Abstract
Point-to-point distance estimation in large scale graphs is a fundamental and well studied problem with applications in many areas such as Social Search. Previous work has focused on selecting an appropriate subset of vertices as landmarks, aiming to derive distance upper or lower bounds that are as tight as possible. In order to compute a distance bound between two vertices, the proposed methods apply triangle inequalities on top of the precomputed distances between each of these vertices and the landmarks, and then use the tightest one. In this work we take a fresh look at this setting and approach it as a learning problem. As features, we use structural attributes of the vertices involved as well as the bounds described above, and we learn a function that predicts the distance between a source and a destination vertex. We conduct an extensive experimental evaluation on a variety of real-world graphs and show that the average relative prediction error of our proposed methods significantly outperforms state-of-the-art landmark-based estimates. Our method is particularily efficient when the available space is very limited.
| Original language | English (US) |
|---|---|
| Title of host publication | Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 335-344 |
| Number of pages | 10 |
| ISBN (Print) | 9781479956654 |
| DOIs | |
| State | Published - Jan 7 2015 |
| Event | 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 - Washington, United States Duration: Oct 27 2014 → Oct 30 2014 |
Other
| Other | 2nd IEEE International Conference on Big Data, IEEE Big Data 2014 |
|---|---|
| Country | United States |
| City | Washington |
| Period | 10/27/14 → 10/30/14 |
ASJC Scopus subject areas
- Artificial Intelligence
- Information Systems
Cite this
Estimating pairwise distances in large graphs. / Christoforaki, Maria; Suel, Torsten.
Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014. Institute of Electrical and Electronics Engineers Inc., 2015. p. 335-344 7004250.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Estimating pairwise distances in large graphs
AU - Christoforaki, Maria
AU - Suel, Torsten
PY - 2015/1/7
Y1 - 2015/1/7
N2 - Point-to-point distance estimation in large scale graphs is a fundamental and well studied problem with applications in many areas such as Social Search. Previous work has focused on selecting an appropriate subset of vertices as landmarks, aiming to derive distance upper or lower bounds that are as tight as possible. In order to compute a distance bound between two vertices, the proposed methods apply triangle inequalities on top of the precomputed distances between each of these vertices and the landmarks, and then use the tightest one. In this work we take a fresh look at this setting and approach it as a learning problem. As features, we use structural attributes of the vertices involved as well as the bounds described above, and we learn a function that predicts the distance between a source and a destination vertex. We conduct an extensive experimental evaluation on a variety of real-world graphs and show that the average relative prediction error of our proposed methods significantly outperforms state-of-the-art landmark-based estimates. Our method is particularily efficient when the available space is very limited.
AB - Point-to-point distance estimation in large scale graphs is a fundamental and well studied problem with applications in many areas such as Social Search. Previous work has focused on selecting an appropriate subset of vertices as landmarks, aiming to derive distance upper or lower bounds that are as tight as possible. In order to compute a distance bound between two vertices, the proposed methods apply triangle inequalities on top of the precomputed distances between each of these vertices and the landmarks, and then use the tightest one. In this work we take a fresh look at this setting and approach it as a learning problem. As features, we use structural attributes of the vertices involved as well as the bounds described above, and we learn a function that predicts the distance between a source and a destination vertex. We conduct an extensive experimental evaluation on a variety of real-world graphs and show that the average relative prediction error of our proposed methods significantly outperforms state-of-the-art landmark-based estimates. Our method is particularily efficient when the available space is very limited.
UR - http://www.scopus.com/inward/record.url?scp=84921778513&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84921778513&partnerID=8YFLogxK
U2 - 10.1109/BigData.2014.7004250
DO - 10.1109/BigData.2014.7004250
M3 - Conference contribution
SN - 9781479956654
SP - 335
EP - 344
BT - Proceedings - 2014 IEEE International Conference on Big Data, IEEE Big Data 2014
PB - Institute of Electrical and Electronics Engineers Inc.
ER -