Property graphs are an intuitive way to model, analyze and visualize complex relationships among heterogeneous data objects, for example, as they occur in social, biological and information networks. These graphs typically contain thousands or millions of vertices and edges and their entire representation can easily overwhelm an analyst. One way to reduce complexity is the grouping of vertices and edges to summary graphs. We presented an algorithm for graph grouping with support for attribute aggregation and structural summarization by user-defined vertex and edge properties. The algorithm is part of GRADOOP, an open-source system for graph analytics. GRADOOP is implemented on top of Apache Flink, a state-of-the-art distributed dataflow framework, and thus allows us to scale graph analytical programs across multiple machines.
We implemented a demo application for visualizing summary graphs. A user can adjust the parameters for graph grouping and execute the operator locally using Apache Flink.
- Clone the repo
- Run
org.gradoop.demos.grouping.server.Server - Navigate to
http://localhost:2342/gradoop/grouping/demo.html - Select data set from drop down list (initial meta data computation takes a few seconds)
- Enjoy
- Create a CSV graph using Gradoop (see gradoop-examples)
- Copy CSV graph to
src/main/resources/data - Restart the server