On succinct representation of directed graphs

Victor Parque Tenorio; Tomoyuki Miyashita

doi:10.1109/BIGCOMP.2017.7881738

On succinct representation of directed graphs

Victor Parque Tenorio, Tomoyuki Miyashita

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

14 Citations (Scopus)

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	https://doi.org/10.1109/BIGCOMP.2017.7881738
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

ASJC Scopus subject areas

Information Systems
Artificial Intelligence
Computer Science Applications
Computer Vision and Pattern Recognition

Access to Document

10.1109/BIGCOMP.2017.7881738

Fingerprint Dive into the research topics of 'On succinct representation of directed graphs'. Together they form a unique fingerprint.

Cite this

Parque Tenorio, V & Miyashita, T 2017, On succinct representation of directed graphs. in 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017., 7881738, Institute of Electrical and Electronics Engineers Inc., pp. 199-205, 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017, Jeju Island, Korea, Republic of, 17/2/13. https://doi.org/10.1109/BIGCOMP.2017.7881738

@inproceedings{df106dabb40641df904689964b2793c3,

title = "On succinct representation of directed graphs",

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.",

author = "{Parque Tenorio}, Victor and Tomoyuki Miyashita",

year = "2017",

month = mar,

day = "17",

doi = "10.1109/BIGCOMP.2017.7881738",

language = "English",

pages = "199--205",

booktitle = "2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 ; Conference date: 13-02-2017 Through 16-02-2017",

}

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.

T2 - 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017

Y2 - 13 February 2017 through 16 February 2017

ER -