HONEM: Learning Embedding for Higher Order Networks
Document Type
Article
Publication Date
2020
Keywords
higher order network, network embedding, network representation learning
Digital Object Identifier (DOI)
https://doi.org/10.1089/big.2019.0169
Abstract
Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network. Thus, the embeddings that are generated may not accurately represent the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this study presents higher order network embedding (HONEM), a higher order network (HON) embedding method that captures the non-Markovian higher order dependencies in a network. HONEM is specifically designed for the HON structure and outperforms other state-of-the-art methods in node classification, network reconstruction, link prediction, and visualization for networks that contain non-Markovian higher order dependencies.
Was this content written or created while at USF?
Yes
Citation / Publisher Attribution
Big Data, v. 8, issue 4, p. 255-269
Scholar Commons Citation
Saebi, Mandana; Ciampaglia, Giovanni Luca; Kaplan, Lance M.; and Chawla, Nitesh V., "HONEM: Learning Embedding for Higher Order Networks" (2020). Computer Science and Engineering Faculty Publications. 151.
https://digitalcommons.usf.edu/esb_facpub/151
