CloseGraph: mining closed frequent graph patterns

By Xifeng Yan, Jiawei Han. Knowledge Discovery and Data Mining, 2003

738 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/157e33bc77be75f381e431c436641e8738f7bd3e

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Citations in the corpus

2015 Ieee International Conference on Big Data (big Data) Task-based Recommendation on a Web-scale (neutral): “The research of FPM investigates algorithms for a wide scope of data mining problems, including frequent item set mining [22] , association rule mining [23], frequent sequence mining [24], [25], [26], and frequent tree or graph mining [27], [28].”
Efficiently mining frequent trees in a forest: algorithms and applications (2005, methodology): “Recent methods to mine graphs using a depthfirst tree-based extension have been proposed in [36], [37].”
Mining significant graph patterns by leap search (2008, methodology): “Therefore, we compare three algorithms performance, BB as the baseline, fLeap+fp (fLeap with CloseGraph inside), and fLeap+sLeap (fLeap with sLeap inside).”
Mining sequential patterns by pattern-growth: the PrefixSpan approach (2004, methodology): “A recent study [26] has developed an efficient closed subgraph patternmethod, calledCloseGraph, which is also based on the pattern-growth framework and influenced by this approach.”
A quickstart in frequent structure mining can make a difference (2004, methodology): “Diﬀer-ent codes have been proposed, among which DFS codes (in gSpan [14]), adjacency matrices (in FFSM [5]) and tree codes with backtrack symbols (in FreeTreeMiner [2] and TreeMiner [15]).”
Privacy-preserving SVM using nonlinear kernels on horizontally partitioned data (2006, neutral): “Binary feature vector has been a popular representation in many learning methodologies (e.g., Rule learning like k -DNF and CNF, decision tree, SVM) and used in a wide domains of applications (e.g., automatic diagnosis [18], classifying Web pages and emails [35], mining graphs [34]).”
GPLAG: detection of software plagiarism by program dependence graph analysis (2006, methodology): “CloseGraph: mining closed frequent graph patterns.”
SPIN: mining maximal frequent subgraphs from graph databases (2004, methodology): “This is true for Closegraph [20] as well as for FFSM [9], which is our recently developed depth-ﬁrst subgraph mining algorithm.”
GRAMI: Frequent Subgraph and Pattern Mining in a Single Large Graph (2014, background): “M ARGIN [26] returns maximal subgraphs only, whereas C LOSEGRAPH [30] generates subgraphs that have strictly smaller support than any of their parts.”
History Driven Program Repair (2016, methodology): “We therefore employ an extension of gSpan,5 an implementation of a stateof-the-art frequent graph miner [52] for this task.”
Frequent Subtree Mining – An Overview (2004, neutral): “The frequent connected subgraph problem was also tackled by Yan et al. [45, 46] in the pure depth-ﬁrst algorithm gSpan and by Huan et al. [19] in the combined depth-ﬁrst/breadth-ﬁrst algorithm FFSM.”
Graph indexing: a frequent structure-based approach (2004, methodology): “Yan and Han [21] and Borgelt and Berthold [2] apply the pattern-growth approach to directly generate frequent subgraphs.”
Efficient graphlet kernels for large graph comparison (2009, methodology): “[1]), on the other hand, aim to detect subgraphs that are frequent in a given dataset of graphs.”
Frequent pattern mining: current status and future directions (2007, methodology): “Mining closed graph patterns was studied by Yan and Han (2003) with the proposal of the algo-rithm, CloseGraph , as an extension of gSpan and CloSpan (Yan et al. 2003).”
A survey of frequent subgraph mining algorithms (2012, background): “In (Yan & Han 2003) it was observed that about 1 , 000 , 000 frequent graph patterns were generated from 422 chemical compounds (using a support threshold of 5%); amongst these many were found to structurally repetitive.”
Finding Frequent Patterns in a Large Sparse Graph* (2004, methodology): “Within the context of graphs, the most widely used definition of a pattern is that of a connected subgraph (Borgelt and Berthold, 2002; Yan and Han, 2002, 2003; Inokuchi et al., 2003; Hong et al., 2003; Huan et al., 2003; Kuramochi and Karypis, 2004a) and is the definition that we will use in this…”
Near-optimal Supervised Feature Selection among Frequent Subgraphs (2009, methodology): “Using the gSpan extension CloseGraph [31], they have published experiments on the NCI screenings which we compare to ours in Table 4.”