Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)

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Comparative Analysis of Heap Algorithms for Anomaly Localization in Dynamic Graph Neural Networks

Authors
Kiyas Mahmud1, Tauhid Sarker1, Syeda Shakira Akter1, Md. Maruf Hossain Munna1, Kazi Redwan1, Md. Faruk Abdullah Al Sohan1, *
1American International University-Bangladesh, Dhaka, Bangladesh
*Corresponding author. Email: faruk.sohan@aiub.edu
Corresponding Author
Md. Faruk Abdullah Al Sohan
Available Online 18 November 2025.
DOI
10.2991/978-94-6463-884-4_80How to use a DOI?
Keywords
Graph Neural Network; Real-Time Processing; Dynamic Graph; Heap Algorithm; Dijkstra’s Algorithm
Abstract

Anomaly localization in dynamic graph neural networks (DGNNs) is very important to detect faults and abnormalities in complex systems like water supply and electricity networks. Online anomaly detection is demanding due to computational overheads during graph traversal in Dijkstra’s Algorithm. In this research, three heap-based priority queues such as Fibonacci Heap, D-ary Heap, and Priority Queue Heap are evaluated within Dijkstra’s Algorithm for optimizing anomaly localization. Using GPerftools for monitoring memory allocation and <Chrono> for assessing execution time, this study determines the most suitable heap structure to utilize in managing large-scale dynamic graphs. The results show that heaps’ selection greatly enhances the execution speed, memory utilization, and scalability in DGNN-based systems. Optimization enables real-time anomaly detection to be more effective, thus rendering critical infrastructure networks more resilient and secure.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
Series
Advances in Engineering Research
Publication Date
18 November 2025
ISBN
978-94-6463-884-4
ISSN
2352-5401
DOI
10.2991/978-94-6463-884-4_80How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Kiyas Mahmud
AU  - Tauhid Sarker
AU  - Syeda Shakira Akter
AU  - Md. Maruf Hossain Munna
AU  - Kazi Redwan
AU  - Md. Faruk Abdullah Al Sohan
PY  - 2025
DA  - 2025/11/18
TI  - Comparative Analysis of Heap Algorithms for Anomaly Localization in Dynamic Graph Neural Networks
BT  - Proceedings of the 8th International Conference on Engineering Research, Innovation, and Education 2025 (ICERIE 2025)
PB  - Atlantis Press
SP  - 663
EP  - 671
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-884-4_80
DO  - 10.2991/978-94-6463-884-4_80
ID  - Mahmud2025
ER  -