Automatic multi-partite graph generation from arbitrary data

• A model for automatic generation of multipartite graphs from arbitrary data is proposed.
• Easy and flexible basis for transforming an arbitrary dataset into a graph.
• GraphGen, a tool implementing the model for graph generation, is presented.
• Use cases in real scenarios: query logs, bibliographic databases, and social networks.

In this paper we present a generic model for automatic generation of basic multi-partite graphs obtained from collections of arbitrary input data following user indications. The paper also presents GraphGen, a tool that implements this model. The input data is a collection of complex objects composed by a set or list of heterogeneous elements. Our tool provides a simple interface for the user to specify the types of nodes that are relevant for the application domain in each case. The nodes and the relationships between them are derived from the input data through the application of a set of derivation rules specified by the user. The resulting graph can be exported in the standard GraphML format so that it can be further processed with other graph management and mining systems. We end by giving some examples in real scenarios that show the usefulness of this model.