Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
Handbook of graph grammars and computing by graph transformation: vol. 2: applications, languages, and tools
Approaches to parallel graph-based knowledge discovery
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Scaling Up Inductive Logic Programming by Learning from Interpretations
Data Mining and Knowledge Discovery
IEEE Intelligent Systems
Selected Papers from the 6th International Workshop on Inductive Logic Programming
ILP '96 Selected Papers from the 6th International Workshop on Inductive Logic Programming
Graph-Based Relational Concept Learning
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
gSpan: Graph-Based Substructure Pattern Mining
ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Multi-relational data mining: the current frontiers
ACM SIGKDD Explorations Newsletter
ACM SIGKDD Explorations Newsletter
Graph-based Relational Learning with Application to Security
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
A probabilistic framework for relational clustering
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph-Based Analysis of Human Transfer Learning Using a Game Testbed
IEEE Transactions on Knowledge and Data Engineering
An expert system for detecting automobile insurance fraud using social network analysis
Expert Systems with Applications: An International Journal
Spatial clustering of structured objects
ILP'05 Proceedings of the 15th international conference on Inductive Logic Programming
Graph-based Relational Learning with Application to Security
Fundamenta Informaticae - Advances in Mining Graphs, Trees and Sequences
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Graph-based relational learning (GBRL) differs from logic-based relational learning, as addressed by inductive logic programming techniques, and differs from frequent subgraph discovery, as addressed by many graph-based data mining techniques. Learning from graphs, rather than logic, presents representational issues both in input data preparation and output pattern language. While a form of graph-based data mining, GBRL focuses on identifying novel, not necessarily most frequent, patterns in a graph-theoretic representation of data. This approach to graph-based data mining provides both simplifications and challenges over frequency-based approaches. In this paper we discuss these issues and future directions of graph-based relational learning.