喺數學同電腦科學裡邊,圖編輯距離英文graph edit distanceGED)係用嚟量度兩幅有幾相似,係 1983年由Alberto Sanfeliu同傅京孫首先將呢個概念用數學表達出嚟[1]。一個主要嘅功用係做非精確圖配對(inexact graph matching),例如機械學習入邊嘅容錯規律辨識[2] 圖編輯距離同字串編輯距離有啲關係。如果將字串睇做連通有向無環、最大一嘅圖嘅話,各種經典嘅距離例如Levenshtein距離[3][4]漢明距離[5]Jaro-Winkler距離都可以睇做某一類圖嘅圖編輯距離,另外,圖編輯距離亦都係樹編輯距離嘅廣義化。[6][7][8][9]

定義同性質 編輯

圖編輯距離嘅定義好取決於用緊邊一種圖,例如圖嘅頂點有無標號,點樣標號,啲邊有無方向等等。一般嚟講,畀一柞圖編輯操作(graph edit operation,又叫基礎圖操作elementary graph operation),兩幅圖  之間嘅圖編輯距離 定義係

 

呢度 裝住所有由圖 去圖 嘅編輯路徑, 係圖編輯操作   嘅成本。

基本圖操作一般包括頂點同邊嘅加插(insertion)、刪除(deletion)同改標號(substitution)。另外仲可能包埋加插一粒頂點落一條邊中間嘅裂邊(edge splitting)同移除一粒度二嘅頂點嘅合邊(edge contraction)。雖然好多複雜嘅操作都可以用簡單嘅嘅操作複合而成,不過包埋呢啲複雜操作嘅話,如果佢嘅成本比啲簡單操作加埋仲少,就會有一個精細啲嘅成本函數。

基礎圖編輯操作嘅深入研究可以喺(Serratosa, Cortés 2015[10])、(Serratosa 2019[11])、(Serratosa 2021[12])到搵到。

有一啲方法可以自動計算呢啲基礎圖編輯操作[13][14][15][16][17],甚至有啲係喺網上邊學[18]

應用 編輯

圖編輯距離可以用喺手寫辨識[19]指紋辨識[20]化學資訊學[21]

演算法同複雜度 編輯

計算圖編輯距離嘅精確演算法通常係將條問題睇做「去搵最短嘅編輯路徑」,即係諗做一條最短路徑問題,之後用A* 搜尋演算法做。

精確演算法以外,有一啲高效嘅估算演算法,大部份都係行立方時間[22][23] [24] [25] [26]。甚至有行綫性時間嘅[27]

雖然以上講嘅演算法喺實際用途嚟講係行得到,但其實搵圖編輯距離呢個問題係NP-難嘅(網上證明,睇Zeng et al.嘅第二節),甚至係難以估算嘅(APX-難[28])。

