Abstract
Directed graphs encode meaningful dependencies among objects ubiquitously. This paper introduces new and simple representations for labeled directed graphs with the properties of being succinct (space is information-theoretically optimal); in which we avoid exploiting a-priori knowledge on digraph regularity such as triangularity, separability, planarity, symmetry and sparsity. Our results have direct implications to model directed graphs by using single integer numbers effectively, which is significant to enable canonical (generation of graph instances is unique) and efficient (coding and decoding take polynomial time) encodings for learning and optimization algorithms. To the best of our knowledge, the proposed representations are the first known in the literature.
| Original language | English |
|---|---|
| Title of host publication | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 199-205 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781509030156 |
| DOIs | |
| Publication status | Published - 2017 Mar 17 |
| Event | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of Duration: 2017 Feb 13 → 2017 Feb 16 |
Other
| Other | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 |
|---|---|
| Country | Korea, Republic of |
| City | Jeju Island |
| Period | 17/2/13 → 17/2/16 |
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ASJC Scopus subject areas
- Information Systems
- Artificial Intelligence
- Computer Science Applications
- Computer Vision and Pattern Recognition
Cite this
On succinct representation of directed graphs. / Parque Tenorio, Victor; Miyashita, Tomoyuki.
2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. 199-205 7881738.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - On succinct representation of directed graphs
AU - Parque Tenorio, Victor
AU - Miyashita, Tomoyuki
PY - 2017/3/17
Y1 - 2017/3/17
N2 - Directed graphs encode meaningful dependencies among objects ubiquitously. This paper introduces new and simple representations for labeled directed graphs with the properties of being succinct (space is information-theoretically optimal); in which we avoid exploiting a-priori knowledge on digraph regularity such as triangularity, separability, planarity, symmetry and sparsity. Our results have direct implications to model directed graphs by using single integer numbers effectively, which is significant to enable canonical (generation of graph instances is unique) and efficient (coding and decoding take polynomial time) encodings for learning and optimization algorithms. To the best of our knowledge, the proposed representations are the first known in the literature.
AB - Directed graphs encode meaningful dependencies among objects ubiquitously. This paper introduces new and simple representations for labeled directed graphs with the properties of being succinct (space is information-theoretically optimal); in which we avoid exploiting a-priori knowledge on digraph regularity such as triangularity, separability, planarity, symmetry and sparsity. Our results have direct implications to model directed graphs by using single integer numbers effectively, which is significant to enable canonical (generation of graph instances is unique) and efficient (coding and decoding take polynomial time) encodings for learning and optimization algorithms. To the best of our knowledge, the proposed representations are the first known in the literature.
UR - http://www.scopus.com/inward/record.url?scp=85017591541&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85017591541&partnerID=8YFLogxK
U2 - 10.1109/BIGCOMP.2017.7881738
DO - 10.1109/BIGCOMP.2017.7881738
M3 - Conference contribution
AN - SCOPUS:85017591541
SP - 199
EP - 205
BT - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017
PB - Institute of Electrical and Electronics Engineers Inc.
ER -