Haocheng Sun, Ping Li, Ying Li^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4899-4914, 2025, DOI:10.32604/cmc.2025.063029 - 19 May 2025

Abstract Traffic flow prediction is a key component of intelligent transportation systems, particularly in data-scarce regions where traditional models relying on complete datasets often fail to provide accurate forecasts. These regions are characterized by limited sensor coverage and sparse data collection, pose significant challenges for existing prediction methods. To address this, we propose a novel transfer learning framework called transfer learning with deep knowledge distillation (TL-DKD), which combines graph neural network (GNN) with deep knowledge distillation to enable effective knowledge transfer from data-rich to data-scarce domains. Our contributions are three-fold: (1) We introduce, for the first… More >

Pengfei Zhang¹, Rui Xin¹, Xing Xu¹, Yuzhen Wang¹, Xiaodong Li², Xiao Zhang², Meina Song², Zhonghong Ou^3,*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5413-5428, 2025, DOI:10.32604/cmc.2025.062618 - 19 May 2025

Abstract Session-based recommendation systems (SBR) are pivotal in suggesting items by analyzing anonymized sequences of user interactions. Traditional methods, while competent, often fall short in two critical areas: they fail to address potential inter-session item transitions, which are behavioral dependencies that extend beyond individual session boundaries, and they rely on monolithic item aggregation to construct session representations. This approach does not capture the multi-scale and heterogeneous nature of user intent, leading to a decrease in modeling accuracy. To overcome these limitations, a novel approach called HMGS has been introduced. This system incorporates dual graph architectures to… More >

Shaoming Qiu, Xinchen Huang^*, Liangyu Liu, Bicong E, Jingfeng Ye

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5657-5678, 2025, DOI:10.32604/cmc.2025.062482 - 19 May 2025

Abstract Most existing knowledge graph relationship prediction methods are unable to capture the complex information of multi-relational knowledge graphs, thus overlooking key details contained in different entity pairs and making it difficult to aggregate more complex relational features. Moreover, the insufficient capture of multi-hop relational information limits the processing capability of the global structure of the graph and reduces the accuracy of the knowledge graph completion task. This paper uses graph neural networks to construct new message functions for different relations, which can be defined as the rotation from the source entity to the target entity… More >

Seyha Ros^1,#, Seungwoo Kang^1,#, Taikuong Iv¹, Inseok Song¹, Prohim Tam², Seokhoon Kim^1,3,*

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 1649-1665, 2025, DOI:10.32604/cmc.2025.062716 - 16 April 2025

Abstract Recently, Network Functions Virtualization (NFV) has become a critical resource for optimizing capability utilization in the 5G/B5G era. NFV decomposes the network resource paradigm, demonstrating the efficient utilization of Network Functions (NFs) to enable configurable service priorities and resource demands. Telecommunications Service Providers (TSPs) face challenges in network utilization, as the vast amounts of data generated by the Internet of Things (IoT) overwhelm existing infrastructures. IoT applications, which generate massive volumes of diverse data and require real-time communication, contribute to bottlenecks and congestion. In this context, Multi-access Edge Computing (MEC) is employed to support resource… More >

Dawei Xu^1,2, Fan Huang¹, Jiaxin Zhang¹, Yunfang Liang¹, Baokun Zheng^3,*, Jian Zhao¹

CMC-Computers, Materials & Continua, Vol.83, No.2, pp. 2207-2220, 2025, DOI:10.32604/cmc.2025.061455 - 16 April 2025

Abstract With the rapid proliferation of Internet of Things (IoT) devices, ensuring their communication security has become increasingly important. Blockchain and smart contract technologies, with their decentralized nature, provide strong security guarantees for IoT. However, at the same time, smart contracts themselves face numerous security challenges, among which reentrancy vulnerabilities are particularly prominent. Existing detection tools for reentrancy vulnerabilities often suffer from high false positive and false negative rates due to their reliance on identifying patterns related to specific transfer functions. To address these limitations, this paper proposes a novel detection method that combines pattern matching… More >