參考資料 編輯

  1. Sanfeliu, Alberto; Fu, King-Sun (1983). "A distance measure between attributed relational graphs for pattern recognition". IEEE Transactions on Systems, Man and Cybernetics. 13 (3): 353–363. doi:10.1109/TSMC.1983.6313167.
  2. Gao, Xinbo; Xiao, Bing; Tao, Dacheng; Li, Xuelong (2010). "A survey of graph edit distance". Pattern Analysis and Applications. 13: 113–129. doi:10.1007/s10044-008-0141-y.
  3. Влади́мир И. Левенштейн (1965). Двоичные коды с исправлением выпадений, вставок и замещений символов [Binary codes capable of correcting deletions, insertions, and reversals]. Доклады Академий Наук СССР (俄文). 163 (4): 845–848.
  4. Levenshtein, Vladimir I. (February 1966). "Binary codes capable of correcting deletions, insertions, and reversals". Soviet Physics Doklady. 10 (8): 707–710.
  5. Hamming, Richard W. (1950). "Error detecting and error correcting codes" (PDF). Bell System Technical Journal. 29 (2): 147–160. doi:10.1002/j.1538-7305.1950.tb00463.x. hdl:10945/46756. MR 0035935. 歸檔時間2006-05-25.{{cite journal}}: CS1 maint: bot: original URL status unknown (link)
  6. Shasha, D; Zhang, K (1989). "Simple fast algorithms for the editing distance between trees and related problems". SIAM J. Comput. 18 (6): 1245–1262. CiteSeerX 10.1.1.460.5601. doi:10.1137/0218082.
  7. Zhang, K (1996). "A constrained edit distance between unordered labeled trees". Algorithmica. 15 (3): 205–222. doi:10.1007/BF01975866.
  8. Bille, P (2005). "A survey on tree edit distance and related problems". Theor. Comput. Sci. 337 (1–3): 22–34. doi:10.1016/j.tcs.2004.12.030.
  9. Demaine, Erik D.; Mozes, Shay; Rossman, Benjamin; Weimann, Oren (2010). "An optimal decomposition algorithm for tree edit distance". ACM Transactions on Algorithms. 6 (1): A2. arXiv:cs/0604037. CiteSeerX 10.1.1.163.6937. doi:10.1145/1644015.1644017. MR 2654906.
  10. Serratosa, Francesc; Cortés, Xavier (2015). Graph Edit Distance: moving from global to local structure to solve the graph-matching problem. Pattern Recognition Letters, 65, pp: 204-210.
  11. Serratosa, Francesc (2019). Graph edit distance: Restrictions to be a metric. Pattern Recognition, 90, pp: 250-256.
  12. Serratosa, Francesc (2021). Redefining the Graph Edit Distance. S. N. Computer Science, pp: 2-438.
  13. Santacruz, Pep; Serratosa, Francesc (2020). Learning the graph edit costs based on a learning model applied to sub-optimal graph matching. Neural Processing Letters, 51, pp: 881–904.
  14. Algabli, Shaima; Serratosa, Francesc (2018). Embedding the node-to-node mappings to learn the Graph edit distance parameters. Pattern Recognition Letters, 112, pp: 353-360.
  15. Xavier, Cortés; Serratosa, Francesc (2016). Learning Graph Matching Substitution Weights based on the Ground Truth Node Correspondence. International Journal of Pattern Recognition and Artificial Intelligence, 30(2), pp: 1650005 [22 pages].
  16. Xavier, Cortés; Serratosa, Francesc (2015). Learning Graph-Matching Edit-Costs based on the Optimality of the Oracle's Node Correspondences. Pattern Recognition Letters, 56, pp: 22 - 29.
  17. Conte, Donatello; Serratosa, Francesc (2020). Interactive Online Learning for Graph Matching using Active Strategies. Knowledge Based Systems, 105, pp: 106275.
  18. Rica, Elena; Álvarez, Susana; Serratosa, Francesc (2021). On-line learning the graph edit distance costs. Pattern Recognition Letters, 146, pp: 52-62.
  19. Fischer, Andreas; Suen, Ching Y.; Frinken, Volkmar; Riesen, Kaspar; Bunke, Horst (2013), "A Fast Matching Algorithm for Graph-Based Handwriting Recognition", Graph-Based Representations in Pattern Recognition, Lecture Notes in Computer Science,第7877卷, pp. 194–203, doi:10.1007/978-3-642-38221-5_21, ISBN 978-3-642-38220-8
  20. Neuhaus, Michel; Bunke, Horst (2005), "A Graph Matching Based Approach to Fingerprint Classification using Directional Variance", Audio- and Video-Based Biometric Person Authentication, Lecture Notes in Computer Science,第3546卷, pp. 191–200, doi:10.1007/11527923_20, ISBN 978-3-540-27887-0
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  23. Riesen, Kaspar (Feb 2016). Structural Pattern Recognition with Graph Edit Distance: Approximation Algorithms and Applications. Advances in Computer Vision and Pattern Recognition. Springer. ISBN 978-3319272511.
  24. Serratosa, Francesc (2014). Fast Computation of Bipartite Graph Matching. Pattern Recognition Letters, 45, pp: 244 - 250.
  25. Serratosa, Francesc (2015). Speeding up Fast Bipartite Graph Matching through a new cost matrix. International Journal of Pattern Recognition and Artificial Intelligence, 29 (2), 1550010, [17 pages].
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  27. Santacruz, Pep; Serratosa, Francesc (2018). Error-tolerant graph matching in linear computational cost using an initial small partial matching. Pattern Recognition Letters.
  28. Lin, Chih-Long (1994-08-25). "Hardness of approximating graph transformation problem". 出自 Du, Ding-Zhu; Zhang, Xiang-Sun (編). Algorithms and Computation. Lecture Notes in Computer Science (英文).第834卷. Springer Berlin Heidelberg. pp. 74–82. doi:10.1007/3-540-58325-4_168. ISBN 9783540583257.