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Journal articles on the topic 'Graph-based algorithm'

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Published: 4 June 2025

Last updated: 1 August 2025

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1

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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Dang, Vu-Tuan, Viet-Vu Vu, Hong-Quan Do, and Thi Kieu Oanh Le. "GRAPH BASED CLUSTERING WITH CONSTRAINTS AND ACTIVE LEARNING." Journal of Computer Science and Cybernetics 37, no. 1 (2021): 71–89. http://dx.doi.org/10.15625/1813-9663/37/1/15773.

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During the past few years, semi-supervised clustering has emerged as a new interesting direction in machine learning research. In a semi-supervised clustering algorithm, the clustering results can be significantly improved by using side information, which is available or collected from users. There are two main kinds of side information that can be learned in semi-supervised clustering algorithms: the class labels - called seeds or the pairwise constraints. The first semi-supervised clustering was introduced in 2000, and since that, many algorithms have been presented in literature. However, i

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Kureichik, V. V., and Vl Vl Kureichik. "Graph Partitioning Based on the Combined Approach." INFORMACIONNYE TEHNOLOGII 26, no. 12 (2020): 667–72. http://dx.doi.org/10.17587/it.26.667-672.

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The article considers one of the most important combinatorial optimization problems — the problem of graph partitioning. It belongs to the class of NP-complex optimization problems. The article presents the partitioning problem statement. Due to the complexity of this task, the article proposes a new search strategy based on a combined approach. The combined approach is to divide the decision-making process into two levels. At the first level, the bee optimization method is used to quickly obtain subdomains with a high value of the objective function, and at the second level, an evolutionary a

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FOGGIA, PASQUALE, GENNARO PERCANNELLA, CARLO SANSONE, and MARIO VENTO. "A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION." International Journal of Pattern Recognition and Artificial Intelligence 22, no. 05 (2008): 843–60. http://dx.doi.org/10.1142/s0218001408006557.

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In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A compariso

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Liang, Weixuan, Xinwang Liu, Sihang Zhou, Jiyuan Liu, Siwei Wang, and En Zhu. "Robust Graph-Based Multi-View Clustering." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7462–69. http://dx.doi.org/10.1609/aaai.v36i7.20710.

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Graph-based multi-view clustering (G-MVC) constructs a graphical representation of each view and then fuses them to a unified graph for clustering. Though demonstrating promising clustering performance in various applications, we observe that their formulations are usually non-convex, leading to a local optimum. In this paper, we propose a novel MVC algorithm termed robust graph-based multi-view clustering (RG-MVC) to address this issue. In particular, we define a min-max formulation for robust learning and then rewrite it as a convex and differentiable objective function whose convexity and d

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Chuang Ma, Chuang Ma, and Helong Xia Chuang Ma. "Author Name Disambiguation Based on Heterogeneous Graph." 電腦學刊 34, no. 4 (2023): 041–52. http://dx.doi.org/10.53106/199115992023083404004.

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<p>Since multiple people share the same name in the real world, this will cause performance degradation to academic search systems and lead to misattribution of publications. The author name disambiguation algorithm has not yet to be well solved. In this paper, we propose a disambiguation method that combines heterogeneous graph-based and improved label propagation, first we construct a publication heterogeneous graph network, then graph neural networks is applied to aggregate the nodes representation and relation types, finally combined with the improved label propagation algorithm to r

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Izadkhah, Habib. "Learning Based Genetic Algorithm for Task Graph Scheduling." Applied Computational Intelligence and Soft Computing 2019 (February 3, 2019): 1–15. http://dx.doi.org/10.1155/2019/6543957.

