Evading control flow graph based GNN malware detectors via active opcode insertion method with maliciousness preserving by Hao Peng, Zehao Yu, Dandan Zhao, Zhiguo Ding, Jieshuai Yang, Bo Zhang, Jianming Han, Xuhong Zhang, Shouling Ji, Ming Zhong

“…Abstract With the continuous advancement of machine learning, numerous malware detection methods that leverage this technology have emerged, presenting new challenges to the generation of adversarial malware. …”
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An Anomaly Node Detection Method for Wireless Sensor Networks Based on Deep Metric Learning with Fusion of Spatial–Temporal Features by Ziheng Wang, Miao Ye, Jin Cheng, Cheng Zhu, Yong Wang

“…To address these challenges, this paper presents an anomaly detection approach that integrates deep learning with metric learning. …”
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Knowledge graph reasoning: modern methods and applications by Wenguang WANG

“…Knowledge reasoning over knowledge graph aims to discover new knowledge according to the existing knowledge.It is a pivotal technology to realize the human reasoning and decision-making ability of machine.The modern methods of knowledge reasoning over knowledge graph were studied systematically.And the methods based on vector representations with a unified framework were introduced, including the methods based on embedding into Euclidean space and hyperbolic space, and based on deep learning methods such as convolution neural network, capsule network, graph neural network, etc.Simultaneously, the applications of knowledge reasoning in various technical fields and industries were presented, and the existing challenges and opportunities were pointed out as well.…”
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Comparing graphs and text: Effects of complexity and task by Sunjung Kim, Linda J. Lombardino

“…When the more complex graphic patterns were presented, the advantage of graphs over text became less apparent. …”
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The algebraic extended atom-type graph-based model for precise ligand–receptor binding affinity prediction by Farjana Tasnim Mukta, Md Masud Rana, Avery Meyer, Sally Ellingson, Duc D. Nguyen

“…Scientific Contribution The AGL-EAT-Score presents an algebraic graph-based framework that predicts ligand-receptor binding affinity by constructing multiscale weighted colored subgraphs from the 3D structure of protein-ligand complexes. …”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…This is particularly crucial in the context of the long-tail distribution problem present in the dataset, as it demonstrates the model's learning capability across all predicate categories. …”
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Effective last-mile delivery using reinforcement learning and social media-based traffic prediction in underdeveloped megacities by Luis Rabelo, Cristian Rincón-Guio, Valeria Laynes, Edgar Gutierrez-Franco, Vasanth Bhat, Juan Zamora-Aguas, Marwen Elkamel

“…Abstract This paper presents a framework for effective last-mile delivery in underdeveloped megacities by combining social media, machine learning, and reinforcement learning. …”
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Deep reinforcement learning for dynamic vehicle routing with demand and traffic uncertainty by Shirali Kadyrov, Azamkhon Azamov, Yelbek Abdumajitov, Cemil Turan

“…Experiments on synthetic grid-based routing environments show that our method outperforms classical heuristics and greedy baselines in minimizing travel cost while maintaining feasibility. The learned policies generalize to unseen demand and traffic scenarios and scale to larger graphs than those seen during training. …”
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Origin-destination prediction from road average speed data using GraphResLSTM model by Guangtong Hu, Jun Zhang

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Intelligent Waste Management Using WasteIQNet With Hierarchical Learning and Meta-Optimization by Sakshi Tiwari, Snigdha Bisht, Kanchan Sharma

“…The model integrates MobileNetV3 for semantic feature extraction with GraphSAGE to capture structural relationships among image representations. …”
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Heterogeneous Graph Neural-Network-Based Scheduling Optimization for Multi-Product and Variable-Batch Production in Flexible Job Shops by Yuxin Peng, Youlong Lyu, Jie Zhang, Ying Chu

“…In view of the Flexible Job-shop Scheduling Problem (FJSP) under multi-product and variable-batch production modes, this paper presents an intelligent scheduling approach based on a heterogeneity-enhanced graph neural network combined with deep reinforcement learning. …”
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Underwater Visual Object Tracking Method Based on Scene Perception by Qianwei HU, Daiwei WANG, Renjie LI, Xiaofan YU, Bin KANG, Ruoyu SU

“…Additionally, a dual-view graph contrastive learning strategy was introduced, which enabled unsupervised online updates for the graph convolution module by generating randomly perturbed target feature views, ensuring strong adaptability and stability of the model in complex environments. …”
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Locality-Preserving Multiprojection Discriminant Analysis by Jiajun Ma

“…Furthermore, an auto-optimized graph technique is also integrated into the discriminant analysis framework to explore the local structure of the data. …”
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STTMC: A Few-Shot Spatial Temporal Transductive Modulation Classifier by Yunhao Shi, Hua Xu, Zisen Qi, Yue Zhang, Dan Wang, Lei Jiang

“…The identification of modulation types under small sample conditions has become an increasingly urgent problem. In this paper, we present a novel few-shot AMC model named the Spatial Temporal Transductive Modulation Classifier (STTMC), which comprises two modules: a feature extraction module and a graph network module. …”
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Enhancing the performance of neurosurgery medical question-answering systems using a multi-task knowledge graph-augmented answer generation model by Ting Pan, Jiang Shen, Man Xu

“…., contextual prioritization, empathy modulation). These present challenges for further improving the semantic understanding, memory integration, and trustworthiness of intelligent Q&A systems in neurosurgery.ApproachTo address these challenges, we propose a Multi-Task Knowledge Graph-Augmented Answer Generation model (MT-KGAG), designed to enhance perceptual fidelity. …”
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EquiRank: Improved protein-protein interface quality estimation using protein language-model-informed equivariant graph neural networks by Md Hossain Shuvo, Debswapna Bhattacharya

“…Here we present EquiRank, an improved protein-protein interface quality estimation method by leveraging the strength of a symmetry-aware E(3) equivariant deep graph neural network (EGNN) and integrating pLM embeddings from the pretrained ESM-2 model. …”
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MGNN: Moment Graph Neural Network for Universal Molecular Potentials by Jian Chang, Shuze Zhu

“…We present the Moment Graph Neural Network (MGNN), a rotation-invariant message passing neural network architecture that capitalizes on the moment representation learning of 3D molecular graphs, is adept at capturing the nuanced spatial relationships inherent in three-dimensional molecular structures. …”
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Optimizing the early diagnosis of neurological disorders through the application of machine learning for predictive analytics in medical imaging by Vijaya Bhaskar Sadu, Sathvik Bagam, Mohd Naved, Siva Krishna Reddy Andluru, Kamalakar Ramineni, Meshal Ghalib Alharbi, Sudhakar Sengan, Rahmaan Khadhar Moideen

“…The present investigation introduces the STGCN-ViT, a hybrid model that integrates CNN + Spatial–Temporal Graph Convolutional Networks (STGCN) + Vision Transformer (ViT) components to address these gaps. …”
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Optimal Transport Based Graph Kernels for Drug Property Prediction by Mohammed Aburidi, Roummel Marcia

“…To overcome these obstacles, there has been a growing reliance on computational and predictive tools, leveraging recent advancements in machine learning and graph-based methodologies. This study presents an innovative approach that harnesses the power of optimal transport (OT) theory to construct three graph kernels for predicting drug ADMET properties. …”
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Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers by Dingge Liang, Junliang Li, Junping Yin

“…To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. …”
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