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Journal articles on the topic 'Subgraph isomorphism'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Duong, Chi Thang, Trung Dung Hoang, Hongzhi Yin, Matthias Weidlich, Quoc Viet Hung Nguyen, and Karl Aberer. "Efficient streaming subgraph isomorphism with graph neural networks." Proceedings of the VLDB Endowment 14, no. 5 (2021): 730–42. http://dx.doi.org/10.14778/3446095.3446097.

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Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do not scale to a dynamic setting of a continuous stream of queries. In this paper, we address the scalability challenges induced by a stream of subgraph isomorphism queries by caching and re-use of previous results. We first present a novel subgraph index based on graph embeddings that serves as the foundation for efficient stream processing. It

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2

Douar, Brahim, Chiraz Latiri, Michel Liquiere, and Yahya Slimani. "A Projection Bias in Frequent Subgraph Mining Can Make a Difference." International Journal on Artificial Intelligence Tools 23, no. 05 (2014): 1450005. http://dx.doi.org/10.1142/s0218213014500055.

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The aim of the frequent subgraph mining task is to find frequently occurring subgraphs in a large graph database. However, this task is a thriving challenge, as graph and subgraph isomorphisms play a key role throughout the computations. Since subgraph isomorphism testing is a hard problem, subgraph miners are exponential in runtime. To alleviate the complexity issue, we propose to introduce a bias in the projection operator and instead of using the costly subgraph isomorphism projection, one can use a polynomial projection having a semantically-valid structural interpretation. This paper pres

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3

Koca, Mehmet Burak, and Fatih Erdoğan Sevilgen. "An Efficient Index-Based Algorithm for Exact Subgraph Isomorphism on Bipartite Graphs." Scientific Research Communications 4, no. 1 (2024): 44–60. https://doi.org/10.52460/src.2024.005.

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Graphs are widely used to represent various real-world networks, but their non-linear nature and size increase pose challenges for efficient analysis. The subgraph isomorphism problem, which involves identifying subgraphs that are isomorphic to a query graph, plays a crucial role in diverse domains. In this paper, we focus on the exact subgraph isomorphism problem in bipartite graphs and propose a novel index-based solution algorithm. Our algorithm leverages triplet structures for graph embedding and uses a multi-level hash map for efficient filtering. We also introduce an optimized solution b

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4

Demetrovics, J., H. M. Quang, N. V. Anh, and V. D. Thi. "An Optimization of Closed Frequent Subgraph Mining Algorithm." Cybernetics and Information Technologies 17, no. 1 (2017): 3–15. http://dx.doi.org/10.1515/cait-2017-0001.

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Abstract Graph mining isamajor area of interest within the field of data mining in recent years. Akey aspect of graph mining is frequent subgraph mining. Central to the entire discipline of frequent subgraph mining is the concept of subgraph isomorphism. One major issue in early subgraph isomorphism research concerns computational complexity. Normally, the subgraph isomorphism problem is NP-complete. Previous studies of frequent subgraph mining have not solved NP-complete problem in the subgraph isomorphism. In this paper, we proposeanew algorithm which can deal with this problem. The proposed

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Xu, Zifeng, Fucai Zhou, Yuxi Li, Jian Xu, and Qiang Wang. "Privacy-Preserving Subgraph Matching Protocol for Two Parties." International Journal of Foundations of Computer Science 30, no. 04 (2019): 571–88. http://dx.doi.org/10.1142/s0129054119400136.

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Graph data structure has been widely used across many application areas, such as web data, social network, and cheminformatics. The main benefit of storing data as graphs is there exists a rich set of graph algorithms and operations that can be used to solve various computing problems, including pattern matching, data mining, and image processing. Among these graph algorithms, the subgraph isomorphism problem is one of the most fundamental algorithms that can be utilized by many higher level applications. The subgraph isomorphism problem is defined as, given two graphs [Formula: see text] and

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6

Schellewald, Christian. "A Convex Relaxation Bound for Subgraph Isomorphism." International Journal of Combinatorics 2012 (February 7, 2012): 1–18. http://dx.doi.org/10.1155/2012/908356.