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Nowadays, parallel and distributed based environments are used extensively; hence, for using these environments effectively, scheduling techniques are employed. The scheduling algorithm aims to minimize the makespan (i.e., completion time) of a parallel program. Due to the NP-hardness of the scheduling problem, in the literature, several genetic algorithms have been proposed to solve this problem, which are effective but are not efficient enough. An effective scheduling algorithm attempts to minimize the makespan and an efficient algorithm, in addition to that, tries to reduce the complexity o

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Wheatman, Brian, Xiaojun Dong, Zheqi Shen, et al. "BYO: A Unified Framework for Benchmarking Large-Scale Graph Containers." Proceedings of the VLDB Endowment 17, no. 9 (2024): 2307–20. http://dx.doi.org/10.14778/3665844.3665859.

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A fundamental building block in any graph algorithm is a graph container - - a data structure used to represent the graph. Ideally, a graph container enables efficient access to the underlying graph, has low space usage, and supports updating the graph efficiently. In this paper, we conduct an extensive empirical evaluation of graph containers designed to support running algorithms on large graphs. To our knowledge, this is the first apples-to-apples comparison of graph containers rather than overall systems, which include confounding factors such as differences in algorithm implementations an

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RAJASEKARAN, SANGUTHEVAR, and VAMSI KUNDETI. "SPECTRUM BASED TECHNIQUES FOR GRAPH ISOMORPHISM." International Journal of Foundations of Computer Science 20, no. 03 (2009): 479–99. http://dx.doi.org/10.1142/s0129054109006693.

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The graph isomorphism problem is to check if two given graphs are isomorphic. Graph isomorphism is a well studied problem and numerous algorithms are available for its solution. In this paper we present algorithms for graph isomorphism that employ the spectra of graphs. An open problem that has fascinated many a scientist is if there exists a polynomial time algorithm for graph isomorphism. Though we do not solve this problem in this paper, the algorithms we present take polynomial time. These algorithms have been tested on a good collection of instances. However, we have not been able to prov

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Xia, Zhixin, Zhangqi Zheng, Lexin Bai, Xiaolei Yang, and Yongshan Liu. "A Research Approach to Port Information Security Link Prediction Based on HWA Algorithm." Applied Sciences 14, no. 22 (2024): 10646. http://dx.doi.org/10.3390/app142210646.

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For the protection of information security, link prediction, as a basic problem of network science, has important application significance. However, most of the existing link prediction algorithms rely on the node information of the graph structure, which is not applicable in some graph structure data involving privacy. At the same time, most of the algorithms only consider the general graph structure and do not fully consider the high-order information in the graph. Because of this, this paper proposes an algorithm called hypergraph-based link prediction with self-attention (HWA) to solve the

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Helmi, B. Hoda, Adel T. Rahmani, and Martin Pelikan. "A factor graph based genetic algorithm." International Journal of Applied Mathematics and Computer Science 24, no. 3 (2014): 621–33. http://dx.doi.org/10.2478/amcs-2014-0045.

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Abstract We propose a new linkage learning genetic algorithm called the Factor Graph based Genetic Algorithm (FGGA). In the FGGA, a factor graph is used to encode the underlying dependencies between variables of the problem. In order to learn the factor graph from a population of potential solutions, a symmetric non-negative matrix factorization is employed to factorize the matrix of pair-wise dependencies. To show the performance of the FGGA, encouraging experimental results on different separable problems are provided as support for the mathematical analysis of the approach. The experiments

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van Wyk, B. J., and M. A. van Wyk. "A POCS-based graph matching algorithm." IEEE Transactions on Pattern Analysis and Machine Intelligence 26, no. 11 (2004): 1526–30. http://dx.doi.org/10.1109/tpami.2004.95.

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Wu, Xiang, Xianhui Liu, Yufei Chen, Jianan Shen, and Weidong Zhao. "A graph based superpixel generation algorithm." Applied Intelligence 48, no. 11 (2018): 4485–96. http://dx.doi.org/10.1007/s10489-018-1223-1.

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Ban Kirigin, Tajana, Sanda Bujačić Babić, and Benedikt Perak. "Graph-Based Lexical Sentiment Propagation Algorithm." Mathematics 13, no. 7 (2025): 1141. https://doi.org/10.3390/math13071141.

