Mining significant graph patterns by leap search
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
RAM: Randomized Approximate Graph Mining
SSDBM '08 Proceedings of the 20th international conference on Scientific and Statistical Database Management
On effective presentation of graph patterns: a structural representative approach
Proceedings of the 17th ACM conference on Information and knowledge management
Large-scale graph mining using backbone refinement classes
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part II
Mining graph patterns efficiently via randomized summaries
Proceedings of the VLDB Endowment
Mining correlated subgraphs in graph databases
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
MARGIN: Maximal frequent subgraph mining
ACM Transactions on Knowledge Discovery from Data (TKDD)
DESSIN: mining dense subgraph patterns in a single graph
SSDBM'10 Proceedings of the 22nd international conference on Scientific and statistical database management
Mining for paths in flow graphs
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Fast, effective molecular feature mining by local optimization
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part III
CP-index: on the efficient indexing of large graphs
Proceedings of the 20th ACM international conference on Information and knowledge management
Efficient mining of correlated sequential patterns based on null hypothesis
Proceedings of the 2012 international workshop on Web-scale knowledge representation, retrieval and reasoning
A direct mining approach to efficient constrained graph pattern discovery
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Interesting event detection through hall of fame rankings
Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
The essence of knowledge (bases) through entity rankings
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Frequent subgraph summarization with error control
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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In this paper, we introduce the concept of -orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are -orthogonal if their similarity is bounded above by . Each -orthogonal pattern is also a representative for those patterns that are at least similar to it. Given user defined , [0, 1], the goal is to mine an -orthogonal, -representative set that minimizes the set of unrepresented patterns. We present ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns. ORIGAMI first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. ORIGAMI then extracts an orthogonal, -representative set from the mined maximal patterns. We show the effectiveness of our algorithm on a number of real and synthetic datasets. In particular, we show that our method is able to extract high quality patterns even in cases where existing enumerative graph mining methods fail to do so.