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In this work a convex relaxation of a subgraph isomorphism problem is proposed, which leads to a new lower bound that can provide a proof that a subgraph isomorphism between two graphs can not be found. The bound is based on a semidefinite programming relaxation of a combinatorial optimisation formulation for subgraph isomorphism and is explained in detail. We consider subgraph isomorphism problem instances of simple graphs which means that only the structural information of the two graphs is exploited and other information that might be available (e.g., node positions) is ignored. The bound i

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7

Liu, Xin, and Yangqiu Song. "Graph Convolutional Networks with Dual Message Passing for Subgraph Isomorphism Counting and Matching." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7594–602. http://dx.doi.org/10.1609/aaai.v36i7.20725.

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Graph neural networks (GNNs) and message passing neural networks (MPNNs) have been proven to be expressive for subgraph structures in many applications. Some applications in heterogeneous graphs require explicit edge modeling, such as subgraph isomorphism counting and matching. However, existing message passing mechanisms are not designed well in theory. In this paper, we start from a particular edge-to-vertex transform and exploit the isomorphism property in the edge-to-vertex dual graphs. We prove that searching isomorphisms on the original graph is equivalent to searching on its dual graph.

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8

Chu, Yang Jie, and Xin Jia. "An Improved Algorithm on Frequent Subgraph Query." Applied Mechanics and Materials 623 (August 2014): 169–73. http://dx.doi.org/10.4028/www.scientific.net/amm.623.169.

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This paper studies the frequent subgraph query issues on graph data set. Combining with the approach that frequent subtree extend to frequent subgraphs proposed by Xian-Tong Li, we propose a new algorithm. This algorithm improved its storage structure avoiding direct subgraph isomorphism judgment, reduced the stability requirements on graph set, and enchanced the overall efficiency of the algorithm.

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9

Li, Feng. "An efficient mining algorithm for maximal frequent patterns in uncertain graph database." Journal of Intelligent & Fuzzy Systems 39, no. 5 (2020): 7021–33. http://dx.doi.org/10.3233/jifs-200237.

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Mining maximal frequent patterns is significant in many fields, but the mining efficiency is often low. The bottleneck lies in too many candidate subgraphs and extensive subgraph isomorphism tests. In this paper we propose an efficient mining algorithm. There are two key ideas behind the proposed methods. The first is to divide each edge of every certain graph (converted from equivalent uncertain graph) and build search tree, avoiding too many candidate subgraphs. The second is to search the tree built in the first step in order, avoiding extensive subgraph isomorphism tests. The evaluation of

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10

Jiang, Lincheng, Xiang Zhao, Bin Ge, et al. "On Minimal Unique Induced Subgraph Queries." Applied Sciences 8, no. 10 (2018): 1798. http://dx.doi.org/10.3390/app8101798.

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In this paper, a novel type of interesting subgraph query is proposed: Minimal Unique Induced Subgraph (MUIS) query. Given a (large) graph G and a query vertex (position) q in the graph, can we find an induced subgraph containing q with the minimal number of vertices that is unique in G? MUIS query has many potential applications, such as subgraph retrieval, graph visualization, representative subgraph discovery and vertex property exploration. The formal definition of MUIS is given and the properties are discussed in this paper. The baseline and EQA (Efficient Query Answering) algorithms are

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11

Fu, Lixin. "Optimized Backtracking for Subgraph Isomorphism." International Journal of Database Management Systems 4, no. 6 (2012): 1–10. http://dx.doi.org/10.5121/ijdms.2012.4601.

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12

Plantenga, Todd. "Inexact subgraph isomorphism in MapReduce." Journal of Parallel and Distributed Computing 73, no. 2 (2013): 164–75. http://dx.doi.org/10.1016/j.jpdc.2012.10.005.

