Relevant bibliographies by topics / Deepwalk

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Academic literature on the topic 'Deepwalk'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Deepwalk"

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Deng, Xiaobing. "A novel dual-branch network for comprehensive spatiotemporal information integration for EEG-based epileptic seizure detection." PLOS One 20, no. 6 (2025): e0321942. https://doi.org/10.1371/journal.pone.0321942.

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Epilepsy is a neurological disorder characterized by recurrent seizures caused by abnormal brain activity, which can severely affects people’s normal lives. To improve the lives of these patients, it is necessary to develop accurate methods to predict seizures. Electroencephalography (EEG), as a non-invasive and real-time technique, is crucial for the early diagnosis of epileptic seizures by monitoring abnormal brain activity associated with seizures. Deep learning EEG-based detection methods have made significant progress, but still face challenges such as the underutilization of spatial rela
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Wei, Hao, Zhisong Pan, Guyu Hu, et al. "Attributed network representation learning via DeepWalk." Intelligent Data Analysis 23, no. 4 (2019): 877–93. http://dx.doi.org/10.3233/ida-184121.

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Guo, Jiaao, Qinghuai Liang, and Jiaqi Zhao. "F-Deepwalk: A Community Detection Model for Transport Networks." Entropy 26, no. 8 (2024): 715. http://dx.doi.org/10.3390/e26080715.

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The design of transportation networks is generally performed on the basis of the division of a metropolitan region into communities. With the combination of the scale, population density, and travel characteristics of each community, the transportation routes and stations can be more precisely determined to meet the travel demand of residents within each of the communities as well as the transportation links among communities. To accurately divide urban communities, the original word vector sampling method is improved on the classic Deepwalk model, proposing a Random Walk (RW) algorithm in whi
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Xu, Zhenzhen, Yuyuan Yuan, Haoran Wei, and Liangtian Wan. "A serendipity-biased Deepwalk for collaborators recommendation." PeerJ Computer Science 5 (March 4, 2019): e178. http://dx.doi.org/10.7717/peerj-cs.178.

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Scientific collaboration has become a common behaviour in academia. Various recommendation strategies have been designed to provide relevant collaborators for the target scholars. However, scholars are no longer satisfied with the acquainted collaborator recommendations, which may narrow their horizons. Serendipity in the recommender system has attracted increasing attention from researchers in recent years. Serendipity traditionally denotes the faculty of making surprising discoveries. The unexpected and valuable scientific discoveries in science such as X-rays and penicillin may be attribute
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Qu, Song, Yuqing Du, Mu Zhu, et al. "Dynamic Community Detection Based on Evolutionary DeepWalk." Applied Sciences 12, no. 22 (2022): 11464. http://dx.doi.org/10.3390/app122211464.

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To fully characterize the evolution process of the topological structure of dynamic communities, we propose a dynamic community detection based on Evolutionary DeepWalk (DEDW) for the high-dimensional data and dynamic characteristics. First, DEDW solves the problem of data sparseness in the process of dynamic network data representation through graph embedding. Then, DEDW uses the DeepWalk algorithm to generate node embedding feature vectors based on the characteristics of the stable change of the community structure; finally, DEDW integrates historical network structure information to generat
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Ha, Jihwan. "DeepWalk-Based Graph Embeddings for miRNA–Disease Association Prediction Using Deep Neural Network." Biomedicines 13, no. 3 (2025): 536. https://doi.org/10.3390/biomedicines13030536.

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Background: In recent years, micro ribonucleic acids (miRNAs) have been recognized as key regulators in numerous biological processes, particularly in the development and progression of diseases. As a result, extensive research has focused on uncovering the critical involvement of miRNAs in disease mechanisms to better comprehend the underlying causes of human diseases. Despite these efforts, relying solely on biological experiments to identify miRNA-disease associations is both time-consuming and costly, making it an impractical approach for large-scale studies. Methods: In this paper, we pro
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Cai, Lijun, Yongbao Xu, Tingqin He, Tao Meng, and Huimin Liu. "PROD: A New Algorithm of DeepWalk Based On Probability." Journal of Physics: Conference Series 1069 (August 2018): 012130. http://dx.doi.org/10.1088/1742-6596/1069/1/012130.

