Relevant bibliographies by topics / Graph-based algorithm

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Graph-based algorithm'

Author: Grafiati
Published: 4 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Graph-based algorithm"

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Qiang, He Qun, Chun Hua Qian, and Sheng Rong Gong. "A Graph-Based Image Segmentation Algorithm." Advanced Materials Research 1078 (December 2014): 401–4. http://dx.doi.org/10.4028/www.scientific.net/amr.1078.401.

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According to the problem that classical graph-based image segmentation algorithms are not robust to segmentation of texture image. We propose a novel segmentation algorithm that GBCTRS, which overcame the shortcoming of existed graph-based segmentation algorithms N-cut and EGBIS. It extract feature vector of blocks using color-texture feature, calculate weight between each block using the neighborhood relationship, use minimum spanning tree method to clustering segmentation. The experimental show that the new algorithm is more efficient and robust to segment texture image and strong edges imag
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Sarno, Riyanarto, and Kelly Rossa Sungkono. "A survey of graph-based algorithms for discovering business processes." International Journal of Advances in Intelligent Informatics 5, no. 2 (2019): 137. http://dx.doi.org/10.26555/ijain.v5i2.296.

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Algorithms of process discovery help analysts to understand business processes and problems in a system by creating a process model based on a log of the system. There are existing algorithms of process discovery, namely graph-based. Of all algorithms, there are algorithms that process graph-database to depict a process model. Those algorithms claimed that those have less time complexity because of the graph-database ability to store relationships. This research analyses graph-based algorithms by measuring the time complexity and performance metrics and comparing them with a widely used algori
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Fang, Mingming, Shengen Li, and Jian Wang. "Similar Graph Face Clustering Based on Graph Convolutional Neural Network." Journal of Physics: Conference Series 2589, no. 1 (2023): 012013. http://dx.doi.org/10.1088/1742-6596/2589/1/012013.

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Abstract Face clustering is primarily a method for grouping a large number of face images and has important applications in the fields of label-free face image annotation and image management. Traditional machine learning clustering algorithms do not work well on face image data and are unable to learn effectively on complex face image features. Recent research has turned to the use of graph convolutional neural networks (GCNs) to learn contextual information from neighbourhood features between face images for inference, which can significantly improve performance. Unlike the conventional link
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Ma, Tinghuai, Qian Pan, Hongmei Wang, Wenye Shao, Yuan Tian, and Najla Al-Nabhan. "Graph classification algorithm based on graph structure embedding." Expert Systems with Applications 161 (December 2020): 113715. http://dx.doi.org/10.1016/j.eswa.2020.113715.

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Ge, Kao, Jian-Qiang Zhao, and Yan-Yong Zhao. "GR-GNN: Gated Recursion-Based Graph Neural Network Algorithm." Mathematics 10, no. 7 (2022): 1171. http://dx.doi.org/10.3390/math10071171.

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Under an internet background involving artificial intelligence and big data—unstructured, materialized, network graph-structured data, such as social networks, knowledge graphs, and compound molecules, have gradually entered into various specific business scenarios. One problem that urgently needs to be solved in the industry involves how to perform feature extractions, transformations, and operations in graph-structured data to solve downstream tasks, such as node classifications and graph classifications in actual business scenarios. Therefore, this paper proposes a gated recursion-based gra
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Miao, Fengyu, Xiuzhuang Zhou, Shungen Xiao, and Shiliang Zhang. "A Graph Similarity Algorithm Based on Graph Partitioning and Attention Mechanism." Electronics 13, no. 19 (2024): 3794. http://dx.doi.org/10.3390/electronics13193794.

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In recent years, graph similarity algorithms have been extensively developed based on neural networks. However, with an increase in the node count in graphs, these models either suffer from a reduced representation ability or face a significant increase in the computational cost. To address this issue, a graph similarity algorithm based on graph partitioning and attention mechanisms was proposed in this study. Our method first divided each input graph into the subgraphs to directly extract the local structural features. The residual graph convolution and multihead self-attention mechanisms wer
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Shang, Songtao, Wenqian Shang, Minyong Shi, Shuchao Feng, and Zhiguo Hong. "A Video Recommendation Algorithm Based on Hyperlink-Graph Model." International Journal of Software Innovation 5, no. 3 (2017): 49–63. http://dx.doi.org/10.4018/ijsi.2017070104.

