Abstract
Suppose we arc given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce Dot2Dot, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.
| Original language | English (US) |
|---|---|
| Title of host publication | SIAM International Conference on Data Mining 2013, SMD 2013 |
| Publisher | Society for Industrial and Applied Mathematics Publications |
| Pages | 37-45 |
| Number of pages | 9 |
| ISBN (Print) | 9781627487245 |
| State | Published - 2013 |
| Externally published | Yes |
| Event | 13th SIAM International Conference on Data Mining, SMD 2013 - Austin, United States Duration: May 2 2013 → May 4 2013 |
Other
| Other | 13th SIAM International Conference on Data Mining, SMD 2013 |
|---|---|
| Country | United States |
| City | Austin |
| Period | 5/2/13 → 5/4/13 |
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ASJC Scopus subject areas
- Theoretical Computer Science
- Information Systems
- Signal Processing
- Software
Cite this
Mining connection pathways for marked nodes in large graphs. / Akoglu, Leman; Vreeken, Jilles; Tong, Hanghang; Chau, Duen Horng; Tatti, Nikolaj; Faloutsos, Christos.
SIAM International Conference on Data Mining 2013, SMD 2013. Society for Industrial and Applied Mathematics Publications, 2013. p. 37-45.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Mining connection pathways for marked nodes in large graphs
AU - Akoglu, Leman
AU - Vreeken, Jilles
AU - Tong, Hanghang
AU - Chau, Duen Horng
AU - Tatti, Nikolaj
AU - Faloutsos, Christos
PY - 2013
Y1 - 2013
N2 - Suppose we arc given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce Dot2Dot, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.
AB - Suppose we arc given a large graph in which, by some external process, a handful of nodes are marked. What can we say about these nodes? Are they close together in the graph? or, if segregated, how many groups do they form? We approach this problem by trying to find sets of simple connection pathways between sets of marked nodes. We formalize the problem in terms of the Minimum Description Length principle: a pathway is simple when we need only few bits to tell which edges to follow, such that we visit all nodes in a group. Then, the best partitioning is the one that requires the least number of bits to describe the paths that visit all the marked nodes. We prove that solving this problem is NP-hard, and introduce Dot2Dot, an efficient algorithm for partitioning marked nodes by finding simple pathways between nodes. Experimentation shows that DOT2DOT correctly groups nodes for which good connection paths can be constructed, while separating distant nodes.
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M3 - Conference contribution
SN - 9781627487245
SP - 37
EP - 45
BT - SIAM International Conference on Data Mining 2013, SMD 2013
PB - Society for Industrial and Applied Mathematics Publications
ER -