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In the rapidly developing field of sentiment analysis, it is a challenge to create sentiment dictionaries with broad coverage, especially for languages with limited resources. To address this problem, we propose innovative methodologies that automate the creation of comprehensive sentiment dictionaries, utilising both traditional linguistic approaches and state-of-the-art artificial intelligence technologies. The methodologies are characterised by their universal applicability to different languages. The proposed ConGraCNet Sentiment Propagation algorithm uniquely combines existing sentiment d

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Yi, Yugen, Yuqi Chen, Jiangyan Dai, et al. "Semi-Supervised Ridge Regression with Adaptive Graph-Based Label Propagation." Applied Sciences 8, no. 12 (2018): 2636. http://dx.doi.org/10.3390/app8122636.

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In order to overcome the drawbacks of the ridge regression and label propagation algorithms, we propose a new semi-supervised classification method named semi-supervised ridge regression with adaptive graph-based label propagation (SSRR-AGLP). Firstly, we present a new adaptive graph-learning scheme and integrate it into the procedure of label propagation, in which the locality and sparsity of samples are considered simultaneously. Then, we introduce the ridge regression algorithm into label propagation to solve the “out of sample” problem. As a consequence, the proposed SSSRR-AGLP integrates

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Liu, Zhendong, Gang Li, and Jun S. Liu. "New Algorithms in RNA Structure Prediction Based on BHG." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 13 (2020): 2050031. http://dx.doi.org/10.1142/s0218001420500317.

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There are some NP-hard problems in the prediction of RNA structures. Prediction of RNA folding structure in RNA nucleotide sequence remains an unsolved challenge. We investigate the computing algorithm in RNA folding structural prediction based on extended structure and basin hopping graph, it is a computing mode of basin hopping graph in RNA folding structural prediction including pseudoknots. This study presents the predicting algorithm based on extended structure, it also proposes an improved computing algorithm based on barrier tree and basin hopping graph, which are the attractive approac

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Cui, Beibei, and Jean-Charles Créput. "NCC Based Correspondence Problem for First- and Second-Order Graph Matching." Sensors 20, no. 18 (2020): 5117. http://dx.doi.org/10.3390/s20185117.

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Automatically finding correspondences between object features in images is of main interest for several applications, as object detection and tracking, identification, registration, and many derived tasks. In this paper, we address feature correspondence within the general framework of graph matching optimization and with the principal aim to contribute. We proposed two optimized algorithms: first-order and second-order for graph matching. On the one hand, a first-order normalized cross-correlation (NCC) based graph matching algorithm using entropy and response through Marr wavelets within the

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Tan, Siqi, Li Chen, and Weidong Wang. "Coded computing for distributed graph-based semi-supervised learning." JUSTC 53, no. 4 (2023): 0401. http://dx.doi.org/10.52396/justc-2022-0133.

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Semi-supervised learning (SSL) has been applied to many practical applications over the past few years. Recently, distributed graph-based semi-supervised learning (DGSSL) has been shown to have good performance. Current DGSSL algorithms usually have the problems of inefficient graph construction and the straggler effect. This paper proposes a novel coded DGSSL (CDGSSL) to solve these problems. We first provide a novel parallel and distributed solution of matrix completion for efficient graph construction. Then, we develop the CDGSSL algorithm based on coding theory. Specifically, the proposed

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Liu, Bing Yue. "The Extended Algorithm of the Bipartite Graph Matching Semantic Web Service Discovery." Advanced Materials Research 886 (January 2014): 677–80. http://dx.doi.org/10.4028/www.scientific.net/amr.886.677.

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In previous work, the ontology-based algorithm of bipartite graph matching semantic web service discovery was presented. But in these algorithms, the problem of finding augment path was not resolved very well. It will affect the recall rate and precision rate of web service discovery. In order to resolve these problems, this paper proposes an ontology-based bipartite graph matching semantic web service discovery algorithm, which extends the optimal bipartite graph matching algorithm. It uses the slack function to find the augment path in the equivalent sub graph. The algorithm also contributes

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Cui, Huanqing, Yifan Wu, and Silin Lv. "Property Graph Partition Algorithm Based on Improved Barnacle Mating Optimization." Journal of Physics: Conference Series 2832, no. 1 (2024): 012005. http://dx.doi.org/10.1088/1742-6596/2832/1/012005.