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Jian, Yang, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "Spatiotemporal DeepWalk Gated Recurrent Neural Network: A Deep Learning Framework for Traffic Learning and Forecasting." Journal of Advanced Transportation 2022 (April 18, 2022): 1–11. http://dx.doi.org/10.1155/2022/4260244.

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As a typical spatiotemporal problem, there are three main challenges in traffic forecasting. First, the road network is a nonregular topology, and it is difficult to extract complex spatial dependence accurately. Second, there are short- and long-term dependencies between traffic dates. Third, there are many other factors besides the influence of spatiotemporal dependence, such as semantic characteristics. To address these issues, we propose a spatiotemporal DeepWalk gated recurrent unit model (ST-DWGRU), a deep learning framework that fuses spatial, temporal, and semantic features for traffic
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Yang, Xin, Shuaishuai Bo, and Zhaojie Zhang. "Classifying Urban Functional Zones Based on Modeling POIs by Deepwalk." Sustainability 15, no. 10 (2023): 7995. http://dx.doi.org/10.3390/su15107995.

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Developing urban functional zone classification method to study urban spatial structure is a hotspot in current research. Using the word embedding model to excavate spatial relationship of the geographic elements in urban functional zones is an important way to develop urban functional zone classification method. However, in these studies, the spatial relationship of geographic elements was regarded as their homogeneity, while the structural similarity of geographical elements was ignored, which inevitably reduces the classification accuracy of urban functional zone classification method. This
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Sonia, Kapil Sharma, and Monika Bajaj. "DeepWalk Based Influence Maximization (DWIM): Influence Maximization Using Deep Learning." Intelligent Automation & Soft Computing 35, no. 1 (2023): 1087–101. http://dx.doi.org/10.32604/iasc.2023.026134.

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Books on the topic "Deepwalk"

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Survey, United States Geological. Deepwell Ranch quadrangle, Arizona--Cochise Co: 7.5 minute series (topographic). For sale by the Survey, 2001.

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(Editor), Philip E. Lamoreaux, and Jsroslav Vrba (Editor), eds. Hydrogeology and Management of Hazardous Waste by Deepwell Disposal (International contributions to hydrogeology). A.A. Balkema, 1990.

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Book chapters on the topic "Deepwalk"

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Yuzhi Xiao. "Text-Associated Max-Margin DeepWalk." In Big Data. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2922-7_21.

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, Yuzhi Xiao, and Zhaoyang Wang. "Improved DeepWalk Algorithm Based on Preference Random Walk." In Natural Language Processing and Chinese Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32233-5_21.

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Chen, Yunfang, Li Wang, Dehao Qi, and Wei Zhang. "Community Detection Based on DeepWalk in Large Scale Networks." In Communications in Computer and Information Science. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7530-3_43.

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Yu, Shikang, Yang Wu, Yurong Song, Guoping Jiang, and Xiaoping Su. "Application of DeepWalk Based on Hyperbolic Coordinates on Unsupervised Clustering." In Science of Cyber Security. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34637-9_8.

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Singh, Deepti, and Ankita Verma. "Extracting Community Structure in Multi-relational Network via DeepWalk and Consensus Clustering." In Intelligent Human Computer Interaction. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44689-5_21.

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Gao, Xu, Wenjia Niu, Jingjing Liu, et al. "A DeepWalk-Based Approach to Defend Profile Injection Attack in Recommendation System." In IFIP Advances in Information and Communication Technology. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-00828-4_22.

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Song, Xiao-Yu, Tong Liu, Ze-Yang Qiu, et al. "Prediction of lncRNA-Disease Associations from Heterogeneous Information Network Based on DeepWalk Embedding Model." In Intelligent Computing Methodologies. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60796-8_25.

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Wang, Yijia, Junjie Lin, Yong Tang, Chengzhe Yuan, and Luming Zhang. "Adaptive DeepWalk and Prior-Enhanced Graph Neural Network for Scholar Influence Maximization in Social Networks." In Communications in Computer and Information Science. Springer Nature Singapore, 2025. https://doi.org/10.1007/978-981-96-2373-0_17.

