ePrints@IIScePrints@IISc Home | About | Browse | Latest Additions | Advanced Search | Contact | Help

C3MM: Clique-closure based hyperlink prediction

Sharma, G and Patil, P and Narasimha Murty, M (2020) C3MM: Clique-closure based hyperlink prediction. In: IJCAI International Joint Conference on Artificial Intelligence, 1 January 2021, Yokohama, pp. 3364-3370.

[img] PDF
IJCAI_2020.pdf - Published Version
Restricted to Registered users only

Download (197kB) | Request a copy
Official URL: https://doi.org/10.24963/ijcai.2020/465


Usual networks lossily (if not incorrectly) represent higher-order relations, which calls for complex structures such as hypergraphs to be used instead. Akin to the link prediction problem in graphs, we deal with hyperlink (higher-order link) prediction in hypergraphs. With a handful of solutions in the literature that seem to have merely scratched the surface, we provide improvements for the same. Motivated by observations in recent literature, we first formulate a “clique-closure” hypothesis (viz., hyperlinks are more likely to be formed from near-cliques rather than from non-cliques), test it on real hypergraphs, and then exploit it for our very problem. In the process, we generalize hyperlink prediction on two fronts: (1) from small-sized to arbitrary-sized hyperlinks, and (2) from a couple of domains to a handful. We perform experiments (both the hypothesis-test as well as the hyperlink prediction) on multiple real datasets, report results, and provide both quantitative and qualitative arguments favouring better performances w.r.t. the state-of-the-art.

Item Type: Conference Paper
Publication: IJCAI International Joint Conference on Artificial Intelligence
Publisher: International Joint Conferences on Artificial Intelligence
Additional Information: The copyright for this article belongs to International Joint Conferences on Artificial Intelligence.
Keywords: Artificial intelligence; Forecasting; Graph theory, Complex structure; Higher-order; Hyper graph; Hyperlinks; Hypothesis tests; Link prediction; Real data sets; State of the art, Hypertext systems
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Date Deposited: 07 Feb 2023 09:35
Last Modified: 07 Feb 2023 09:35
URI: https://eprints.iisc.ac.in/id/eprint/80001

Actions (login required)

View Item View Item