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The traditional graph-based personal recommendation algorithms mainly depend the user-item model to construct a bipartite graph. However, the traditional algorithms have low efficiency, because the matrix of the algorithms is sparse and it cost lots of time to compute the similarity between users or items. Therefore, this paper proposes an improved video recommendation algorithm based on hyperlink-graph model. This method cannot only improve the accuracy of the recommendation algorithms, but also reduce the running time. Furthermore, the Internet users may have different interests, for example
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Chen, Yuzhong, Zhenyu Liu, Yulin Liu, and Chen Dong. "Distributed Attack Modeling Approach Based on Process Mining and Graph Segmentation." Entropy 22, no. 9 (2020): 1026. http://dx.doi.org/10.3390/e22091026.

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Attack graph modeling aims to generate attack models by investigating attack behaviors recorded in intrusion alerts raised in network security devices. Attack models can help network security administrators discover an attack strategy that intruders use to compromise the network and implement a timely response to security threats. However, the state-of-the-art algorithms for attack graph modeling are unable to obtain a high-level or global-oriented view of the attack strategy. To address the aforementioned issue, considering the similarity between attack behavior and workflow, we employ a heur
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Yang, Li Hua, Gui Lin Li, Shao Bin Zhou, and Ming Hong Liao. "INOD: A Graph-Based Outlier Detection Algorithm." Applied Mechanics and Materials 475-476 (December 2013): 1008–12. http://dx.doi.org/10.4028/www.scientific.net/amm.475-476.1008.

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The outlier detection is to select uncommon data from a data set, which can significantly improve the quality of results for the data mining algorithms. A typical feature of the outliers is that they are always far away from a majority of data in the data set. In this paper, we present a graph-based outlier detection algorithm named INOD, which makes use of this feature of the outlier. The DistMean-neighborhood is used to calculate the cumulative in-degree for each data. The data, whose cumulative in-degree is smaller than a threshold, is judged as an outlier candidate. A KNN-based selection a
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Chen, Hong, Ming Xin Gan, and Meng Zhao Song. "An Improved Recommendation Algorithm Based on Graph Model." Applied Mechanics and Materials 380-384 (August 2013): 1266–69. http://dx.doi.org/10.4028/www.scientific.net/amm.380-384.1266.

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According to the problem that the traditional search algorithms dont consider the needs of individuals, various recommender systems employing different data representations and recommendation methods are currently used to cope with these challenges. In this paper, inspired by the network-based user-item rating matrix, we introduce an improved algorithm which combines the similarity of items with a dynamic resource allocation process. To demonstrate its accuracy and usefulness, this paper compares the proposed algorithm with collaborative filtering algorithm using data from MovieLens. The evalu
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Dissertations / Theses on the topic "Graph-based algorithm"

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Ozturk, Gizem. "A Hybrid Veideo Recommendation System Based On A Graph Based Algorithm." Master's thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/12612624/index.pdf.

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This thesis proposes the design, development and evaluation of a hybrid video recommendation system. The proposed hybrid video recommendation system is based on a graph algorithm called Adsorption. Adsorption is a collaborative filtering algorithm in which relations between users are used to make recommendations. Adsorption is used to generate the base recommendation list. In order to overcome the problems that occur in pure collaborative system, content based filtering is injected. Content based filtering uses the idea of suggesting similar items that matches user preferences. In order to use
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Behzadi, Lila. "An improved spring-based graph embedding algorithm and LayoutShow, a Java environment for graph drawing." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/mq43368.pdf.

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Gkirtzou, Aikaterini. "Sparsity regularization and graph-based representation in medical imaging." Phd thesis, Ecole Centrale Paris, 2013. http://tel.archives-ouvertes.fr/tel-00960163.