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13

Fehér, Péter, Márk Asztalos, Tamás Vajk, Tamás Mészáros, and László Lengyel. "Detecting subgraph isomorphism with MapReduce." Journal of Supercomputing 73, no. 5 (2016): 1810–51. http://dx.doi.org/10.1007/s11227-016-1885-6.

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14

Kijima, Shuji, Yota Otachi, Toshiki Saitoh, and Takeaki Uno. "Subgraph isomorphism in graph classes." Discrete Mathematics 312, no. 21 (2012): 3164–73. http://dx.doi.org/10.1016/j.disc.2012.07.010.

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15

Shahrivari, Saeed, and Saeed Jalili. "Fast Parallel All-Subgraph Enumeration Using Multicore Machines." Scientific Programming 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/901321.

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Enumerating all subgraphs of an input graph is an important task for analyzing complex networks. Valuable information can be extracted about the characteristics of the input graph using all-subgraph enumeration. Notwithstanding, the number of subgraphs grows exponentially with growth of the input graph or by increasing the size of the subgraphs to be enumerated. Hence, all-subgraph enumeration is very time consuming when the size of the subgraphs or the input graph is big. We propose a parallel solution namedSubenumwhich in contrast to available solutions can perform much faster. Subenum enume

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16

Yow, Kai Siong, Ningyi Liao, Siqiang Luo, and Reynold Cheng. "Machine Learning for Subgraph Extraction: Methods, Applications and Challenges." Proceedings of the VLDB Endowment 16, no. 12 (2023): 3864–67. http://dx.doi.org/10.14778/3611540.3611571.

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Subgraphs are obtained by extracting a subset of vertices and a subset of edges from the associated original graphs, and many graph properties are known to be inherited by subgraphs. Subgraphs can be applied in many areas such as social networks, recommender systems, biochemistry and fraud discovery. Researchers from various communities have paid a great deal of attention to investigate numerous subgraph problems, by proposing algorithms that mainly extract important structures of a given graph. There are however some limitations that should be addressed, with regard to the efficiency, effecti

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17

Yu, Xingtong, Zemin Liu, Yuan Fang, and Xinming Zhang. "Learning to Count Isomorphisms with Graph Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 4 (2023): 4845–53. http://dx.doi.org/10.1609/aaai.v37i4.25610.

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Subgraph isomorphism counting is an important problem on graphs, as many graph-based tasks exploit recurring subgraph patterns. Classical methods usually boil down to a backtracking framework that needs to navigate a huge search space with prohibitive computational cost. Some recent studies resort to graph neural networks (GNNs) to learn a low-dimensional representation for both the query and input graphs, in order to predict the number of subgraph isomorphisms on the input graph. However, typical GNNs employ a node-centric message passing scheme that receives and aggregates messages on nodes,

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18

Li, Qiyan, and Jeffrey Xu Yu. "Fast Local Subgraph Counting." Proceedings of the VLDB Endowment 17, no. 8 (2024): 1967–80. http://dx.doi.org/10.14778/3659437.3659451.

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We study local subgraph counting queries, Q = ( p, o ), to count how many times a given k -node pattern graph p appears around every node υ in a data graph G when the given center node o in p maps to υ. Such local subgraph counting becomes important in GNNs (Graph Neural Networks), where incorporating such counts for every node in G into the GNN architecture enhances the model's ability to capture complex relationships within the graph G. It is challenging to count by subgraph isomorphism, which is known to be NP-hard. In this paper, we propose a novel approach by tree-decomposition-based coun

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19

Xiao, Hong, Yuan Li, Jian-Feng Yu, and Hui Cheng. "Dynamic assembly simplification for virtual assembly process of complex product." Assembly Automation 34, no. 1 (2014): 1–15. http://dx.doi.org/10.1108/aa-12-2012-093.