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Abstract Distributed graph processing systems have been used more frequently in various fields, and graph partitioning is the basis of these systems. Graph partitioning algorithms generally aim to minimize the communication cost while the number of vertices or edges reach the load balance. But the vertices and/or edges of property graphs require some storage volume, so the traditional graph partition algorithms will lead to an unbalanced storage volume load. This paper proposes an edge-cut graph partitioning algorithm to generate partitions with equal size and storage volume as well as low cut

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Huang, Gang, Man Yuan, Chun-Sheng Li, and Yong-he Wei. "Personalized Knowledge Recommendation Based on Knowledge Graph in Petroleum Exploration and Development." International Journal of Pattern Recognition and Artificial Intelligence 34, no. 10 (2020): 2059033. http://dx.doi.org/10.1142/s0218001420590338.

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Firstly, this paper designs the process of personalized recommendation method based on knowledge graph, and constructs user interest model. Second, the traditional personalized recommendation algorithms are studied and their advantages and disadvantages are analyzed. Finally, this paper focuses on the combination of knowledge graph and collaborative filtering recommendation algorithm. They are effective to solve the problem where [Formula: see text] value is difficult to be determined in the clustering process of traditional collaborative filtering recommendation algorithm as well as data spar

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Lei Wang, Lei Wang, Ting-Ting Niu Lei Wang, Wei-Hao Qiao Ting-Ting Niu, and Song Cui Wei-Hao Qiao. "Wireless Sensor Networks Node Localization Algorithm Based on Range Optimization and Graph Optimization." 電腦學刊 35, no. 1 (2024): 001–16. http://dx.doi.org/10.53106/199115992024023501001.

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<p>To address the problem of low localization accuracy in the node localization algorithms of wireless sensor networks (WSN) based on received signal strength indication (RSSI) ranging, a WSN node localization algorithm based on ranging optimization and graph optimization is proposed. In terms of RSSI ranging, the outliers are removed using the Grubbs method, and the data are processed using a moving average smoothing-Gaussian hybrid filter to establish a Bessel function ranging model to reduce the ranging error; in terms of node localization, the signal strength data are employed to con

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Bernardi, Olivier, Eric Fusy, and Shizhe Liang. "A Census of Graph-Drawing Algorithms Based on Generalized Transversal Structures." Journal of Graph Algorithms and Applications 29, no. 1 (2025): 187–246. https://doi.org/10.7155/jgaa.v29i1.2940.

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We present two graph drawing algorithms based on the recently defined "grand-Schnyder woods", which are a far-reaching generalization of the classical Schnyder woods. The first is a straight-line drawing algorithm for plane graphs with faces of degree 3 and 4 with no separating 3-cycle, while the second is a rectangular drawing algorithm for the dual of such plane graphs.In our algorithms, the coordinates of the vertices are defined in a global manner, based on the underlying grand-Schnyder woods. The grand-Schnyder woods and drawings are computed in linear time. When specializing our algorith

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Yuhong Zhao, Yuhong Zhao, Xiangming Ni Yuhong Zhao, Yue Yao Xiangming Ni, and Peng Mei Yue Yao. "Research on Link Prediction Method Based on Information Fusion Graph Embedding." 電腦學刊 35, no. 4 (2024): 059–73. http://dx.doi.org/10.53106/199115992024083504005.

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<p>To accurately and efficiently capture the topological and attribute information of nodes and apply them to the link prediction task, this paper proposes a Dual Channel Graph Convolution Link Prediction (DC-GCN). DC-GCN constructs a dual channel through the graph convolution network. DC-GCN can learn both topological embeddings and attribute embeddings of nodes; it introduces an attention mechanism to learn the weights of each embedding adaptively and then performs weighted fusion to obtain the final embedding representation of nodes. Finally, the Hadamard distance of nodes is used to

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Li, Wei, Haomin Wang, Xiaoliang Zhang, et al. "Security Service Function Chain Based on Graph Neural Network." Information 13, no. 2 (2022): 78. http://dx.doi.org/10.3390/info13020078.

