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Journal articles on the topic 'Deepwalk'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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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2

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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8

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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10

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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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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12

Wang, Xun, Fuyu Wang, Xinzeng Wang, Sibo Qiao, and Yu Zhuang. "DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction." Journal of Sensors 2021 (October 16, 2021): 1–8. http://dx.doi.org/10.1155/2021/9678747.

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miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node fro

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Tie, Jiaojiao, Xiujuan Lei, and Yi Pan. "Metabolite-disease association prediction algorithm combining DeepWalk and random forest." Tsinghua Science and Technology 27, no. 1 (2022): 58–67. http://dx.doi.org/10.26599/tst.2021.9010003.

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14

Wang, Jiamiao, Xindong Wu, and Lei Li. "A framework for semantic connection based topic evolution with DeepWalk." Intelligent Data Analysis 22, no. 1 (2018): 211–37. http://dx.doi.org/10.3233/ida-163282.

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15

Chen, Yunfang, Li Wang, Dehao Qi, Tinghuai Ma, and Wei Zhang. "Community Detection Based on DeepWalk Model in Large-Scale Networks." Security and Communication Networks 2020 (November 20, 2020): 1–13. http://dx.doi.org/10.1155/2020/8845942.

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The large-scale and complex structure of real networks brings enormous challenges to traditional community detection methods. In order to detect community structure in large-scale networks more accurately and efficiently, we propose a community detection algorithm based on the network embedding representation method. Firstly, in order to solve the scarce problem of network data, this paper uses the DeepWalk model to embed a high-dimensional network into low-dimensional space with topology information. Then, low-dimensional data are processed, with each node treated as a sample and each dimensi

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Varnamkhasti, Mohammad Mahmoodi. "Persian readability classification using DeepWalk and tree-based ensemble methods." Natural Language Processing Journal 9 (December 2024): 100116. http://dx.doi.org/10.1016/j.nlp.2024.100116.

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Yang, Ce, Jiaming Guo, Hong Wen, and Weihong Huang. "A collaborative filtering recommendation algorithm based on DeepWalk and self-attention." International Journal of Computational Science and Engineering 1, no. 1 (2022): 1. http://dx.doi.org/10.1504/ijcse.2022.10050515.

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Cai, Lijun, Jibin Wang, Tingqin He, Tao Meng, and Qi Li. "A Novel Link Prediction Algorithm Based on Deepwalk and Clustering Method." Journal of Physics: Conference Series 1069 (August 2018): 012131. http://dx.doi.org/10.1088/1742-6596/1069/1/012131.

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19

Li, Guanghui, Jiawei Luo, Diancheng Wang, et al. "Potential circRNA-disease association prediction using DeepWalk and network consistency projection." Journal of Biomedical Informatics 112 (December 2020): 103624. http://dx.doi.org/10.1016/j.jbi.2020.103624.

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20

Li, Guanghui, Jiawei Luo, Qiu Xiao, Cheng Liang, Pingjian Ding, and Buwen Cao. "Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk." IEEE Access 5 (2017): 24032–39. http://dx.doi.org/10.1109/access.2017.2766758.

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Guo, Jiaming, Hong Wen, Weihong Huang, and Ce Yang. "A collaborative filtering recommendation algorithm based on DeepWalk and self-attention." International Journal of Computational Science and Engineering 26, no. 3 (2023): 296–304. http://dx.doi.org/10.1504/ijcse.2023.131503.

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BENBATATA, Sabrina, and Bilal SAOUD. "Network Embedding Methods: Study and Comparison." Electrotehnica, Electronica, Automatica 72, no. 4 (2024): 72–78. https://doi.org/10.46904/eea.24.72.4.1108008.

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Graph is a powerful language that can model many systems in different fields such as information sciences, social sciences, Biology, mathematics, physics, etc. Graphs can capture very well the relationships between nodes and their structure. Representing data through graphs has some limitations and is challenging to use them like input in machine learning and deep learning models. This challenge can be overcome by using network embedding. Embedding represents a network into low-dimensional vector space. Several methods have been proposed to embed networks. Methods like DeepWalk, Node2Vec and G

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23

Wang, Jiahui, Kun Yue, Liang Duan, Zhiwei Qi, and Shaojie Qiao. "An efficient approach for multiple probabilistic inferences with Deepwalk based Bayesian network embedding." Knowledge-Based Systems 239 (March 2022): 107996. http://dx.doi.org/10.1016/j.knosys.2021.107996.

