SPIN: mining maximal frequent subgraphs from graph databases

By Jun Huan, Wei Wang, Jan F. Prins, Jiong Yang. Knowledge Discovery and Data Mining, 2004

381 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/5df863695a0842c43d4e3950e72f2e5e32cdb10f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Hong Cheng
2. Jiawei Han
3. Chuntao Jiang
4. Yuanyuan Tian
5. Xifeng Yan
6. Shijie Zhang
7. Dong Xin
8. Frans Coenen

Citations in the corpus

• K-isomorphism: privacy preserving network publication against structural attacks (2010, background): “Second, most PAGs actually share with each other a large number of common subgraphs (as also pointed out in maximal frequent subgraph mining [13]).”
• GADDI: distance index based subgraph matching in biological networks (2009, background): “As a result, graph database research has attracted a large amount of attention from the database and data mining communities, such as subgraph search in a database of multiple graphs [5, 23, 38, 46, 51, 52, 53, 16], approximate subgraph matching [17, 40, 44, 47, 48, 11], frequent subgraph mining [21”
• A survey of frequent subgraph mining algorithms (2012, methodology): “SPIN (Huan et al., 2004) is a spanning tree-based FSM algorithm designed to discover only maximal frequent subgraphs with the intention of reducing the overall computation cost.”
• Frequent pattern mining: current status and future directions (2007, methodology): “Mining coherent subgraphs was studied by Huan et al. (2004).”
• Efficient aggregation for graph summarization (2008, methodology): “Methods that mine graphs for frequent patterns [11, 19, 20, 23] are also employed to understand the characteristics of large graphs.”
• Attributed Graph Mining and Matching: An Attempt to Define and Extract Soft Attributed Patterns (2014, background): “1(bottom), MFS extraction [22, 12, 10, 23] is based on graph isomorphisms and usually require 1) the distinguishing (topological) structure of the graph pattern, and 2) pre-defined distinct node/edge labels or potential inter-graph node correspondences determined by local consistency.”