Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Discovery of frequent DATALOG patterns
Data Mining and Knowledge Discovery
An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Computing Frequent Graph Patterns from Semistructured Data
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Efficient Mining of Frequent Subgraphs in the Presence of Isomorphism
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
CloseGraph: mining closed frequent graph patterns
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining the space of graph properties
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A quickstart in frequent structure mining can make a difference
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Efficient Algorithm for Discovering Frequent Subgraphs
IEEE Transactions on Knowledge and Data Engineering
Mining tree queries in a graph
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Finding Frequent Patterns in a Large Sparse Graph*
Data Mining and Knowledge Discovery
Mining for Tree-Query Associations in a Graph
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
Subgraph Support in a Single Large Graph
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
ArnetMiner: extraction and mining of academic social networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Social Network Extraction of Academic Researchers
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Within-Network Classification Using Local Structure Similarity
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
What is frequent in a single graph?
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
A Combination Approach to Web User Profiling
ACM Transactions on Knowledge Discovery from Data (TKDD)
Learning first-order Horn clauses from web text
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Topic level expertise search over heterogeneous networks
Machine Learning
Infrastructure Pattern Discovery in Configuration Management Databases via Large Sparse Graph Mining
ICDM '11 Proceedings of the 2011 IEEE 11th International Conference on Data Mining
Random walk inference and learning in a large scale knowledge base
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Meta path-based collective classification in heterogeneous information networks
Proceedings of the 21st ACM international conference on Information and knowledge management
AMIE: association rule mining under incomplete evidence in ontological knowledge bases
Proceedings of the 22nd international conference on World Wide Web
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Over the years, frequent subgraphs have been an important kind of targeted pattern in pattern mining research, where most approaches deal with databases holding a number of graph transactions, e.g., the chemical structures of compounds. These methods rely heavily on the downward-closure property (DCP) of the support measure to ensure an efficient pruning of the candidate patterns. When switching to the emerging scenario of single-graph databases such as Google's Knowledge Graph and Facebook's social graph, the traditional support measure turns out to be trivial (either 0 or 1). However, to the best of our knowledge, all attempts to redefine a single-graph support have resulted in measures that either lose DCP, or are no longer semantically intuitive. This paper targets pattern mining in the single-graph setting. We propose mining a new class of patterns called frequent neighborhood patterns, which is free from the "DCP-intuitiveness" dilemma of mining frequent subgraphs in a single graph. A neighborhood is a specific topological pattern in which a vertex is embedded, and the pattern is frequent if it is shared by a large portion (above a given threshold) of vertices. We show that the new patterns not only maintain DCP, but also have equally significant interpretations as subgraph patterns. Experiments on real-life datasets support the feasibility of our algorithms on relatively large graphs, as well as the capability of mining interesting knowledge that is not discovered by prior methods.