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With the rapid development and wide application of cloud computing, security protection in cloud environment has become an urgent problem to be solved. However, traditional security service equipment is closely coupled with the network topology, so it is difficult to upgrade and expand the security service, which cannot change with the change of network application security requirements. Building a security service function chain (SSFC) makes the deployment of security service functions more dynamic and scalable. Based on a software defined network (SDN) and network function virtualization (NF

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Shang, Liuting, Sheng Lu, Yichen Zhang, Sungyong Jung, and Chenyun Pan. "Directed Acyclic Graph-Based Datapath Synthesis Using Graph Isomorphism and Gate Reconfiguration." Chips 3, no. 2 (2024): 182–95. http://dx.doi.org/10.3390/chips3020008.

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Datapath synthesis is a crucial step in synthesis flow and aims at globally minimizing an area by identifying shareable logic structures. This paper introduces a novel Directed Acyclic Graph (DAG)-based datapath synthesis method based on graph isomorphism and gate reconfiguration. Unlike algorithms that identify common specification logic, our approach simplifies the problem by focusing on searching for common topology. Leveraging the concept of gate reconfiguration, our algorithm extends the applicability of DAG-based datapath synthesis by transforming a topology-equivalent network into a spe

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Kim, Sang-Kyun, DongHyoung Lee, Anna Kim, and SeJin Nam. "Construction of Korean Medicine Knowledge Graph and Development of Prescription Recommendation System based on RippleNet." Journal of Korean Medicine 46, no. 2 (2025): 51–62. https://doi.org/10.13048/jkm.25017.

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Objectives: We aim to propose an artificial intelligence system for recommending Korean medicine prescriptions based on the RippleNet algorithm using a knowledge graph.Methods: The Korean medicine knowledge graph was constructed with information on prescriptions, medicinal materials, chemical compounds, gene targets, and modern diseases from the TM-MC database. The RippleNet algorithm was trained with the knowledge graph and the prescription history of diseases from TM-MC. The optimized hyperparameter values of the algorithm were derived using the grid search method. The recommendation system

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HOLZRICHTER, MICHAEL, and SUELY OLIVEIRA. "A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM." International Journal of Foundations of Computer Science 10, no. 02 (1999): 225–46. http://dx.doi.org/10.1142/s0129054199000162.

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The problem of partitioning a graph such that the number of edges incident to vertices in different partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing fill-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smallest eigenvalue of a matrix called the graph Laplacian. The focus of this paper is the use graph theory to compute this eigenvector more quickly.

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Singh, Rashmi, Khursheed Alam, and Neha Bhardwaj. "Graph-based recommendation algorithm for personalized suggestions." Journal of Discrete Mathematical Sciences and Cryptography 28, no. 3 (2025): 931–44. https://doi.org/10.47974/jdmsc-2204.

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In the present study, we put forward a graph-based recommendation algorithm designed to provide personalized suggestions to users. By leveraging principles from graph theory, our algorithm utilizes the inherent structure for the recommendation system. We consider user preferences and item similarities to construct a graph representation of the recommendation network. Through a combination of graph traversal and similarity analysis techniques, our algorithm identifies relevant items for each user based on their preferences and the connections between items. The personalized suggestions generate

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Jin, Haiyan, Guodong Xu, Kangda Cheng, Jinlong Liu, and Zhilu Wu. "A Link Prediction Algorithm Based on GAN." Electronics 11, no. 13 (2022): 2059. http://dx.doi.org/10.3390/electronics11132059.