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Purpose – Virtual assembly process plays an important role in assembly design of complex product and is typically time- and resource-intensive. This paper aims to investigate a dynamic assembly simplification approach in order to demonstrate and interact with virtual assembly process of complex product in real time. Design/methodology/approach – The proposed approach regards the virtual assembly process of complex product as an incremental growth process of dynamic assembly. During the growth process, the current-assembled-state assembly model is simplified with appearance preserved by detecti

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20

Shankar, Vaishaal, Jordan Zhang, Jerry Chen, Christopher Dinh, Mattthew Clements, and Avideh Zakhor. "Approximate Subgraph Isomorphism for Image Localization." Electronic Imaging 2016, no. 15 (2016): 1–9. http://dx.doi.org/10.2352/issn.2470-1173.2016.15.ipas-191.

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21

Amano, Kazuyuki. "k-Subgraph Isomorphism on AC0 Circuits." computational complexity 19, no. 2 (2010): 183–210. http://dx.doi.org/10.1007/s00037-010-0288-y.

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22

Solnon, Christine. "AllDifferent-based filtering for subgraph isomorphism." Artificial Intelligence 174, no. 12-13 (2010): 850–64. http://dx.doi.org/10.1016/j.artint.2010.05.002.

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23

Jüttner, Alpár, and Péter Madarasi. "VF2++—An improved subgraph isomorphism algorithm." Discrete Applied Mathematics 242 (June 2018): 69–81. http://dx.doi.org/10.1016/j.dam.2018.02.018.

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24

Čibej, Uroš, and Jurij Mihelič. "SEARCH-TREE SIZE ESTIMATION FOR THE SUBGRAPH ISOMORPHISM PROBLEM." Acta Electrotechnica et Informatica 18, no. 4 (2018): 3–10. http://dx.doi.org/10.15546/aeei-2018-0026.

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25

Leng, Fangling, Fan Li, Yubin Bao, Tiancheng Zhang, and Ge Yu. "FSM-BC-BSP: Frequent Subgraph Mining Algorithm Based on BC-BSP." Applied Sciences 14, no. 8 (2024): 3154. http://dx.doi.org/10.3390/app14083154.

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As graph models become increasingly prevalent in the processing of scientific data, the exploration of effective methods for the mining of meaningful patterns from large-scale graphs has garnered significant research attention. This paper delves into the complexity of frequent subgraph mining and proposes a frequent subgraph mining (FSM) algorithm. This FSM algorithm is developed within a distributed graph iterative system, designed for the Big Cloud (BC) environment of the China Mobile Corp., and is based on the bulk synchronous parallel (BSP) model, named FSM-BC-BSP. Its aim is to address th

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26

Adinayev, Arthur, and Itamar Stein. "Diamond Subgraphs in the Reduction Graph of a One-Rule String Rewriting System." Fundamenta Informaticae 178, no. 3 (2021): 173–85. http://dx.doi.org/10.3233/fi-2021-2002.

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In this paper, we study a certain case of a subgraph isomorphism problem. We consider the Hasse diagram of the lattice Mk (the unique lattice with k + 2 elements and one anti-chain of length k) and find the maximal k for which it is isomorphic to a subgraph of the reduction graph of a given one-rule string rewriting system. We obtain a complete characterization for this problem and show that there is a dichotomy. There are one-rule string rewriting systems for which the maximal such k is 2 and there are cases where there is no maximum. No other intermediate option is possible.

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27

Rosenthal, Gregory. "Beating Treewidth for Average-Case Subgraph Isomorphism." Algorithmica 83, no. 8 (2021): 2521–51. http://dx.doi.org/10.1007/s00453-021-00813-y.

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28

Messmer, B. T., and H. Bunke. "Efficient subgraph isomorphism detection: a decomposition approach." IEEE Transactions on Knowledge and Data Engineering 12, no. 2 (2000): 307–23. http://dx.doi.org/10.1109/69.842269.