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Choudhary, Aniket, Anubhav Mendhiratta, Divyansh Vinayak, and Arti Arya. "Enhanced Traffic Forecasting for Urban Planning Using DeepWalk Embeddings and Spatio-Temporal Graph Ordinary Differential Equation Model." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83796-8_7.

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Wu, Anxu. "Anisotropy of Earth Tide from Deepwell Strain." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-02116-0_13.

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Conference papers on the topic "Deepwalk"

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Zebua, Hans Jonathan, Emmanuel Brandon Hamdi, and Felix Indra Kurniadi. "Unsupervised Content-Based Book Recommendation System Using DeepWalk." In 2024 International Conference on ICT for Smart Society (ICISS). IEEE, 2024. http://dx.doi.org/10.1109/iciss62896.2024.10751026.

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Kumamoto, Koh, and Yasunori Endo. "Whole Time-Series Clustering by Embedding Relational Graphs into Vector Spaces Using DeepWalk." In 2024 Joint 13th International Conference on Soft Computing and Intelligent Systems and 25th International Symposium on Advanced Intelligent Systems (SCIS&ISIS). IEEE, 2024. https://doi.org/10.1109/scisisis61014.2024.10760058.

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Xue, Yinjie, Zhiyi Zhang, Chen Liu, Shuxian Chen, and Zhiqiu Huang. "DeepWeak: Weak Mutation Testing for Deep Learning Systems." In 2024 IEEE 24th International Conference on Software Quality, Reliability and Security (QRS). IEEE, 2024. http://dx.doi.org/10.1109/qrs62785.2024.00015.

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Pinto, Allan, Gustavo Leite, Marcio Pereira, Hervé Yviquel, Sandro Rigo, and Guido Araujo. "DeepWave: A Software Stack for Parallelizing Deep Learning Models Used in Geophysics." In 2024 IEEE 36th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2024. http://dx.doi.org/10.1109/sbac-pad63648.2024.00013.

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Brooks, William (Bill) W. "Microbiologically – Influenced Corrosion Riviera Park Case Study." In CORROSION 2013. NACE International, 2013. https://doi.org/10.5006/c2013-02525.

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Abstract As part of a pipeline integrity management (PIM) assessment of the Riviera Park line north of Amarillo, Texas, we were performing an internal corrosion direct assessment (ICDA) dig to determine if internal corrosion was present due to this location being identified as a critical angle.1, 2. A critical angle is an area that could be a possible water hold up region. The pipe being examined was immediately upstream of a suspended creek crossing, and MIC was discovered on the external surface of the east leg of the crossing. Finding microbiologically influenced corrosion (MIC) in this par
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Perozzi, Bryan, Rami Al-Rfou, and Steven Skiena. "DeepWalk." In KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, 2014. http://dx.doi.org/10.1145/2623330.2623732.

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Mohammed Hamakarim, Karwan, Aland Farhad Mohammed, Rawaz Abdulrahman Faqe Mohammed, and Bawan Nawzad Hamatahir. "Using DeepWalk to Link Prediction in Subgraph." In 2023 18th International Workshop on Semantic and Social Media Adaptation & Personalization (SMAP). IEEE, 2023. http://dx.doi.org/10.1109/smap59435.2023.10255206.

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Rodriguez, Diego, and Sven Behnke. "DeepWalk: Omnidirectional Bipedal Gait by Deep Reinforcement Learning." In 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. http://dx.doi.org/10.1109/icra48506.2021.9561717.

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Chen, Fenxiao, Bin Wang, and C. C. Jay Kuo. "Deepwalk-assisted Graph PCA (DGPCA) for Language Networks." In ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. http://dx.doi.org/10.1109/icassp.2019.8682615.

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Guo, Lantian, Xiaoyan Cai, Haohua Qin, Yangming Guo, Fei Li, and Gang Tian. "Citation Recommendation with a Content-Sensitive DeepWalk Based Approach." In 2019 International Conference on Data Mining Workshops (ICDMW). IEEE, 2019. http://dx.doi.org/10.1109/icdmw.2019.00082.

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