Higher-Order Graph Analytics

Models, Algorithms and Applications
Erscheinungsjahr 2027
ISBN: 978-981-9256-54-9
Verlag: Springer

Buch, Englisch, Format (B × H): 155 mm x 235 mm

Reihe: Data Analytics

Graphs have become a fundamental tool for data analysis, but conventional models based on homogeneous, static, and pairwise connections often leave important information hidden. Higher-order graph analytics reveals a richer view of connected data by distinguishing typed relationships, tracing time-ordered interactions, studying dependencies across layers, and capturing multiway group relationships. These capabilities open new possibilities for analyzing complex data in social networks, transportation, communication, biology, finance, and many other domains.

This book provides a systematic guide to higher-order graph analytics through four major models: heterogeneous information networks (HINs), temporal graphs, multilayer graphs, and hypergraphs. For each model, it connects key concepts and problem formulations with computational methods, algorithms, and real-world applications. The discussion shows how semantic types, temporal order, cross-layer dependencies, and multiway interactions change the way graph data is modeled and analyzed.

Covering core tasks, including centrality, community detection, cohesive subgraph analysis, traversal, and pattern matching, the book equips readers with the tools and knowledge needed to analyze complex graph data effectively. It serves as an essential resource for researchers, data scientists, and students seeking to master this rapidly evolving field and apply higher-order graph analytics across disciplines.

Zielgruppe

Research

Autoren/Hrsg.

Yuan, Long

Yang, Zhengyi

Zhang, Wenjie

Inhaltsverzeichnis

Chapter 1. Introduction.- Chapter 2. Heterogeneous Information Networks.- Chapter 3. Temporal Graphs.- Chapter 4. Multilayer Graphs.- Chapter 5. Hypergraphs.- Chapter 6. Conclusion.

Über Autor(innen)

Long Yuan is a Professor in the School of Artificial Intelligence at Wuhan University of Technology. He received his PhD from the University of New South Wales, Australia, and his MS and BS degrees from Sichuan University, China. His research interests include big graph analytics and graph data management. He has published extensively in leading venues, including SIGMOD, VLDB, ICDE, The Web Conference, The VLDB Journal, and IEEE Transactions on Knowledge and Data Engineering. He has served as a program committee member for major conferences, including VLDB, ICDE, The Web Conference, and CIKM. He received the ACM SIGMOD China Rising Star Award in 2022 and the Best Student Paper Award at DASFAA 2023.

Zhengyi Yang is an ARC Early Career Industry Fellow in the School of Computer Science at the University of Sydney and an Adjunct Lecturer at the University of New South Wales. He is also the Founder of Euler AI. He received his PhD from the University of New South Wales, Australia, and an MEng degree from University College London, UK. His research focuses on data management, graph analytics, and AI-driven data systems. He has published extensively in leading venues, including SIGMOD, VLDB, ICDE, HPDC, The Web Conference, and The VLDB Journal. He received the Best Paper Award at KDExLLM 2026 and Best Student Paper Awards at ADMA 2024, ADC 2022, and KSEM 2020. He has served as a program committee member for major conferences, including VLDB, ICDE, The Web Conference, KDD, and CIKM. He has also served as Program Committee Chair for LSGDA at VLDB 2024 and 2025, Program Committee Chair for D2AI at ICDM 2026, and Publication Chair for APWeb-WAIM 2024.

Wenjie Zhang is a Professor in the School of Computer Science and Engineering at the University of New South Wales, Sydney. Her main research interests lie in large-scale data management and its applications. She serves as an Associate Editor for IEEE Transactions on Knowledge and Data Engineering, The VLDB Journal, and ACM Transactions on Knowledge Discovery from Data. She has held numerous leadership roles at major international conferences, including PC Co-Chair of ICDE 2025, DASFAA 2027, APWeb-WAIM 2024, and WISE 2021; Tutorial Chair for VLDB and ICDE; Workshop Chair for ICDM; and Area Chair for VLDB and ICDE. She currently chairs the Steering Committee of the Australasian Database Conference. Her research has been recognized with the ACM SIGMOD Research Highlight Award, the CORE Chris Wallace Research Award, and 19 best-paper awards or nominations from conferences including SIGMOD and ICDE. She is an elected member of the CORE Academy and a Fellow of both the Australian Computer Society and the Royal Society of New South Wales.