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29

Kim, Jinha, Hyungyu Shin, Wook-Shin Han, Sungpack Hong, and Hassan Chafi. "Taming subgraph isomorphism for RDF query processing." Proceedings of the VLDB Endowment 8, no. 11 (2015): 1238–49. http://dx.doi.org/10.14778/2809974.2809985.

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30

Ren, Xuguang, and Junhu Wang. "Multi-query optimization for subgraph isomorphism search." Proceedings of the VLDB Endowment 10, no. 3 (2016): 121–32. http://dx.doi.org/10.14778/3021924.3021929.

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31

El-Sonbaty, Yasser, and M. A. Ismail. "A new algorithm for subgraph optimal isomorphism." Pattern Recognition 31, no. 2 (1998): 205–18. http://dx.doi.org/10.1016/s0031-3203(97)00041-1.

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32

Gupta, Sushmita, and Sanjukta Roy. "Stable Matching Games: Manipulation via Subgraph Isomorphism." Algorithmica 80, no. 9 (2017): 2551–73. http://dx.doi.org/10.1007/s00453-017-0382-5.

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33

Krissinel, Evgeny B., and Kim Henrick. "Common subgraph isomorphism detection by backtracking search." Software: Practice and Experience 34, no. 6 (2004): 591–607. http://dx.doi.org/10.1002/spe.588.

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34

Zampelli, Stéphane, Yves Deville, and Christine Solnon. "Solving subgraph isomorphism problems with constraint programming." Constraints 15, no. 3 (2009): 327–53. http://dx.doi.org/10.1007/s10601-009-9074-3.

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35

Santos, Ricardo, Rodolfo Azevedo, and Guido Araujo. "Instruction Scheduling Based on Subgraph Isomorphism for a High Performance Computer Processor." JUCS - Journal of Universal Computer Science 14, no. (21) (2008): 3465–80. https://doi.org/10.3217/jucs-014-21-3465.

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This paper1 presents an instruction scheduling algorithm based on the Subgraph Isomorphism Problem. Given a Directed Acyclic Graph (DAG) G1, our algorithm looks for a subgraph G02 in a base graph G2, such that G02 is isomorphic to G1. The base graph G2 represents the arrangement of the processing elements of a high performance computer architecture named 2D-VLIW and G02 is the set of those processing elements required to execute operations in G1. We have compared this algorithm with a greedy list scheduling strategy using programs of the SPEC and MediaBench suites. In our experiments, the aver

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McCreesh, Ciaran, Patrick Prosser, Christine Solnon, and James Trimble. "When Subgraph Isomorphism is Really Hard, and Why This Matters for Graph Databases." Journal of Artificial Intelligence Research 61 (March 30, 2018): 723–59. http://dx.doi.org/10.1613/jair.5768.

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The subgraph isomorphism problem involves deciding whether a copy of a pattern graph occurs inside a larger target graph. The non-induced version allows extra edges in the target, whilst the induced version does not. Although both variants are NP-complete, algorithms inspired by constraint programming can operate comfortably on many real-world problem instances with thousands of vertices. However, they cannot handle arbitrary instances of this size. We show how to generate "really hard" random instances for subgraph isomorphism problems, which are computationally challenging with a couple of h

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37

Dietrich, Heiko, and Subhrajyoti Saha. "A note on skeleton groups in coclass graphs." International Journal of Algebra and Computation 29, no. 01 (2019): 127–46. http://dx.doi.org/10.1142/s0218196718500650.

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Recent studies of [Formula: see text]-groups of coclass [Formula: see text] concentrate on the coclass graph [Formula: see text]. While the detailed structure of [Formula: see text] is unknown, it is known that its general structure is dominated by the subgraph of ‘skeleton groups’. The original definition of these groups is technical, but some modifications for special cases have been used successfully in the literature. Given their importance, in this paper we define and investigate skeleton groups more rigorously. In particular, we study their isomorphism problem, which is a crucial step to

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38

Bunke, H., and B. T. Messmer. "Recent Advances in Graph Matching." International Journal of Pattern Recognition and Artificial Intelligence 11, no. 01 (1997): 169–203. http://dx.doi.org/10.1142/s0218001497000081.