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Yang, Kang, and Jinghua Zhu. "Next POI Recommendation via Graph Embedding Representation From H-Deepwalk on Hybrid Network." IEEE Access 7 (2019): 171105–13. http://dx.doi.org/10.1109/access.2019.2956138.

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25

Bi, Jingshu, Yuanjie Zheng, Fang Yan, Sujuan Hou, and Chengjiang Li. "Prediction of Epitope-Associated TCR by Using Network Topological Similarity Based on Deepwalk." IEEE Access 7 (2019): 151273–81. http://dx.doi.org/10.1109/access.2019.2948178.

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26

Zhang, Na, and Yuanyuan Zou. "Construction and Prediction of Students’ Multiattribute Social Network Based on Psychological Big Data Analysis." Mobile Information Systems 2022 (July 31, 2022): 1–8. http://dx.doi.org/10.1155/2022/5287364.

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In view of the limitations of the current research on students’ single attribute psychological problems. In this paper, a multiattribute social network model was constructed based on students’ social data and psychological label data, and the improved MANE algorithm was used to solve the problem to predict students’ psychological problems. In addition, DeepWalk and Node2vec network embedding algorithms were used to embed students’ multiattribute social network, respectively, so to verify the effectiveness of the model. Finally, based on the prediction model of students’ psychological problems,

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Bhavan, Anjali, Mohit Sharma, Ramit Sawhney, and Rajiv Ratn Shah. "Analysis of Parliamentary Debate Transcripts Using Community-Based Graphical Approaches (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 10 (2020): 13753–54. http://dx.doi.org/10.1609/aaai.v34i10.7148.

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Gauging political sentiments and analyzing stances of elected representatives pose an important challenge today, and one with wide-ranging ramifications. Community-based analysis of parliamentary debate sentiments could pave a way for better insights into the political happenings of a nation and help in keeping the voters informed. Such analysis could be given another dimension by studying the underlying connections and networks in such data. We present a sentiment classification method for UK Parliament debate transcripts, which is a combination of a graphical method based on DeepWalk embeddi

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Xu, Xin, Xinya Lu, and Jianan Wang. "DeeWaNA: An Unsupervised Network Representation Learning Framework Integrating Deepwalk and Neighborhood Aggregation for Node Classification." Entropy 27, no. 3 (2025): 322. https://doi.org/10.3390/e27030322.

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This paper introduces DeeWaNA, an unsupervised network representation learning framework that unifies random walk strategies and neighborhood aggregation mechanisms to improve node classification performance. Unlike existing methods that treat these two paradigms separately, our approach integrates them into a cohesive model, addressing limitations in structural feature extraction and neighborhood relationship modeling. DeeWaNA first leverages DeepWalk to capture global structural information and then employs an attention-based weighting mechanism to refine neighborhood relationships through a

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, and Yu Zhu. "Multi-View Network Representation Learning Algorithm Research." Algorithms 12, no. 3 (2019): 62. http://dx.doi.org/10.3390/a12030062.

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Network representation learning is a key research field in network data mining. In this paper, we propose a novel multi-view network representation algorithm (MVNR), which embeds multi-scale relations of network vertices into the low dimensional representation space. In contrast to existing approaches, MVNR explicitly encodes higher order information using k-step networks. In addition, we introduce the matrix forest index as a kind of network feature, which can be applied to balance the representation weights of different network views. We also research the relevance amongst MVNR and several e

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Wong, Leon, Zhu-Hong You, Zhen-Hao Guo, Hai-Cheng Yi, Zhan-Heng Chen, and Mei-Yuan Cao. "MIPDH: A Novel Computational Model for Predicting microRNA–mRNA Interactions by DeepWalk on a Heterogeneous Network." ACS Omega 5, no. 28 (2020): 17022–32. http://dx.doi.org/10.1021/acsomega.9b04195.

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31

Yan, Chaokun, Luping Feng, Wenxiu Wang, Jianlin Wang, Ge Zhang, and Junwei Luo. "A Novel Drug Repositioning Approach Based on Integrative Multiple Similarity Measures." Current Molecular Medicine 20, no. 6 (2020): 442–51. http://dx.doi.org/10.2174/1566524019666191115103307.

