Graph Management and Mining

Representing complex data and relationships using graphs has become ubiquitous in many emerging disciplines such as computational biology, social and communication networking, and scientific data management in general. Several novel methods for managing and mining graph data have been developed to address the steadily increasing amount and complexity of graph data. While many theoretical aspects such as graph isomorphism and partitioning have been studied extensively in graph theory and related fields, new aspects arise from an application point of view. The objective of this seminar is to get an overview of the most recent developments in the field of graph management and graph mining with a particular focus on massive graph data.

Topics include

  • Graph Indexing and Summarization 
  • Graph Query Languages
  • Graph Similarity 
  • Graph Clustering
  • Graph Classification
  • Mining Graph Patterns
  •  Mining Geometric Graphs 
  • Mining Streaming Graph Data

Time and Location:

Seminar: Thursdays, 2-4pm, INF 368, Room 248
First meeting: Thursday, April 14, 2pm, INF 368, Room 248

Suggested Previous Knowledge:

Data Mining, Algorithms and Data Structures, Efficient Algorithms


Information about the seminar topics and corresponding literature (conference and journal papers) will be given during the first meeting. All papers will also be distributed through the Moodle Web-site for this seminar.

Credit Points:

In order to receive the 4 ECST for this seminar, students have to (1) participate in all presentations, (2) give a presentation (about 40 minutes), and (3) prepare a technical report covering the topic they presented in the seminar. More details will be given during the first meeting.


Students with Computer Science (Informatik) as major or minor.

Further Information:

Prof. Dr. Michael Gertz,, INF 348, Room 12b. All material related to this seminar will be provided through the Moodle Web-site for this seminar; see also