Graph-based relational learning

Abstract

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.