Characterizing the Hypergraph-of-Entity Representation Model

• Type: Article in International Conference Proceedings Book
• Date: 2019

Authors

José Devezas (Author) Other; Sérgio Nunes (Author) FEUP

Conference proceedings International

Title: Complex Networks and Their Applications VIII - Volume 2 Proceedings of the Eighth International Conference on Complex Networks and Their Applications COMPLEX NETWORKS 2019, Lisbon, Portugal, December 10-12, 2019.

Pages: 3-14

8th International Conference on Complex Networks and their Applications, COMPLEX NETWORKS 2019

10 December 2019 through 12 December 2019

Indexing

Scopus - 1 Citation

Other information

Authenticus ID: P-00S-7FC

DOI: 10.1007/978-3-030-36683-4_1

Abstract (EN): The hypergraph-of-entity is a joint representation model for terms, entities and their relations, used as an indexing approach in entity-oriented search. In this work, we characterize the structure of the hypergraph, from a microscopic and macroscopic scale, as well as over time with an increasing number of documents. We use a random walk based approach to estimate shortest distances and node sampling to estimate clustering coefficients. We also propose the calculation of a general mixed hypergraph density based on the corresponding bipartite mixed graph. We analyze these statistics for the hypergraph-of-entity, finding that hyperedge-based node degrees are distributed as a power law, while node-based node degrees and hyperedge cardinalities are log-normally distributed. We also find that most statistics tend to converge after an initial period of accentuated growth in the number of documents. © 2020, Springer Nature Switzerland AG.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 12