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Medical images have been used to depict the anatomy or function. Their high-dimensionality and their non-linearity nature makes their analysis a challenging problem. In this thesis, we address the medical image analysis from the viewpoint of statistical learning theory. First, we examine regularization methods for analyzing MRI data. In this direction, we introduce a novel regularization method, the k-support regularized Support Vector Machine. This algorithm extends the 1 regularized SVM to a mixed norm of both '1 and '2 norms. We evaluate our algorithm in a neuromuscular disease classificati
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Huang, Zan. "GRAPH-BASED ANALYSIS FOR E-COMMERCE RECOMMENDATION." Diss., Tucson, Arizona : University of Arizona, 2005. http://etd.library.arizona.edu/etd/GetFileServlet?file=file:///data1/pdf/etd/azu%5Fetd%5F1167%5F1%5Fm.pdf&type=application/pdf.

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Jiahui, Yu. "Research on collaborative filtering algorithm based on knowledge graph and long tail." Thesis, Blekinge Tekniska Högskola, Institutionen för datavetenskap, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-18828.

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Background: With the popularization of the Internet and the development of information technology, the network information data has shown an explosive growth, and the problem of information overload [1] has been highlighted. In order to help users, find the information they are interested in from a large amount of information, and help information producers to let their own information be concerned by the majority of users, the recommendation system came into being.   Objectives: However, the sparseness problem, the neglect of semantic information, and the failure to consider the coverage rate
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Koushaeian, Reza. "An Ontology And Conceptual Graph Based Best Matching Algorithm For Context-aware Applications." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613216/index.pdf.

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Context-aware computing is based on using knowledge about the current context. Interpretation of current context to an understandable knowledge is carried out by reasoning over context and in some cases by matching the current context with the desired context. In this thesis we concentrated on context matching issue in context-aware computing domain. Context matching can be done in various ways like it is done in other matching processes. Our matching approach is best matching in order to generate granular similarity results and not to be limited to Boolean values. We decided to use Ontology a
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Prego, Lilach. "Algorithm for directed graph clustering, based on edge weights and the implementation on web graphs /." [S.l.] : [s.n.], 2005. http://lib.haifa.ac.il/theses/general/001344252.pdf.

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Lambert, Trystan. "Galaxy groups in the updated 2MRS using a graph-theory based friends-of-friends algorithm." Master's thesis, Faculty of Science, 2021. http://hdl.handle.net/11427/32785.

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A galaxy group catalogue for the recently-completed 2MASS Redshift Survey (2MRS, Macri et al., 2019) is presented which consists of 44572 redshifts, including 1041 new measurements for galaxies mostly located within the Zone of Avoidance. The galaxy group catalogue is generated using a novel, graph-theory based, modified version of the Friends-of-Friends algorithm. Several graph-theory examples are presented throughout this paper, and include a new method to identify substructures within groups. The results and graph-theory methods have been thoroughly interrogated against previous 2MRS group
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Trivedi, Shubhendu. "A Graph Theoretic Clustering Algorithm based on the Regularity Lemma and Strategies to Exploit Clustering for Prediction." Digital WPI, 2012. https://digitalcommons.wpi.edu/etd-theses/573.

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The fact that clustering is perhaps the most used technique for exploratory data analysis is only a semaphore that underlines its fundamental importance. The general problem statement that broadly describes clustering as the identification and classification of patterns into coherent groups also implicitly indicates it's utility in other tasks such as supervised learning. In the past decade and a half there have been two developments that have altered the landscape of research in clustering: One is improved results by the increased use of graph theoretic techniques such as spectral clustering
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Ward, Paul. "A Scalable Partial-Order Data Structure for Distributed-System Observation." Thesis, University of Waterloo, 2001. http://hdl.handle.net/10012/1161.

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Distributed-system observation is foundational to understanding and controlling distributed computations. Existing tools for distributed-system observation are constrained in the size of computation that they can observe by three fundamental problems. They lack scalable information collection, scalable data-structures for storing and querying the information collected, and scalable information-abstraction schemes. This dissertation addresses the second of these problems. Two core problems were identified in providing a scalable data structure. First, in spite of the existence of
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Books on the topic "Graph-based algorithm"

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Vathy-Fogarassy, Ágnes. Graph-Based Clustering and Data Visualization Algorithms. Springer London, 2013.

