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Journal articles on the topic 'Graph-based analysis'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Bhosale, Bharat. "Curvelet Based Multiresolution Analysis of Graph Neural Networks." International Journal of Applied Physics and Mathematics 4, no. 5 (2014): 313–23. http://dx.doi.org/10.7763/ijapm.2014.v4.304.

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2

Weghenkel, Björn, Asja Fischer, and Laurenz Wiskott. "Graph-based predictable feature analysis." Machine Learning 106, no. 9-10 (May 9, 2017): 1359–80. http://dx.doi.org/10.1007/s10994-017-5632-x.

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3

Micheloyannis, Sifis. "Graph-based network analysis in schizophrenia." World Journal of Psychiatry 2, no. 1 (2012): 1. http://dx.doi.org/10.5498/wjp.v2.i1.1.

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4

Coffman, Thayne, Seth Greenblatt, and Sherry Marcus. "Graph-based technologies for intelligence analysis." Communications of the ACM 47, no. 3 (March 2004): 45–47. http://dx.doi.org/10.1145/971617.971643.

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5

Stephen, Mutua, Changgui Gu, and Huijie Yang. "Visibility Graph Based Time Series Analysis." PLOS ONE 10, no. 11 (November 16, 2015): e0143015. http://dx.doi.org/10.1371/journal.pone.0143015.

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6

Perak, Benedikt, and Tajana Ban Kirigin. "Corpus-Based Syntactic-Semantic Graph Analysis." Rasprave Instituta za hrvatski jezik i jezikoslovlje 46, no. 2 (October 30, 2020): 957–96. http://dx.doi.org/10.31724/rihjj.46.2.27.

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This research exemplifies the corpus-based graph approach to the syntactic-semantic analysis of a concept feeling using the Construction Grammar Conceptual network methodology. by constructing a lexical network from grammatically tagged collocations of the english and the Croatian web corpora, the structure of the semantic domains is revealed as a set of sub-graphs derived from the source lexeme’s friend-of-a-friend graph. the subgraph structures, calculated with the community detection algorithm, are interpreted as the semantic domains associated with the source lexeme’s conceptual matrix. lexical structures are analyzed using a centrality algorithm that determines the overall rank of the salience and semantic relatedness to the source concept feeling. this empirical approach can be used for developing nlP methods and tasks, such as computing semantic similarity, sense disambiguation, sense structuring, as well as for comparative corpus and cross-cultural studies. ConGraCnet has a web application on the page <a target="_blank" rel="nofollow" href="http://emocnet.uniri.hr/congracnet">http://emocnet.uniri.hr/congracnet</a>.

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7

Qiang Luo, Quan Zhang, Mohsin Hafeez, Hua Xie, and Jie Li. "Graph-based Topology Analysis of Basin Structure." Journal of Convergence Information Technology 6, no. 5 (May 31, 2011): 245–50. http://dx.doi.org/10.4156/jcit.vol6.issue5.28.

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8

M. Dakhare, Kalyani. "Semantic Information Search Based on Graph Analysis." HELIX 8, no. 5 (August 31, 2018): 3927–31. http://dx.doi.org/10.29042/2018-3927-3931.

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9

Long, Seth, and Lawrence B. Holder. "Graph-Based Shape Analysis for MRI Classification." International Journal of Knowledge Discovery in Bioinformatics 2, no. 2 (April 2011): 19–33. http://dx.doi.org/10.4018/jkdb.2011040102.

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Searching for correlations between brain structure and attributes of a person’s intellectual state is a process which may be better done by automation than by human labor. Such an automated system would be capable of performing classification based on the discovered correlation, which would be means of testing how accurate the discovered correlation is. The authors have developed a system which generates a graph-based representation of the shape of the third and lateral ventricles based on a structural MRI, and classifies images represented in this manner. The system is evaluated on accuracy at classifying individuals showing cognitive impairment to Alzheimer’s Disease. Classification accuracy is 74.2% when individuals with CDR 0.5 are included as impaired in a balanced dataset of 166 images, and 79.3% accuracy when differentiating individuals with CDR at least 1.0 and healthy individuals in a balanced dataset of 54 images. Finally, the system is used to classify MR images according to level of education, with 77.2% accuracy differentiating highly-educated individuals from those for whom no higher education is listed, in a balanced dataset of 178 images.

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10

Caux, C., M. Barth, and R. De Guio. "Graph Based Tools for Production Flow Analysis." IFAC Proceedings Volumes 33, no. 17 (July 2000): 885–89. http://dx.doi.org/10.1016/s1474-6670(17)39520-4.

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11

Anderson, Blake, Daniel Quist, Joshua Neil, Curtis Storlie, and Terran Lane. "Graph-based malware detection using dynamic analysis." Journal in Computer Virology 7, no. 4 (June 8, 2011): 247–58. http://dx.doi.org/10.1007/s11416-011-0152-x.

