Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
A database perspective on knowledge discovery
Communications of the ACM
Authoritative sources in a hyperlinked environment
Proceedings of the ninth annual ACM-SIAM symposium on Discrete algorithms
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Proceedings of the 11th international conference on World Wide Web
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Relational Association Rules: Getting WARMeR
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Efficiently mining frequent trees in a forest
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Scaling personalized web search
WWW '03 Proceedings of the 12th international conference on World Wide Web
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining frequent cross-graph quasi-cliques
ACM Transactions on Knowledge Discovery from Data (TKDD)
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Mining frequent neighborhood patterns in a large labeled graph
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Existing data mining algorithms on graphs look for nodes satisfying specific properties, such as specific notions of structural similarity or specific measures of link-based importance. While such analyses for predetermined properties can be effective in well-understood domains, sometimes identifying an appropriate property for analysis can be a challenge, and focusing on a single property may neglect other important aspects of the data. In this paper, we develop a foundation for mining the properties themselves. We present a theoretical framework defining the space of graph properties, a variety of mining queries enabled by the framework, techniques to handle the enormous size of the query space, and an experimental system called F-Miner that demonstrates the utility and feasibility of property mining.