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

Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs

Yadati, N and Gao, T and Asoodeh, S and Talukdar, P and Louis, A (2021) Graph Neural Networks for Soft Semi-Supervised Learning on Hypergraphs. In: 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 11-14 May 2021, pp. 447-458.

Full text not available from this repository.
Official URL: https://doi.org/10.1007/978-3-030-75762-5_36


Graph-based semi-supervised learning (SSL) assigns labels to initially unlabelled vertices in a graph. Graph neural networks (GNNs), esp. graph convolutional networks (GCNs), are at the core of the current-state-of-the art models for graph-based SSL problems. GCNs have recently been extended to undirected hypergraphs in which relationships go beyond pairwise associations. There is a need to extend GCNs to directed hypergraphs which represent more expressively many real-world data sets such as co-authorship networks and recommendation networks. Furthermore, labels of interest in these applications are most naturally represented by probability distributions. Motivated by these needs, in this paper, we propose a novel GNN-based method for directed hypergraphs, called Directed Hypergraph Network (DHN) for semi-supervised learning of probability distributions (Soft SSL). A key contribution of this paper is to establish generalisation error bounds for GNN-based soft SSL. In fact, our theoretical analysis is quite general and has straightforward applicability to DHN as well as to existing hypergraph methods. We demonstrate the effectiveness of our method through detailed experimentation on real-world datasets. We have made the code available. © 2021, Springer Nature Switzerland AG.

Item Type: Conference Paper
Publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher: Springer Science and Business Media Deutschland GmbH
Additional Information: The copyright for this article belongs to Springer Science and Business Media Deutschland GmbH
Keywords: Convolutional neural networks; Data mining; Error analysis; Graphic methods; Probability distributions; Semi-supervised learning, Co-authorship networks; Convolutional networks; Directed hypergraphs; Generalisation; Graph neural networks; Real-world datasets; Semi-supervised learning (SSL); State of the art, Graph theory
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 28 Nov 2021 09:55
Last Modified: 28 Nov 2021 09:55
URI: http://eprints.iisc.ac.in/id/eprint/70005

Actions (login required)

View Item View Item