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12

Ren, Sheng-bing, Xi-e. Wang, Zhi-gang Hu, Ge Hu, and Guo-jun Wang. "Graph transformation based reduction analysis of PID." ACM SIGSOFT Software Engineering Notes 34, no. 4 (July 6, 2009): 1–5. http://dx.doi.org/10.1145/1543405.1543422.

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13

Kruck, S. E., Faye Teer, and William A. Christian. "GSLAP: a graph‐based web analysis tool." Industrial Management & Data Systems 108, no. 2 (March 14, 2008): 162–72. http://dx.doi.org/10.1108/02635570810847554.

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14

Pokorádi, L. "Graph model-based analysis of technical systems." IOP Conference Series: Materials Science and Engineering 393 (August 10, 2018): 012007. http://dx.doi.org/10.1088/1757-899x/393/1/012007.

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15

Cook, Diane, Lawrence Holder, Sandy Thompson, Paul Whitney, and Lawrence Chilton. "Graph-Based Analysis of Nuclear Smuggling Data." Journal of Applied Security Research 4, no. 4 (October 6, 2009): 501–17. http://dx.doi.org/10.1080/19361610903176310.

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16

Durango, Sebastián, Jorge Correa, and Oscar E. Ruiz. "Graph-based structural analysis of planar mechanisms." Meccanica 52, no. 1-2 (March 3, 2016): 441–55. http://dx.doi.org/10.1007/s11012-016-0403-5.

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17

Tang, Jin, Bo Jiang, Chin-Chen Chang, and Bin Luo. "Graph structure analysis based on complex network." Digital Signal Processing 22, no. 5 (September 2012): 713–25. http://dx.doi.org/10.1016/j.dsp.2012.04.011.

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18

Yongyue, Chen, and Li Ruijing. "Academic Genealogy Analysis Based on Knowledge Graph." International Journal of Economics, Finance and Management Sciences 9, no. 1 (2021): 29. http://dx.doi.org/10.11648/j.ijefm.20210901.14.

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19

Debusscher, Bos, and Frieke Van Coillie. "Object-Based Flood Analysis Using a Graph-Based Representation." Remote Sensing 11, no. 16 (August 12, 2019): 1883. http://dx.doi.org/10.3390/rs11161883.

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The amount of freely available satellite data is growing rapidly as a result of Earth observation programmes, such as Copernicus, an initiative of the European Space Agency. Analysing these huge amounts of geospatial data and extracting useful information is an ongoing pursuit. This paper presents an alternative method for flood detection based on the description of spatio-temporal dynamics in satellite image time series (SITS). Since synthetic aperture radar (SAR) satellite data has the capability of capturing images day and night, irrespective of weather conditions, it is the preferred tool for flood mapping from space. An object-based approach can limit the necessary computer power and computation time, while a graph-based approach allows for a comprehensible interpretation of dynamics. This method proves to be a useful tool to gain insight in a flood event. Graph representation helps to identify and locate entities within the study site and describe their evolution throughout the time series.

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20

Li, Enpu, Xiaoying Zhuang, Wenbo Zheng, and Yongchang Cai. "Effect of graph generation on slope stability analysis based on graph theory." Journal of Rock Mechanics and Geotechnical Engineering 6, no. 4 (August 2014): 380–86. http://dx.doi.org/10.1016/j.jrmge.2014.05.003.

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21

Koam, Ali N. A., Muhammad Akram, and Peide Liu. "Decision-Making Analysis Based on Fuzzy Graph Structures." Mathematical Problems in Engineering 2020 (August 12, 2020): 1–30. http://dx.doi.org/10.1155/2020/6846257.

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A graph structure is a useful framework to solve the combinatorial problems in various fields of computational intelligence systems and computer science. In this research article, the concept of fuzzy sets is applied to the graph structure to define certain notions of fuzzy graph structures. Fuzzy graph structures can be very useful in the study of various structures, including fuzzy graphs, signed graphs, and the graphs having labeled or colored edges. The notions of the fuzzy graph structure, lexicographic-max product, and degree and total degree of a vertex in the lexicographic-max product are introduced. Further, the proposed concepts are explained through several numerical examples. In particular, applications of the fuzzy graph structures in decision-making process, regarding detection of marine crimes and detection of the road crimes, are presented. Finally, the general procedure of these applications is described by an algorithm.

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22

Bordoloi, Monali, and Saroj Kr Biswas. "Graph based sentiment analysis using keyword rank based polarity assignment." Multimedia Tools and Applications 79, no. 47-48 (July 24, 2020): 36033–62. http://dx.doi.org/10.1007/s11042-020-09289-4.