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Vathy-Fogarassy, Ágnes, and János Abonyi. Graph-Based Clustering and Data Visualization Algorithms. Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5158-6.

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Abonyi, János, and Ágnes Vathy-Fogarassy. Graph-Based Clustering and Data Visualization Algorithms. Springer, 2013.

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Abonyi, János, and Ágnes Vathy-Fogarassy. Graph-Based Clustering and Data Visualization Algorithms. Springer, 2013.

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Graph Theoretic Algorithms for the Ground Based Strategic Deterrent Program: Prioritization and Scheduling. RAND Corporation, 2021. http://dx.doi.org/10.7249/rra583-1.

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Snyder, Don, Christian Johnson, Lance Menthe, Bart E. Bennett, and Parousia Rockstroh. Graph Theoretic Algorithms for the Ground Based Strategic Deterrent Program: Prioritization and Scheduling. RAND Corporation, The, 2022.

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Coolen, Ton, Alessia Annibale, and Ekaterina Roberts. Generating Random Networks and Graphs. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198709893.001.0001.

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This book supports researchers who need to generate random networks, or who are interested in the theoretical study of random graphs. The coverage includes exponential random graphs (where the targeted probability of each network appearing in the ensemble is specified), growth algorithms (i.e. preferential attachment and the stub-joining configuration model), special constructions (e.g. geometric graphs and Watts Strogatz models) and graphs on structured spaces (e.g. multiplex networks). The presentation aims to be a complete starting point, including details of both theory and implementation,
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Book chapters on the topic "Graph-based algorithm"

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Gehrke, Marcel, Tanya Braun, and Ralf Möller. "Lifted Dynamic Junction Tree Algorithm." In Graph-Based Representation and Reasoning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91379-7_5.

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Zhang, Weidong, and Mingyue Zhang. "LSH-Based Graph Partitioning Algorithm." In Communications in Computer and Information Science. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-2122-1_5.

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Hlaoui, Adel, and Shengrui Wang. "A New Median Graph Algorithm." In Graph Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/3-540-45028-9_20.

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Carletti, Vincenzo, Pasquale Foggia, Pierluigi Ritrovato, Mario Vento, and Vincenzo Vigilante. "A Parallel Algorithm for Subgraph Isomorphism." In Graph-Based Representations in Pattern Recognition. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-20081-7_14.

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Damiand, Guillaume, Colin de la Higuera, Jean-Christophe Janodet, Émilie Samuel, and Christine Solnon. "A Polynomial Algorithm for Submap Isomorphism." In Graph-Based Representations in Pattern Recognition. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02124-4_11.

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Menéndez, Héctor, and David Camacho. "A Genetic Graph-Based Clustering Algorithm." In Intelligent Data Engineering and Automated Learning - IDEAL 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_27.

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Zhu, Ge. "Citation Graph Based Similarity Search Algorithm." In Lecture Notes in Electrical Engineering. Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4811-1_24.

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Sechen, Carl. "A Graph-Based Global Router Algorithm." In The Kluwer International Series in Engineering and Computer Science. Springer US, 1988. http://dx.doi.org/10.1007/978-1-4613-1697-8_8.

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Leutwyler, Nicolás, Mario Lezoche, Diego Torres, and Hervé Panetto. "Towards a Flexible and Scalable Data Stream Algorithm in FCA." In Graph-Based Representation and Reasoning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40960-8_9.

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Bougleux, Sébastien, Benoit Gaüzère, and Luc Brun. "A Hungarian Algorithm for Error-Correcting Graph Matching." In Graph-Based Representations in Pattern Recognition. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-58961-9_11.

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Conference papers on the topic "Graph-based algorithm"

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Qin, Dayu, Yan Yi, and Ercan E. Kuruoglu. "Graph LLM-Based Portfolio Management Algorithm." In 2025 IEEE Conference on Artificial Intelligence (CAI). IEEE, 2025. https://doi.org/10.1109/cai64502.2025.00032.