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Background: Drug repositioning refers to discovering new indications for the existing drugs, which can improve the efficiency of drug research and development. Methods: In this work, a novel drug repositioning approach based on integrative multiple similarity measure, called DR_IMSM, is proposed. The process of integrative similarity measure contains three steps. First, a heterogeneous network can be constructed based on known drug-disease association, shared entities information for drug pairwise and diseases pairwise. Second, a deep learning method, DeepWalk, is used to capture the topology

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Yang, Jian, Jinhong Li, Lu Wei, Lei Gao, and Fuqi Mao. "ST-AGRNN: A Spatio-Temporal Attention-Gated Recurrent Neural Network for Traffic State Forecasting." Journal of Advanced Transportation 2022 (October 3, 2022): 1–17. http://dx.doi.org/10.1155/2022/2806183.

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Accurate traffic state prediction plays an important role in traffic guidance, travel planning, etc. Due to the existence of complex spatio-temporal relationships, there are some challenges in forecasting. Firstly, in terms of spatial correlation, some models only consider the road network structure information, and ignore the relative location relationships between nodes. Secondly, some models ignore the different impacts of nodes in the global road network on traffic. To solve these problems, we propose a new traffic state-forecasting model, namely, spatio-temporal attention-gated recurrent

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Dallmann, Alexander, Thomas Niebler, Florian Lemmerich, and Andreas Hotho. "Extracting Semantics from Random Walks on Wikipedia: Comparing Learning and Counting Methods." Proceedings of the International AAAI Conference on Web and Social Media 10, no. 2 (2021): 33–40. http://dx.doi.org/10.1609/icwsm.v10i2.14831.

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Semantic relatedness between words has been extracted from a variety of sources.In this ongoing work, we explore and compare several options for determining if semantic relatedness can be extracted from navigation structures in Wikipedia. In that direction, we first investigate the potential of representation learning techniques such as DeepWalk in comparison to previously applied methods based on counting co-occurrences. Since both methods are based on (random) paths in the network, we also study different approaches to generate paths from Wikipedia link structure. For this task, we do not on

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Zhao, Zijuan, Kai Yang, and Jinli Guo. "Link Prediction with Hypergraphs via Network Embedding." Applied Sciences 13, no. 1 (2022): 523. http://dx.doi.org/10.3390/app13010523.

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Network embedding is a promising field and is important for various network analysis tasks, such as link prediction, node classification, community detection and others. Most research studies on link prediction focus on simple networks and pay little attention to hypergraphs that provide a natural way to represent complex higher-order relationships. In this paper, we propose a link prediction method with hypergraphs using network embedding (HNE). HNE adapts a traditional network embedding method, Deepwalk, to link prediction in hypergraphs. Firstly, the hypergraph model is constructed based on

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35

Zhang, Qi, Zufan Zhang, Maobin Yang, and Lianxiang Zhu. "Exploring Coevolution of Emotional Contagion and Behavior for Microblog Sentiment Analysis: A Deep Learning Architecture." Complexity 2021 (January 22, 2021): 1–10. http://dx.doi.org/10.1155/2021/6630811.

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This paper aims to explore coevolution of emotional contagion and behavior for microblog sentiment analysis. Accordingly, a deep learning architecture (denoted as MSA-UITC) is proposed for the target microblog. Firstly, the coevolution of emotional contagion and behavior is described by the tie strength between microblogs, that is, with the spread of emotional contagion, user behavior such as emotional expression will be affected. Then, based on user interaction and the correlation with target microblog, the Hawkes process is adopted to quantify the tie strength between microblogs so as to bui

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Chen, Jing, Haitong Zhao, Xinyu Yang, Mingxin Liu, Zeren Yu, and Miaomiao Liu. "Community Evolution Prediction Based on Multivariate Feature Sets and Potential Structural Features." Mathematics 10, no. 20 (2022): 3802. http://dx.doi.org/10.3390/math10203802.

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The current study on community evolution prediction ignores the problem of internal community topology characteristics and does not take feature sets extraction into account. Therefore, the MF-PSF (Multivariate Feature sets and Potential Structural Features) method based on multivariate feature sets and potential structural features for community evolution prediction is proposed in this paper. Firstly, the multivariate feature sets are built from four aspects: community core node features, community structural features, community sequential features and community behavior features. Secondly, t

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Zhang, Rui, and Xin Li. "Graph Convolutional Networks Guided by Explicitly Estimated Homophily and Heterophily Degree." Applied Sciences 12, no. 20 (2022): 10579. http://dx.doi.org/10.3390/app122010579.