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23

Mao, Jian, Xiang Li, Qixiao Lin, and Zhenyu Guan. "Deeply understanding graph-based Sybil detection techniques via empirical analysis on graph processing." China Communications 17, no. 10 (October 2020): 82–96. http://dx.doi.org/10.23919/jcc.2020.10.006.

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24

Pachayappan, Murugaiyan, and Ramakrishnan Venkatesakumar. "A Graph Theory Based Systematic Literature Network Analysis." Theoretical Economics Letters 08, no. 05 (2018): 960–80. http://dx.doi.org/10.4236/tel.2018.85067.

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25

LIU, Qiang, Jian-Ping YIN, Zhi-Ping CAI, and Jie-Ren CHENG. "Uncertain-Graph Based Method for Network Vulnerability Analysis." Journal of Software 22, no. 6 (June 24, 2011): 1398–412. http://dx.doi.org/10.3724/sp.j.1001.2011.03819.

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26

Jie, Yin, Yu Dan, Ye Gang, and Ma Shilong. "Termination Analysis of Sequential Program Based on Graph." Procedia Engineering 29 (2012): 1999–2003. http://dx.doi.org/10.1016/j.proeng.2012.01.251.

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27

Guo, Diansheng, Shufan Liu, and Hai Jin. "A graph-based approach to vehicle trajectory analysis." Journal of Location Based Services 4, no. 3-4 (September 2010): 183–99. http://dx.doi.org/10.1080/17489725.2010.537449.

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28

Yu-Liang Wu, S. Tsukiyama, and M. Marek-Sadowska. "Graph based analysis of 2-D FPGA routing." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 15, no. 1 (January 1996): 33–44. http://dx.doi.org/10.1109/43.486270.

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29

Olejarczyk, Elzbieta, and Wojciech Jernajczyk. "Graph-based analysis of brain connectivity in schizophrenia." PLOS ONE 12, no. 11 (November 30, 2017): e0188629. http://dx.doi.org/10.1371/journal.pone.0188629.

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30

Jiřík, M., T. Ryba, and M. Železný. "Gabor filter and graph cut based texture analysis." Pattern Recognition and Image Analysis 22, no. 1 (March 2012): 215–20. http://dx.doi.org/10.1134/s1054661812010208.

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31

Carra, Damiano, Renato Lo Cigno, and Ernst W. Biersack. "Graph Based Analysis of Mesh Overlay Streaming Systems." IEEE Journal on Selected Areas in Communications 25, no. 9 (December 2007): 1667–77. http://dx.doi.org/10.1109/jsac.2007.071206.

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32

Lillo, Felipe, Leidy García, and Valentín Santander. "Dynamics of global remittances: A graph-based analysis." Mathematical Social Sciences 87 (May 2017): 64–71. http://dx.doi.org/10.1016/j.mathsocsci.2017.02.005.

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33

da S. Torres, R., A. X. Falcão, and L. da F. Costa. "A graph-based approach for multiscale shape analysis." Pattern Recognition 37, no. 6 (June 2004): 1163–74. http://dx.doi.org/10.1016/j.patcog.2003.10.007.

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34

DUCK, GREGORY J., JOXAN JAFFAR, and ROLAND H. C. YAP. "Shape Neutral Analysis of Graph-based Data-structures." Theory and Practice of Logic Programming 18, no. 3-4 (July 2018): 470–83. http://dx.doi.org/10.1017/s147106841800025x.

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AbstractMalformed data-structures can lead to runtime errors such as arbitrary memory access or corruption. Despite this, reasoning over data-structure properties for low-level heap manipulating programs remains challenging. In this paper we present a constraint-based program analysis that checks data-structure integrity, w.r.t. given target data-structure properties, as the heap is manipulated by the program. Our approach is to automatically generate a solver for properties using the type definitions from the target program. The generated solver is implemented using a Constraint Handling Rules (CHR) extension of built-in heap, integer and equality solvers. A key property of our program analysis is that the target data-structure properties are shape neutral, i.e., the analysis does not check for properties relating to a given data-structure graph shape, such as doubly-linked-lists versus trees. Nevertheless, the analysis can detect errors in a wide range of data-structure manipulating programs, including those that use lists, trees, DAGs, graphs, etc. We present an implementation that uses the Satisfiability Modulo Constraint Handling Rules (SMCHR) system. Experimental results show that our approach works well for real-world C programs.

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35

Toni, Laura, and Pascal Frossard. "Prioritized Random MAC Optimization Via Graph-Based Analysis." IEEE Transactions on Communications 63, no. 12 (December 2015): 5002–13. http://dx.doi.org/10.1109/tcomm.2015.2494044.

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36

姚, 远. "Citespace-Based Knowledge Graph Analysis of Smart Library." Advances in Social Sciences 07, no. 07 (2018): 1032–41. http://dx.doi.org/10.12677/ass.2018.77155.

