PathoGraph: A Graph-Based Method for Standardized Representation of Pathology Knowledge by Peiliang Lou, Yuxin Dong, Caixia Ding, Chunbao Wang, Ruifeng Guo, XiaoBo Pang, Chen Wang, Chen Li

“…Furthermore, we validate its computational utility by demonstrating the feasibility of large-scale automated PathoGraph construction, showing performance improvements in downstream deep learning tasks, and presenting two illustrative use cases that highlight its clinical potential. …”
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DGTM: Deriving Graph from transformer with Mamba for panoptic scene graph generation by Youxuan Sun, Yunliang Chen, Xiaohui Huang, Yuewei Wang, Shaoqian Chen, Kangfei Yao, Ao Yang

“…Scene Graph Generation (SGG) transforms images into structured graph representations that encapsulate the objects, attributes, and relationships present within objects. …”
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Optimizing Personalized Recommender Systems for Teachers’ Digital Learning Models Using Deep Learning Algorithms by Jun Zhong, Wenjuan Zhang

“…To address this issue, this paper proposes a personalized recommendation algorithm based on Graph Neural Networks (PRAGNN) for teachers’ digital learning models. …”
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Human Activity Recognition Using Graph Structures and Deep Neural Networks by Abed Al Raoof K. Bsoul

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Global confidence degree based graph neural network for financial fraud detection by Jiaxun Liu, Yue Tian, Guanjun Liu

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Predicting phage-host interaction via hyperbolic Poincaré graph embedding and large-scale protein language technique by Jie Pan, Rui Wang, Wenjing Liu, Li Wang, Zhuhong You, Yuechao Li, Zhemeng Duan, Qinghua Huang, Jie Feng, Yanmei Sun, Shiwei Wang

“…Existing computational tools often fail to accurately identify phages across different bacterial species. In this study, we present GE-PHI, a machine-learning-based model for predicting phage-host interactions (PHIs) by integrating knowledge graph embedding algorithm with a large-scale protein language model. …”
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Detecting sarcasm in user-generated content integrating transformers and gated graph neural networks by Zhenkai Qin, Qining Luo, Zhidong Zang, Hongpeng Fu

“…BERT is utilized to extract deep contextual information from the text, while GGNN is employed to learn global semantic structures by incorporating dependency and emotion graphs. …”
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Text Geolocation Prediction via Self-Supervised Learning by Yuxing Wu, Zhuang Zeng, Kaiyue Liu, Zhouzheng Xu, Yaqin Ye, Shunping Zhou, Huangbao Yao, Shengwen Li

“…Specifically, GeoSG integrates spatial distance and hierarchical constraints to characterize the interactions of POIs and text in a geographic relationship graph. And it designs two self-supervised tasks to train a shared network to learn the relationships among POIs and texts. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

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Large Language Models Meet Graph Neural Networks: A Perspective of Graph Mining by Yuxin You, Zhen Liu, Xiangchao Wen, Yongtao Zhang, Wei Ai

“…Graph mining is an important area in data mining and machine learning that involves extracting valuable information from graph-structured data. …”
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Topological Sequences Connected With Inverse Graphs of Finite Flexible Weak Inverse Property Quasigroups: An Approach From Polynomials to Machine Learning by Faizah D. Alanazi

“…This manuscript studies the relationship between topological sequences Tf and inverse graphs ΓCλ×Z3,⊙ of finite flexible weak inverse property quasigroups, and it presents a new computational framework with applications ranging from polynomials to machine learning. …”
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A reinforcement learning approach to effective forecasting of pediatric hypoglycemia in diabetes I patients using an extended de Bruijn graph by Mert Onur Cakiroglu, Hasan Kurban, Lilia Aljihmani, Khalid Qaraqe, Goran Petrovski, Mehmet M. Dalkilic

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A topology-guided high-quality solution learning framework for security-constraint unit commitment based on graph convolutional network by Liqian Gao, Lishen Wei, Shichang Cui, Jiakun Fang, Xiaomeng Ai, Wei Yao, Jinyu Wen

“…Firstly, a GCN-based method is presented to learn the potential patterns between commitments and graph data associated with bus feature and power grid topology. …”
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An inductive learning-based method for predicting drug-gene interactions using a multi-relational drug-disease-gene graph by Jian He, Yanling Wu, Linxi Yuan, Jiangguo Qiu, Menglong Li, Xuemei Pu, Yanzhi Guo

“…In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. …”
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Optimizing Language Model-Based Educational Assistants Using Knowledge Graphs: Integration With Moodle LMS by William Villegas-Ch, Jaime Govea, Rommel Gutierrez

“…The system also demonstrates adaptability, effectively adjusting to students’ learning styles and academic levels. This work indicates that knowledge graph integration and hyperparameter optimization are crucial to improving educational chatbots’ precision, speed, and adaptability, presenting an innovative solution that overcomes the limitations of current systems.…”
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Multi-Attribute Graph Estimation With Sparse-Group Non-Convex Penalties by Jitendra K. Tugnait

“…In this paper we provide a unified theoretical analysis of multi-attribute graph learning using a penalized log-likelihood objective function. …”
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Risk Assessment of Renewable Energy Power Systems via Graph Multi-Attention Networks by BAI Yunpeng, ZHANG Zhiyan, XU Cai, GUO Chuangxin, LIU Zhuping, ZHU Wenhao

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Visual explainable artificial intelligence for graph-based visual question answering and scene graph curation by Sebastian Künzel, Tanja Munz-Körner, Pascal Tilli, Noel Schäfer, Sandeep Vidyapu, Ngoc Thang Vu, Daniel Weiskopf

“…Abstract This study presents a novel visualization approach to explainable artificial intelligence for graph-based visual question answering (VQA) systems. …”
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PS-GCN: psycholinguistic graph and sentiment semantic fused graph convolutional networks for personality detection by Wenjuan Liu, Zhengyan Sun, Subo Wei, Shunxiang Zhang, Guangli Zhu, Lei Chen

“…To address this issue, this paper presents PS-GCN, a model integrating Psychological knowledge and Sentiment semantic features through Graph Convolution Networks. …”
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Intrusion Detection in IoT Networks Using Dynamic Graph Modeling and Graph-Based Neural Networks by William Villegas-Ch, Jaime Govea, Alexandra Maldonado Navarro, Pablo Palacios Jativa

“…To address this problem, this study proposes a graph-based intrusion detection model using Graph Neural Networks (GNNs), leveraging a dynamic representation of IoT network traffic. …”
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