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Deng, Bowen, Lin Feng, Siwen Li, Shuo Qin, and Fancheng Yang. "BDP: Bipartite Graph Adversarial Defense Algorithm Based on Graph Purification." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650844.

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Xu, Feifei, Yumeng Zhang, and Yifei Li. "Graph Neural Network Algorithm Based on Graph Convolution and Attention Mechanism." In 2025 IEEE 5th International Conference on Power, Electronics and Computer Applications (ICPECA). IEEE, 2025. https://doi.org/10.1109/icpeca63937.2025.10928730.

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Zhu, Wenyan, Hoiio Kong, Wenzheng Cai, and Wenhao Zhu. "Traffic flow prediction based on graph attention network." In 4th International Conference on Automation Control. Algorithm and Intelligent Bionics, edited by Jing Na and Shuping He. SPIE, 2024. http://dx.doi.org/10.1117/12.3039334.

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Yang, Ran, Xiaoyan Zheng, Yiwen Shen, and Zhiya Zhou. "Community Detection Algorithm based on Graph Capsule Networks." In 2024 6th Asia Symposium on Image Processing (ASIP). IEEE, 2024. http://dx.doi.org/10.1109/asip63198.2024.00009.

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Huang, Jijie, and Yang Chen. "Parallel Neighborhood Expansion Based Graph Edge Partitioning Algorithm." In 2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS). IEEE, 2024. https://doi.org/10.1109/icpics62053.2024.10796853.

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Si, Guanlin, Min Li, Cong Hou, Yue Sun, and Lin Li. "Knowledge Graph Generation Algorithm Based on Bayesian Network." In 2025 IEEE 8th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2025. https://doi.org/10.1109/itoec63606.2025.10967734.

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Yu, Xiaoling, Ming Jiang, and Aijun Liu. "Recommendation Algorithm based on Graph Network Representation Learning." In 2025 4th International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE). IEEE, 2025. https://doi.org/10.1109/icdcece65353.2025.11034797.

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Deng, Haixia, and Shihu Liu. "Degree-Based triangle classification algorithm for graph data." In 2025 IEEE 14th Data Driven Control and Learning Systems (DDCLS). IEEE, 2025. https://doi.org/10.1109/ddcls66240.2025.11065220.

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Qiu, Shuang, Hairong Hao, and Zhou Dong. "Research on Feature Extraction Algorithm of Graph Data Based on Graph Neural Network." In 2024 6th International Conference on Frontier Technologies of Information and Computer (ICFTIC). IEEE, 2024. https://doi.org/10.1109/icftic64248.2024.10913379.

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Reports on the topic "Graph-based algorithm"

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Qi, Fei, Zhaohui Xia, Gaoyang Tang, et al. A Graph-based Evolutionary Algorithm for Automated Machine Learning. Web of Open Science, 2020. http://dx.doi.org/10.37686/ser.v1i2.77.

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As an emerging field, Automated Machine Learning (AutoML) aims to reduce or eliminate manual operations that require expertise in machine learning. In this paper, a graph-based architecture is employed to represent flexible combinations of ML models, which provides a large searching space compared to tree-based and stacking-based architectures. Based on this, an evolutionary algorithm is proposed to search for the best architecture, where the mutation and heredity operators are the key for architecture evolution. With Bayesian hyper-parameter optimization, the proposed approach can automate th
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Taha, Mohammad. Memristive Architectures and Algorithms for Approximate Graph-based Inference. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.7391.

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Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration
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Soloviev, Vladimir, Victoria Solovieva, Anna Tuliakova, Alexey Hostryk, and Lukáš Pichl. Complex networks theory and precursors of financial crashes. [б. в.], 2020. http://dx.doi.org/10.31812/123456789/4119.

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Based on the network paradigm of complexity in the work, a systematic analysis of the dynamics of the largest stock markets in the world and cryptocurrency market has been carried out. According to the algorithms of the visibility graph and recurrence plot, the daily values of stock and crypto indices are converted into a networks and multiplex networks, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. This work is the first to investigate the network properties of the crypto index CCI30 and the multiplex networ
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