Improving frequent subgraph mining in the presence of symmetry

author: Christian Desrosiers, Polytechnique Montréal
published: Sept. 5, 2007,   recorded: August 2007,   views: 3273

Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


The difficulty of the frequent subgraph mining problem arises from the tasks of enumerating the subgraphs and calculating their support in the dataset. If the dataset graphs have additional information in the form of labels, these problems can be solved quite easily. However, if the dataset graphs are unlabeled or only have a few labels, then the complexity of these problems greatly reduces the number and sizes of the dataset graphs that can be managed. Thus far, researchers working on the frequent subgraph mining problem have given little attention to such datasets, and current algorithms tend to do poorly on them. Yet, there are many applications which deal with this type of data, mainly in the fields of compute vision where the data is structured as 2D or 3D meshes [8], or communication/transportation networks where the information is mostly topological.

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: