57 citations · 218 across the 37 of their papers we have counts for
8 papers · 1 filter
Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks
Shuai Wang, David W. Zhang, Jia-Hong Huang +4
Hypergraphs serve as an effective model for depicting complex connections in various real-world scenarios, from social to biological networks. The development of Hypergraph Neural…
Modeling Edge-Specific Node Features through Co-Representation Neural Hypergraph Diffusion
Yijia Zheng, Marcel Worring
Hypergraphs are widely being employed to represent complex higher-order relations in real-world applications. Most existing research on hypergraph learning focuses on node-level or…
Prototype-Enhanced Hypergraph Learning for Heterogeneous Information Networks
Shuai Wang, Jiayi Shen, Athanasios Efthymiou +4
The variety and complexity of relations in multimedia data lead to Heterogeneous Information Networks (HINs). Capturing the semantics from such networks requires approaches capable…
Multimodal Temporal Fusion Transformers Are Good Product Demand Forecasters
Maarten Sukel, Stevan Rudinac, Marcel Worring
Multimodal demand forecasting aims at predicting product demand utilizing visual, textual, and contextual information. This paper proposes a method for multimodal product demand fo…
Adaptive Neural Message Passing for Inductive Learning on Hypergraphs
Devanshu Arya, Deepak K. Gupta, Stevan Rudinac +1
Graphs are the most ubiquitous data structures for representing relational datasets and performing inferences in them. They model, however, only pairwise relations between nodes an…
HyperSAGE: Generalizing Inductive Representation Learning on Hypergraphs
Devanshu Arya, Deepak K. Gupta, Stevan Rudinac +1
Graphs are the most ubiquitous form of structured data representation used in machine learning. They model, however, only pairwise relations between nodes and are not designed for…
HyperLearn: A Distributed Approach for Representation Learning in Datasets With Many Modalities
Devanshu Arya, Stevan Rudinac, Marcel Worring
Multimodal datasets contain an enormous amount of relational information, which grows exponentially with the introduction of new modalities. Learning representations in such a scen…
Multimodal Classification of Urban Micro-Events
Maarten Sukel, Stevan Rudinac, Marcel Worring
In this paper we seek methods to effectively detect urban micro-events. Urban micro-events are events which occur in cities, have limited geographical coverage and typically affect…