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Link prediction, as an important research direction in complicated network analysis, has broad application prospects. However, traditional link prediction algorithms are generally designed by the sparse expression of the adjacency matrix, which is computationally expensive and inefficient, being also unable to run on large-scale networks and to preserve their higher order structural features. To fill this gap, we propose a GAN (generative adversarial network)-based link prediction algorithm. The algorithm layers the network graph, preserving the local features and higher-level structural featu

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Fang, Xue, Lin Li, and Zheng Wei. "Design of Recommendation Algorithm Based on Knowledge Graph." Journal of Physics: Conference Series 2425, no. 1 (2023): 012025. http://dx.doi.org/10.1088/1742-6596/2425/1/012025.

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Abstract The commodity data in the shopping system contains a wealth of feature information, and different commodities are related by these features. Traditional collaborative filtering algorithms represent commodity data in a structured way, but they have issues such as low commodity similarity calculation accuracy, poor recommendation effect, and unfriendly recommendation results. The commodity recommendation algorithm based on the knowledge graph put forward in this paper initially automatically extracts the entities and entity relationships in the commodity data as the vertices and edges o

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V., Jenifer. "A BRIEF SURVEY ON DISTRIBUTED GRAPH ALGORITHMS FOR SHORTEST DISTANCE." Journal of Emerging Technologies and Innovative Research 6, no. 4 (2019): 20–24. https://doi.org/10.5281/zenodo.4301270.

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There is an extended history of study in theoretical computer science faithful to designing proficient algorithms for graph problems. In several modern applications the graph in query is altering over time, and to avoid rerunning algorithm on the entire graph every time a small change occurs. This paper aims to present a brief survey on graph theory based on Shortest Distances in Dynamic Graphs techniques in which the goal is to minimize the amount of work needed to re-optimize the solution when the graph changes. Number of relative studies namely Graph pattern matching, Spatially Induced Link

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JENIFER.V. "A Brief Survey on Distributed Graph Algorithms for Shortest Distance." CiiT International Journal of Software Engineering and Technology Vol 13, No 1,, no. 8 (2021): 4. https://doi.org/10.5281/zenodo.4738503.

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There is an extended history of study in theoretical computer science faithful to designing proficient algorithms for graph problems. In several modern applications the graph in query is altering over time, and to avoid rerunning algorithm on the entire graph every time a small change occurs. This paper aims to present a brief survey on graph theory based on Shortest Distances in Dynamic Graphs techniques in which the goal is to minimize the amount of work needed to re-optimize the solution when the graph changes. Number of relative studies namely Graph pattern matching, Spatially Induced Link

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Lin, Hanyang, Yongzhao Zhan, Zizheng Zhao, Yuzhong Chen, and Chen Dong. "Overlapping Community Detection Based on Attribute Augmented Graph." Entropy 23, no. 6 (2021): 680. http://dx.doi.org/10.3390/e23060680.

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There is a wealth of information in real-world social networks. In addition to the topology information, the vertices or edges of a social network often have attributes, with many of the overlapping vertices belonging to several communities simultaneously. It is challenging to fully utilize the additional attribute information to detect overlapping communities. In this paper, we first propose an overlapping community detection algorithm based on an augmented attribute graph. An improved weight adjustment strategy for attributes is embedded in the algorithm to help detect overlapping communitie

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Ho, Tran, Ho-Seok Kang, and Sung-Ryul Kim. "Graph-based KNN Algorithm for Spam SMS Detection." JUCS - Journal of Universal Computer Science 19, no. (16) (2013): 2404–19. https://doi.org/10.3217/jucs-019-16-2404.

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In the modern life, SMS (Short Message Service) is one of the most necessary services on mobile devices. Because of its popularity, many companies use SMS as an effective marketing and advertising tool. Also, the popularity gives hackers chances to abuse SMS to cheat mobile users and steal personal information in their mobile phones, for example. In this paper, we propose a method to detect spam SMS on mobile devices and smart phones. Our approach is based on improving a graph-based algorithm and utilizing the KNN Algorithm - one of the simplest and most effective classification algorithms. Th

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Mrs., Pooja V. "Enhancing Scheduling Efficiency in Graph Theory: A Novel Approach to Maximum Matching." Journal of Scholastic Engineering Science and Management 3, no. 3 (2024): 1–13. https://doi.org/10.5281/zenodo.10907243.