Aishwarya Rajasekar^*, Vetriselvi Vetrian

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 225-254, 2025, DOI:10.32604/cmes.2025.062642 - 11 April 2025

Abstract The integration of technologies like artificial intelligence, 6G, and vehicular ad-hoc networks holds great potential to meet the communication demands of the Internet of Vehicles and drive the advancement of vehicle applications. However, these advancements also generate a surge in data processing requirements, necessitating the offloading of vehicular tasks to edge servers due to the limited computational capacity of vehicles. Despite recent advancements, the robustness and scalability of the existing approaches with respect to the number of vehicles and edge servers and their resources, as well as privacy, remain a concern. In this paper, a lightweight… More >

Bolin Zhang, Ruichun Gu^*, Haiying Liu

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 2521-2535, 2025, DOI:10.32604/cmc.2024.060331 - 17 February 2025

Abstract Federated Graph Learning (FGL) enables model training without requiring each client to share local graph data, effectively breaking data silos by aggregating the training parameters from each terminal while safeguarding data privacy. Traditional FGL relies on a centralized server for model aggregation; however, this central server presents challenges such as a single point of failure and high communication overhead. Additionally, efficiently training a robust personalized local model for each client remains a significant objective in federated graph learning. To address these issues, we propose a decentralized Federated Graph Learning framework with efficient communication, termed Decentralized… More >

Baoquan Liu^1,3, Xi Chen^2,3, Qingjun Yuan^2,3, Degang Li^2,3, Chunxiang Gu^2,3,*

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3179-3201, 2025, DOI:10.32604/cmc.2024.059688 - 17 February 2025

Abstract With the emphasis on user privacy and communication security, encrypted traffic has increased dramatically, which brings great challenges to traffic classification. The classification method of encrypted traffic based on GNN can deal with encrypted traffic well. However, existing GNN-based approaches ignore the relationship between client or server packets. In this paper, we design a network traffic topology based on GCN, called Flow Mapping Graph (FMG). FMG establishes sequential edges between vertexes by the arrival order of packets and establishes jump-order edges between vertexes by connecting packets in different bursts with the same direction. It not… More >

Jiewen Liu¹, Bing Chen^1,2,*, Baolu Xue¹, Mengya Guo¹, Yuntao Xu¹

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 1857-1877, 2025, DOI:10.32604/cmc.2024.057814 - 17 February 2025

Abstract Federated Graph Neural Networks (FedGNNs) have achieved significant success in representation learning for graph data, enabling collaborative training among multiple parties without sharing their raw graph data and solving the data isolation problem faced by centralized GNNs in data-sensitive scenarios. Despite the plethora of prior work on inference attacks against centralized GNNs, the vulnerability of FedGNNs to inference attacks has not yet been widely explored. It is still unclear whether the privacy leakage risks of centralized GNNs will also be introduced in FedGNNs. To bridge this gap, we present PIAFGNN, the first property inference attack… More >

Fengyu Yang^1,2,*, Fa Zhong², Xiaohui Wei¹, Guangdong Zeng²

CMC-Computers, Materials & Continua, Vol.82, No.2, pp. 3563-3590, 2025, DOI:10.32604/cmc.2024.057697 - 17 February 2025

Abstract The primary goal of software defect prediction (SDP) is to pinpoint code modules that are likely to contain defects, thereby enabling software quality assurance teams to strategically allocate their resources and manpower. Within-project defect prediction (WPDP) is a widely used method in SDP. Despite various improvements, current methods still face challenges such as coarse-grained prediction and ineffective handling of data drift due to differences in project distribution. To address these issues, we propose a fine-grained SDP method called DIDP (drift-immune defect prediction), based on drift-immune graph neural networks (DI-GNN). DIDP converts source code into graph… More >