抄録
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.
本文言語 | English |
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ホスト出版物のタイトル | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 |
出版社 | Institute of Electrical and Electronics Engineers Inc. |
ページ | 199-205 |
ページ数 | 7 |
ISBN(電子版) | 9781509030156 |
DOI | |
出版ステータス | Published - 2017 3月 17 |
イベント | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 - Jeju Island, Korea, Republic of 継続期間: 2017 2月 13 → 2017 2月 16 |
Other
Other | 2017 IEEE International Conference on Big Data and Smart Computing, BigComp 2017 |
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国/地域 | Korea, Republic of |
City | Jeju Island |
Period | 17/2/13 → 17/2/16 |
ASJC Scopus subject areas
- 情報システム
- 人工知能
- コンピュータ サイエンスの応用
- コンピュータ ビジョンおよびパターン認識