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37

Kolesnikov, Victor, Vasily Anikin, Ekaterina Mosolova, Alexey Faizliev, Sergei Mironov, Maria Zemlyanskaya, Michael Pleshakov, and Sergei Sidorov. "Food Chain Analysis Based on Graph Centrality Indicators." Journal of Physics: Conference Series 1334 (October 2019): 012004. http://dx.doi.org/10.1088/1742-6596/1334/1/012004.

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38

Guo, Taolin, Junzhou Luo, Kai Dong, and Ming Yang. "Differentially private graph-link analysis based social recommendation." Information Sciences 463-464 (October 2018): 214–26. http://dx.doi.org/10.1016/j.ins.2018.06.054.

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39

Sugimoto, K. "2P2-G6 Kinematic Analysis based on Graph Theory." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2001 (2001): 74. http://dx.doi.org/10.1299/jsmermd.2001.74_6.

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40

Islam, S. A. "Graph‐based approach towards hardware Trojan vulnerability analysis." Electronics Letters 56, no. 17 (August 2020): 868–71. http://dx.doi.org/10.1049/el.2020.1005.

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41

Jia, Yongqiang, and Lu Gan. "Radiometric Identification Based on Global Graph Fisher Analysis." International Journal of Signal Processing, Image Processing and Pattern Recognition 10, no. 4 (April 30, 2017): 63–78. http://dx.doi.org/10.14257/ijsip.2017.10.4.06.

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42

Wang, Wei, and Thomas E. Daniels. "A Graph Based Approach Toward Network Forensics Analysis." ACM Transactions on Information and System Security 12, no. 1 (October 2008): 1–33. http://dx.doi.org/10.1145/1410234.1410238.

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43

Ly, Nam Hoai, Qian Du, and James E. Fowler. "Collaborative Graph-Based Discriminant Analysis for Hyperspectral Imagery." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7, no. 6 (June 2014): 2688–96. http://dx.doi.org/10.1109/jstars.2014.2315786.

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44

Korres, G. N., P. J. Katsikas, K. A. Clements, and P. W. Davis. "Numerical observability analysis based on network graph theory." IEEE Transactions on Power Systems 18, no. 3 (August 2003): 1035–45. http://dx.doi.org/10.1109/tpwrs.2003.814882.

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45

Yan, Xifeng, Philip S. Yu, and Jiawei Han. "Graph indexing based on discriminative frequent structure analysis." ACM Transactions on Database Systems 30, no. 4 (December 2005): 960–93. http://dx.doi.org/10.1145/1114244.1114248.

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46

Nam Hoai Ly, Qian Du, and James E. Fowler. "Sparse Graph-Based Discriminant Analysis for Hyperspectral Imagery." IEEE Transactions on Geoscience and Remote Sensing 52, no. 7 (July 2014): 3872–84. http://dx.doi.org/10.1109/tgrs.2013.2277251.

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47

Carneiro, Gustavo. "Artistic Image Analysis Using Graph-Based Learning Approaches." IEEE Transactions on Image Processing 22, no. 8 (August 2013): 3168–78. http://dx.doi.org/10.1109/tip.2013.2260167.

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48

Chai, Chunlei, Guoliang Lu, Ruyun Wang, Chen Lyu, Lei Lyu, Peng Zhang, and Hong Liu. "Graph-based structural difference analysis for video summarization." Information Sciences 577 (October 2021): 483–509. http://dx.doi.org/10.1016/j.ins.2021.07.012.

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49

Karanwal, Shekhar. "Graph Based Structure Binary Pattern for Face Analysis." Optik 241 (September 2021): 166965. http://dx.doi.org/10.1016/j.ijleo.2021.166965.

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50

Heimann, Peter, Carl-Arndt Krapp, Bernhard Westfechtel, and Gregor Joeris. "Graph-Based Software Process Management." International Journal of Software Engineering and Knowledge Engineering 07, no. 04 (December 1997): 431–55. http://dx.doi.org/10.1142/s0218194097000254.

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Software process dynamics challenge the capabilities of process-centered software engineering environments. Dynamic task nets represent evolving software processes by hierarchically organized nets of tasks which are connected by control, data, and feedback flows. Project managers operate on dynamic task nets in order to assess the current status of a project, trace its history, perform impact analysis, handle feedback, adapt the project plan to changed product structures, etc. Developers are supported through task agendas and provision of tools and documents. Chained tasks may be executed in parallel (simultaneous engineering), and cooperation is controlled through releases of document versions. Dynamic task nets are formally specified by a programmed graph rewriting system. Operations on task nets are specified declaratively by graph rewrite rules at a high level of abstraction. Furthermore, editing, analysis, and execution steps on a dynamic task net, which may be interleaved seamlessly, are described in a uniform formalism.

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