forthcoming-special-issues/special-issue-on-graph-machine-learning and submission should be done through the journal’s submission website: https://www.editorialmanager.com/pr/default1.aspx by selecting “VSI: Graph Machine Learning” and also clearly indicating the full title of this special issue “Graph Machine Learning for Pattern Recognition on Complex Graphs” in comments to the Editor-in-Chief.
Each submitted paper will be reviewed by expert reviewers. Submission of a paper will imply that it contains original unpublished work and is not being submitted for publication elsewhere.
4. Important Dates
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Submission Open Date: Sep 30, 2022
Final Manuscript Submission Deadline: March 31, 2023
Editorial Acceptance Deadline: July 31, 2023
5. Guest Editors
Di Jin, Tianjin University, Email address: jindi@tju.edu.cn
Bio: He was the recipient of the Best Paper Award Runner-up of WWW 2021, and the Best Student Paper Award Runner-up of ICDM 2021. He serves as the Associate Editor of Information Sciences, the Associate Editor of Humanities & Social Sciences Communications, a PC Board Member of IJCAI 2022-2024, and SPCs in AAAI and IJCAI.Shirui Pan, Griffith University, Email address: shiruipan@ieee.org
Bio: He is recognized as one of the AI 2000 AAAI/IJCAI Most Influential Scholars in Australia (2021). He is an awardee of a prestigious Future Fellowship (2021-2025), one of the most competitive grants from the Australian Research Council (ARC).Weiping Ding, Nantong University, Email address: ding.wp@ntu.edu.cn
Bio: He serves as Associate Editors of IEEE Transactions on Neural Networks and Learning Systems, IEEE Transactions on Fuzzy Systems, IEEE/CAA Journal of Automatica Sinica, Information Sciences (Elsevier), Neurocomputing (Elsevier), and Co-Editor-in-Chief of Journal of Artificial Intelligence and System.Kaska Musial, University of Technology Sydney, Email address: katarzyna.musial-gabrys@uts.edu.au
Bio: She is the Deputy Head of School of Computer Science, University of Technology Sydney. Together with Prof. Gabrys she founded and is now a co-director of the Complex Adaptive Systems Lab within the Data Science Institute.Francoise Soulie, Hub France IA, Email address: francoise.soulie@outlook.com
Bio: She is a co-founder of Hub France Intelligence Artificielle. She is currently a member of the High Level Experts Group that is working with the European Commission on an AI strategy for Europe. She was the PC Chair of KDD 2009.Philip S. Yu, University of Illinois at Chicago, Email address: psyu@uic.edu
Bio: He is a Fellow of the ACM and of the IEEE. He was the Editor-in-Chief of IEEE Transactions on Knowledge and Data Engineering (2001-2004). He is associate editors of ACM Transactions on the Internet Technology and ACM Transactions on Knowledge Discovery from Data.
