Yadong Wang¹, Zhiwei Zhang^1,*, Pengpeng Qiao², Ye Yuan¹, Guoren Wang¹

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-22, 2026, DOI:10.32604/cmc.2025.068590 - 10 November 2025

Abstract Graph Neural Networks (GNNs) have proven highly effective for graph classification across diverse fields such as social networks, bioinformatics, and finance, due to their capability to learn complex graph structures. However, despite their success, GNNs remain vulnerable to adversarial attacks that can significantly degrade their classification accuracy. Existing adversarial attack strategies primarily rely on label information to guide the attacks, which limits their applicability in scenarios where such information is scarce or unavailable. This paper introduces an innovative unsupervised attack method for graph classification, which operates without relying on label information, thereby enhancing its applicability… More >

Thi Phuong Thao Nguyen¹, Tho Thong Nguyen¹, Huu Quynh Nguyen², Tien Duc Nguyen³, Chu Kien Nguyen¹, Nguyen Giap Cu^4,*

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4899-4920, 2025, DOI:10.32604/cmc.2025.067707 - 23 October 2025

Abstract Graph-based image classification has emerged as a powerful alternative to traditional convolutional approaches, leveraging the relational structure between image regions to improve accuracy. This paper presents an enhanced graph-based image classification framework that integrates convolutional neural network (CNN) features with graph convolutional network (GCN) learning, leveraging superpixel-based image representations. The proposed framework initiates the process by segmenting input images into significant superpixels, reducing computational complexity while preserving essential spatial structures. A pre-trained CNN backbone extracts both global and local features from these superpixels, capturing critical texture and shape information. These features are structured into a… More >

G. Naga Chandrika¹, Khalid Alnowibet², K. Sandeep Kautish³, E. Sreenivasa Reddy⁴, Adel F. Alrasheedi², Ali Wagdy Mohamed^5,6,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5707-5720, 2022, DOI:10.32604/cmc.2022.021186 - 11 October 2021

Abstract Graphs are used in various disciplines such as telecommunication, biological networks, as well as social networks. In large-scale networks, it is challenging to detect the communities by learning the distinct properties of the graph. As deep learning has made contributions in a variety of domains, we try to use deep learning techniques to mine the knowledge from large-scale graph networks. In this paper, we aim to provide a strategy for detecting communities using deep autoencoders and obtain generic neural attention to graphs. The advantages of neural attention are widely seen in the field of NLP… More >