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The maximum matching problem is a fundamental problem in graph theory. It asks for the maximum number of edges that can be selected in a graph so that no two selected edges share an endpoint. This problem has applications in many areas, such as scheduling, transportation, and telecommunications. Existing algorithms for finding the maximum matching in a graph are often inefficient. For example, the brute-force algorithm can take exponential time to find the maximum matching in a graph. In this paper, we present a new algorithm for finding the maximum matching in a graph. Our algorithm is based

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Chen, Yuanyi, Yihao Lin, Peng Yu, Yanyun Tao, and Zengwei Zheng. "A graph-based sensor recommendation model in semantic sensor network." International Journal of Distributed Sensor Networks 18, no. 5 (2022): 155014772110493. http://dx.doi.org/10.1177/15501477211049307.

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In the past few years, introducing ontology to describe the concepts and relationships between different entities in semantic sensor network enhances the interoperability between entities. Existing works mostly based on SPARQL retrieval ignore the user’s specific requirements of sensor attributes. Therefore, the recommendation results cannot satisfy the user’s needs. In this article, we propose a graph-based sensor recommendation model. The model mainly includes two parts: (1) Filtering nodes in data graph. In addition to using the traditional graph matching algorithm, we propose a threshold p

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Tian, Zhaoxu, Yongmei Cheng, and Shun Yao. "An Adaptive Fast Incremental Smoothing Approach to INS/GPS/VO Factor Graph Inference." Applied Sciences 14, no. 13 (2024): 5691. http://dx.doi.org/10.3390/app14135691.

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In response to asynchronous and delayed sensors within multi-sensor integrated navigation systems, the computational complexity of joint optimization navigation solutions persistently rises. This paper introduces an adaptive fast integrated navigation algorithm for INS/GPS/VO based on factor graph. The factor graph model for INS/GPS/VO is developed subsequent to individual modeling of the Inertial Navigation System (INS), Global Positioning System (GPS), and Visual Odometer (VO) using the factor graph model approach. Additionally, an Adaptive Fast Incremental Smoothing (AFIS) factor graph opti

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Liu, Shuang, and Zhong Zhang. "Adaptive Graph Cut Based Cloud Detection in Wireless Sensor Networks." International Journal of Distributed Sensor Networks 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/947169.

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We focus on the issue of cloud detection in wireless sensor networks (WSN) and propose a novel detection algorithm named adaptive graph cut (AGC) to tackle this issue. We first automatically label some pixels as “cloud” or “clear sky” with high confidence. Then, those labelled pixels serve as hard constraint seeds for the following graph cut algorithm. In addition, a novel transfer learning algorithm is proposed to transfer knowledge among sensor nodes, such that cloud images captured from different sensor nodes can adapt to different weather conditions. The experimental results show that the

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Lv, Taizhi, Chunxia Zhao, and Jiancheng Bao. "A Global Path Planning Algorithm Based on Bidirectional SVGA." Journal of Robotics 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/8796531.

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For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the

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SKULRATTANAKULCHAI, SAN, and HAROLD N. GABOW. "COLORING ALGORITHMS ON SUBCUBIC GRAPHS." International Journal of Foundations of Computer Science 15, no. 01 (2004): 21–40. http://dx.doi.org/10.1142/s0129054104002285.

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We present efficient algorithms for three coloring problems on subcubic graphs. (A subcubic graph has maximum degree at most three.) The first algorithm is for 4-edge coloring, or more generally, 4-list-edge coloring. Our algorithm runs in linear time, and appears to be simpler than previous ones. The second algorithm is the first randomized EREW PRAM algorithm for the same problem. It uses O(n/ log n) processors and runs in O( log n) time with high probability, where n is the number of vertices of the graph. The third algorithm is the first linear-time algorithm to 5-total-color subcubic grap

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