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Graph convolutional networks (GCNs) have been successfully applied to learning tasks on graph-structured data. However, most traditional GCNs based on graph convolutions assume homophily in graphs, which leads to a poor performance when dealing with heterophilic graphs. Although many novel methods have recently been proposed to deal with heterophily, the effect of homophily and heterophily on classifying node pairs is not clearly separated in existing approaches and inevitably influences each other. To deal with various types of graphs more accurately, in this work we propose a new GCN-based m

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Wang, Wentao, Lintao Wu, Ye Huang, Hao Wang, and Rongbo Zhu. "Link Prediction Based on Deep Convolutional Neural Network." Information 10, no. 5 (2019): 172. http://dx.doi.org/10.3390/info10050172.

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In recent years, endless link prediction algorithms based on network representation learning have emerged. Network representation learning mainly constructs feature vectors by capturing the neighborhood structure information of network nodes for link prediction. However, this type of algorithm only focuses on learning topology information from the simple neighbor network node. For example, DeepWalk takes a random walk path as the neighborhood of nodes. In addition, such algorithms only take advantage of the potential features of nodes, but the explicit features of nodes play a good role in lin

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Ye, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data." Mathematics 7, no. 5 (2019): 460. http://dx.doi.org/10.3390/math7050460.

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Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE.

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Bai, Yun, Suling Jia, Shuangzhe Wang, and Binkai Tan. "Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network." Information 11, no. 3 (2020): 171. http://dx.doi.org/10.3390/info11030171.

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Inferring customers’ preferences and recommending suitable products is a challenging task for companies, although recommender systems are constantly evolving. Loyalty is an indicator that measures the preference relationship between customers and products in the field of marketing. To this end, the aim of this study is to explore whether customer loyalty can improve the accuracy of the recommender system. Two algorithms based on complex networks are proposed: a recommendation algorithm based on bipartite graph and PersonalRank (BGPR), and a recommendation algorithm based on single vertex set n

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Lombardo, Gianfranco, and Agostino Poggi. "ActorNode2Vec: An Actor-based solution for Node Embedding over large networks." Intelligenza Artificiale 14, no. 1 (2020): 103–14. http://dx.doi.org/10.3233/ia-190038.

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The application of Machine Learning techniques over networks, such as prediction tasks over nodes and edges, is becoming often crucial in the analysis of Complex systems in a wide range of research fields. One of the enabling technologies in that sense is represented by Node Embedding, which enables us to learn features automatically over the network. Among the different approaches proposed in the literature, the most promising are DeepWalk and Node2Vec, where the embedding is computed by combining random walks and neural language models. However, characteristic limitations with these techniqu

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Lu, Shuyi, Yuanjie Zheng, Rong Luo, Weikuan Jia, Jian Lian, and Chengjiang Li. "Density Peak Clustering Algorithm Considering Topological Features." Electronics 9, no. 3 (2020): 459. http://dx.doi.org/10.3390/electronics9030459.

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The clustering algorithm plays an important role in data mining and image processing. The breakthrough of algorithm precision and method directly affects the direction and progress of the following research. At present, types of clustering algorithms are mainly divided into hierarchical, density-based, grid-based and model-based ones. This paper mainly studies the Clustering by Fast Search and Find of Density Peaks (CFSFDP) algorithm, which is a new clustering method based on density. The algorithm has the characteristics of no iterative process, few parameters and high precision. However, we

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Khajehnejad, Ahmad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, and Baharan Mirzasoleiman. "CrossWalk: Fairness-Enhanced Node Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 11 (2022): 11963–70. http://dx.doi.org/10.1609/aaai.v36i11.21454.

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The potential for machine learning systems to amplify social inequities and unfairness is receiving increasing popular and academic attention. Much recent work has focused on developing algorithmic tools to assess and mitigate such unfairness. However, there is little work on enhancing fairness in graph algorithms. Here, we develop a simple, effective and general method, CrossWalk, that enhances fairness of various graph algorithms, including influence maximization, link prediction and node classification, applied to node embeddings. CrossWalk is applicable to any random walk based node repres

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Sun, Shixuan, Yuhang Chen, Shengliang Lu, Bingsheng He, and Yuchen Li. "ThunderRW." Proceedings of the VLDB Endowment 14, no. 11 (2021): 1992–2005. http://dx.doi.org/10.14778/3476249.3476257.

