Research on the Construction and Application of Earthquake Emergency Information Knowledge Graph Based on Large Language Models by Wentao Zhou, Meng Huang, Shuai Liu, Qiao You, Fanxin Meng

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Center of Trapezoid Graph: Application in Selecting Center Location to Set up a Private Hospital by Shaoli Nandi, Sukumar Mondal, Sovan Samanta, Sambhu Charan Barman, Leo Mrsic, Antonios Kalampakas

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A Relationships-based Algorithm for Detecting the Communities in Social Networks by Sevda Fotovvat, Habib Izadkhah, Javad Hajipour

“…Social network research analyzes the relationships between interactions, people, organizations, and entities. …”
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Human Activity Recognition Using Graph Structures and Deep Neural Networks by Abed Al Raoof K. Bsoul

“…To address this, we applied the Firefly Optimization Algorithm to fine-tune the hyperparameters of both the graph-based model and a CNN baseline for comparison. …”
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Enhancing cybersecurity through autonomous knowledge graph construction by integrating heterogeneous data sources by Hatoon Alharbi, Ali Hur, Hasan Alkahtani, Hafiz Farooq Ahmad

“…However, constructing an efficient knowledge graph poses challenges. In our research, we construct a cybersecurity knowledge graph (CKG) autonomously using heterogeneous data sources. …”
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Abnormal link detection algorithm based on semi-local structure by Haoran SHI, Lixin JI, Shuxin LIU, Gengrun WANG

“…With the research in network science, real networks involved are becoming more and more extensive.Redundant error relationships in complex systems, or behaviors that occur deliberately for unusual purposes, such as wrong clicks on webpages, telecommunication network spying calls, have a significant impact on the analysis work based on network structure.As an important branch of graph anomaly detection, anomalous edge recognition in complex networks aims to identify abnormal edges in network structures caused by human fabrication or data collection errors.Existing methods mainly start from the perspective of structural similarity, and use the connected structure between nodes to evaluate the abnormal degree of edge connection, which easily leads to the decomposition of the network structure, and the detection accuracy is greatly affected by the network type.In response to this problem, a CNSCL algorithm was proposed, which calculated the node importance at the semi-local structure scale, analyzed different types of local structures, and quantified the contribution of edges to the overall network connectivity according to the semi-local centrality in different structures, and quantified the reliability of the edge connection by combining with the difference of node structure similarity.Since the connected edges need to be removed in the calculation process to measure the impact on the overall connectivity of the network, there was a problem that the importance of nodes needed to be repeatedly calculated.Therefore, in the calculation process, the proposed algorithm also designs a dynamic update method to reduce the computational complexity of the algorithm, so that it could be applied to large-scale networks.Compared with the existing methods on 7 real networks with different structural tightness, the experimental results show that the method has higher detection accuracy than the benchmark method under the AUC measure, and under the condition of network sparse or missing, It can still maintain a relatively stable recognition accuracy.…”
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A New AB Initio Repeats Finding Algorithm for Reference Genome by Shuaibin Lian, Ke Gong, Xiangli Zhang, Xinwu Chen

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Graph reinforcement learning-driven source-load cooperative scheduling optimization for textile production by Tianhao Tan, Tao Wu, Jie Li, Yuyuan Lan, Jinsong Bao

“…There are engineering issues caused by the traditional energy scheduling model where the energy generation (source) adjusts according to the energy consumption (load) in textile industrial parks, leading to a disconnect between the energy system and the production system, low energy efficiency, and high carbon emissions. This research proposes a graph reinforcement learning-driven source-load collaborative optimization method. …”
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A dynamic programming algorithm for generating chemical isomers based on frequency vectors by Ryota Ido, Naveed Ahmed Azam, Jianshen Zhu, Hiroshi Nagamochi, Tatsuya Akutsu

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Research on GNNs with stable learning by Wenbin Li, Wenxuan Wei, Peiyang Wang, Li Pan, Bo Yang, Yanling Xu

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Privacy-preserving distributed optimization algorithm for directed networks via state decomposition and external input by Mengjie Xu, Nuerken Saireke, Jimin Wang

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Complex Network Algorithm for Glossary Formation Context-Related Predictive Terms by Oleg Popov, Adrian Grosu, Sergey Kramarov

“…These terms were ranked using the average score of two algorithms - PageRank and HITS. The algorithm's operation was visualized using the example of generating a graph from the primary term "Quantum computing". …”
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Alleviating Cold Start in the EOSC Recommendations: Extended Page Rank Algorithm by Marcin Wolski, Antoni Klorek, Anna Kobusinska

“…To alleviate this problem, this paper discusses a graph-based recommendation approach extending the Page Rank algorithm by using a co-authorship network. …”
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Bio inspired feature selection and graph learning for sepsis risk stratification by D. Siri, Raviteja Kocherla, Sudharshan Tumkunta, Pamula Udayaraju, Krishna Chaitanya Gogineni, Gowtham Mamidisetti, Nanditha Boddu

“…Using the MIMIC-IV dataset, we employ the Wolverine Optimization Algorithm (WoOA) to select clinically relevant features, followed by a Generative Pre-Training Graph Neural Network (GPT-GNN) that models complex patient relationships through self-supervised learning. …”
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GNN-based optimization algorithm for joint user scheduling and beamforming by Shiwen HE, Jun YUAN, Zhenyu AN, Min ZHANG, Yongming HUANG, Yaoxue ZHANG

“…The coordinated multi-point (CoMP) transmission technology has the characteristics of reducing co-channel interference and improving spectral efficiency.For the CoMP technology, user scheduling (US) and beamforming (BF) design are two fundamental research problems located in the media access control layer and the physical layer, respectively.Under the consideration of user service quality requirements, the joint user US-BF optimization problem was investigated with the goal of maximizing network throughput.To overcome the problem that the traditional optimization algorithm had high computational cost and couldn’t effectively utilize the network historical data information, a joint US and power allocation (M-JEEPON) model based on graph neural network was proposed to realize joint US-BF optimization by combining the beam vector analytical solution.The simulation results show that the proposed algorithm can achieve the performance matching or even better than traditional optimization algorithms with lower computational overhead.…”
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A Comprehensive Review of Path-Planning Algorithms for Planetary Rover Exploration by Qingliang Miao, Guangfei Wei

“…Finally, we synthesize key insights from existing algorithms and discuss future research directions, highlighting their potential applications in planetary exploration missions.…”
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Researches on the problem of link disjoint paths pair with QoS constraints by ZHANG Pin 1, ZHANG Jian-wu1, LI Le-min 2, WANG Sheng2

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Research on dynamic spectrum resource allocation in mobile base station networks by Ming-jie HE, Yong-jun XU, Feng WANG, Wei WU, Chao-nong XU

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Application of the spectral bisection algorithm for the analysis of criminal communities in social networks by K. M. Bondar, V. S. Dunin, P. B. Skripko, N. S. Khokhlov

“…The methods of scientific research used in the work include: analysis of social networks, methods based on graph decomposition algorithms, methods of data analysis from open sources, methods of linear algebra and algorithmization.Result. …”
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Urban intersection traffic flow prediction: A physics-guided stepwise framework utilizing spatio-temporal graph neural network algorithms by Yuyan Annie Pan, Fuliang Li, Anran Li, Zhiqiang Niu, Zhen Liu

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