MTP

Minus Top-k Partition

Overview

Graph partitioning has been one of fundamental problems in graph mining, and especially enforcing the partition to have balanced sizes has been challenging. We propose a novel approach for graph partitioning. First, we finds a large subgraph with high quality partitions, in terms of conductance. Despite the difficulty of the task for the whole graph, we observe that there is a large connected subgraph whose partition quality is much better than the original. Our proposed method MTP (Minus Top-k Partition) finds such a subgraph by removing hub nodes with large degrees, and taking the remaining giant connected component. Second, we discover a global balanced partition by extending MTP to gbMTP (Global Balanced MTP). gbMTP assigns the excluded nodes in MTP to the partition found by MTP in a greedy way.
    Our results are summarized as follows:

Code

Here is a link to download our implementation for MTP and gbMTP. [Download]

Paper

Our proposed methods MTP and gbMTP are described in the following papers.

Dataset

Source Nodes Edges Description Source Nodes Edges Description
Advogato 5,054 49,821 Trust network Slashdot 51,083 116,573 Reply network
Oregon 11,461 32,730 Router connection Epinions 75,877 405,739 Trust network
CondMat 21,363 91,286 Collaboration network Wordnet 142,505 642,207 Word association network
Donations 23,033 877,625 Who donated whom Gowalla 196,591 950,327 Online social network
Enron 33,695 91,286 Enron email data Amazon 334,863 925,872 Co-purchasing network
Cit-HepPh 34,401 420,784 Citation network Flickr 404,733 2,111,078 Social network in Flickr

People