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As random walk is a powerful tool in many graph processing, mining and learning applications, this paper proposes an efficient in-memory random walk engine named ThunderRW. Compared with existing parallel systems on improving the performance of a single graph operation, ThunderRW supports massive parallel random walks. The core design of ThunderRW is motivated by our profiling results: common RW algorithms have as high as 73.1% CPU pipeline slots stalled due to irregular memory access, which suffers significantly more memory stalls than the conventional graph workloads such as BFS and SSSP. To

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Chao, Jinbo, Chunhui Zhao, and Fuzhi Zhang. "Network Embedding-Based Approach for Detecting Collusive Spamming Groups on E-Commerce Platforms." Security and Communication Networks 2022 (January 11, 2022): 1–13. http://dx.doi.org/10.1155/2022/4354086.

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Information security is one of the key issues in e-commerce Internet of Things (IoT) platform research. The collusive spamming groups on e-commerce platforms can write a large number of fake reviews over a period of time for the evaluated products, which seriously affect the purchase decision behaviors of consumers and destroy the fair competition environment among merchants. To address this problem, we propose a network embedding based approach to detect collusive spamming groups. First, we use the idea of a meta-graph to construct a heterogeneous information network based on the user review

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Zhao, Kuo, Huajian Zhang, Jiaxin Li, et al. "Social Network Forensics Analysis Model Based on Network Representation Learning." Entropy 26, no. 7 (2024): 579. http://dx.doi.org/10.3390/e26070579.

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The rapid evolution of computer technology and social networks has led to massive data generation through interpersonal communications, necessitating improved methods for information mining and relational analysis in areas such as criminal activity. This paper introduces a Social Network Forensic Analysis model that employs network representation learning to identify and analyze key figures within criminal networks, including leadership structures. The model incorporates traditional web forensics and community algorithms, utilizing concepts such as centrality and similarity measures and integr

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Pratama, Yudistira Bagus, and Haiyudi Haiyudi. "Knowledge Graph Analysis On English Wikipedia Pages Using A Deep Learning Algorithm." IT Journal Research and Development 8, no. 2 (2024): 175–86. http://dx.doi.org/10.25299/itjrd.2023.13459.

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Analysis of social networks or online communities can be very difficult when working on large networks, as many measurements require expensive hardware. For example, identifying the community structure of a network is a very computationally expensive task. Embedded graph is a way to represent graphs with vectors, so that further analysis becomes easier. The purpose of this research is to analyze the knowledge graph from the wikipedia article data. This research aims to implement web scraping techniques on the wikipedia article search engine and display similar wikipedia pages and analyze them

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Zhao, Bo-Wei, Zhu-Hong You, Lun Hu, et al. "A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning." Cancers 13, no. 9 (2021): 2111. http://dx.doi.org/10.3390/cancers13092111.

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Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN);

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Chae, Dong-Kyu, Sung-Jun Park, Eujeanne Kim, Jiwon Hong, and Sang-Wook Kim. "Identifying the Author Group of Malwares through Graph Embedding and Human-in-the-Loop Classification." Applied Sciences 11, no. 14 (2021): 6640. http://dx.doi.org/10.3390/app11146640.

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Malware are developed for various types of malicious attacks, e.g., to gain access to a user’s private information or control of the computer system. The identification and classification of malware has been extensively studied in academic societies and many companies. Beyond the traditional research areas in this field, including malware detection, malware propagation analysis, and malware family clustering, this paper focuses on identifying the “author group” of a given malware as a means of effective detection and prevention of further malware threats, along with providing evidence for prop

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Jana, Abhik, Gopalakrishnan Venkatesh, Seid Muhie Yimam, and Chris Biemann. "Hypernymy Detection for Low-resource Languages: A Study for Hindi, Bengali, and Amharic." ACM Transactions on Asian and Low-Resource Language Information Processing 21, no. 4 (2022): 1–21. http://dx.doi.org/10.1145/3490389.

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Numerous attempts for hypernymy relation (e.g., dog “is-a” animal) detection have been made for resourceful languages like English, whereas efforts made for low-resource languages are scarce primarily due to lack of gold-standard datasets and suitable distributional models. Therefore, we introduce four gold-standard datasets for hypernymy detection for each of the two languages, namely, Hindi and Bengali, and two gold-standard datasets for Amharic. Another major contribution of this work is to prepare distributional thesaurus (DT) embeddings for all three languages using three different networ

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