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A powerful and universal data structure with applications invarious subfields of science and engineering is graphs. In computer vision and image analysis, graphs are often used for the representation of structured objects. For example, if the problem is to recognize instances of known objects in an image, then often models, or prototypes, of the known objects are represented by means of graphs and stored in a database. The unknown objects in the input image are extracted by means of suitable preprocessing and segmentation algorithms, and represented by graphs that are analogous to the model gr

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39

Ansari, Zubair Ali, Jahiruddin, and Muhammad Abulaish. "An Efficient Subgraph Isomorphism Solver for Large Graphs." IEEE Access 9 (2021): 61697–709. http://dx.doi.org/10.1109/access.2021.3073494.

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40

Eppstein, David. "Subgraph Isomorphism in Planar Graphs and Related Problems." Journal of Graph Algorithms and Applications 3, no. 3 (1999): 1–27. http://dx.doi.org/10.7155/jgaa.00014.

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41

Liu, Yanzhao, Yinliang Zhao, Lei Zhang, and Kai Liu. "Subgraph Isomorphism Based Intrinsic Function Reduction in Decompilation." Journal of Software Engineering and Applications 09, no. 03 (2016): 80–90. http://dx.doi.org/10.4236/jsea.2016.93007.

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42

Choi, Yunyoung, and Kunsoo Park. "Improving Subgraph Isomorphism with Pruning by Bipartite Matching." Journal of KIISE 48, no. 9 (2021): 973–80. http://dx.doi.org/10.5626/jok.2021.48.9.973.

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43

Zhukovskii, M. E. "On first-order definitions of subgraph isomorphism properties." Doklady Mathematics 96, no. 2 (2017): 454–56. http://dx.doi.org/10.1134/s1064562417050167.

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Li, Yuan, Alexander Razborov, and Benjamin Rossman. "On the $AC^0$ Complexity of Subgraph Isomorphism." SIAM Journal on Computing 46, no. 3 (2017): 936–71. http://dx.doi.org/10.1137/14099721x.

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Bonnici, Vincenzo, and Rosalba Giugno. "On the Variable Ordering in Subgraph Isomorphism Algorithms." IEEE/ACM Transactions on Computational Biology and Bioinformatics 14, no. 1 (2017): 193–203. http://dx.doi.org/10.1109/tcbb.2016.2515595.

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Schmidt, Michael, Kay Hamacher, Felix Reinhardt, et al. "SICOR: Subgraph Isomorphism Comparison of RNA Secondary Structures." IEEE/ACM Transactions on Computational Biology and Bioinformatics 17, no. 6 (2020): 2189–95. http://dx.doi.org/10.1109/tcbb.2019.2926711.

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Lipets, V., N. Vanetik, and E. Gudes. "Subsea: an efficient heuristic algorithm for subgraph isomorphism." Data Mining and Knowledge Discovery 19, no. 3 (2009): 320–50. http://dx.doi.org/10.1007/s10618-009-0132-7.

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48

Wong, E. K. "Model matching in robot vision by subgraph isomorphism." Pattern Recognition 25, no. 3 (1992): 287–303. http://dx.doi.org/10.1016/0031-3203(92)90111-u.

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Xiang, Yingzhuo, Jiesi Han, Haijiang Xu, and Xin Guo. "An Improved Heuristic Method for Subgraph Isomorphism Problem." IOP Conference Series: Materials Science and Engineering 231 (September 2017): 012050. http://dx.doi.org/10.1088/1757-899x/231/1/012050.

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Verbitsky, Oleg, and Maksim Zhukovskii. "The Descriptive Complexity of Subgraph Isomorphism Without Numerics." Theory of Computing Systems 63, no. 4 (2018): 902–21. http://dx.doi.org/10.1007/s00224-018-9864-3.

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