Results 11 to 20 of about 47,538 (182)
Journal of Combinatorial Theory, Series B, 2021 The following very natural problem was raised by Chung and Erd s in the early 80's and has since been repeated a number of times. What is the minimum of the Tur n number $\text{ex}(n,\mathcal{H})$ among all $r$-graphs $\mathcal{H}$ with a fixed number of edges?
Matija Bucić +3 more
openaire +4 more sources
Self-Supervised Hypergraph Transformer for Recommender Systems [PDF]
Knowledge Discovery and Data Mining, 2022 Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing ...
Lianghao Xia, Chao Huang, Chuxu Zhang
semanticscholar +1 more source
Hypergraph Neural Networks [PDF]
AAAI Conference on Artificial Intelligence, 2018 In this paper, we present a hypergraph neural networks (HGNN) framework for data representation learning, which can encode high-order data correlation in a hypergraph structure.
Yifan Feng +4 more
semanticscholar +1 more source
Sheaf Hypergraph Networks [PDF]
Neural Information Processing Systems, 2023 Higher-order relations are widespread in nature, with numerous phenomena involving complex interactions that extend beyond simple pairwise connections.
Iulia Duta +3 more
semanticscholar +1 more source
A Survey on Hypergraph Representation Learning
ACM Computing Surveys, 2023 Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts.
Alessia Antelmi +5 more
semanticscholar +1 more source
CHGNN: A Semi-Supervised Contrastive Hypergraph Learning Network [PDF]
IEEE Transactions on Knowledge and Data Engineering, 2023 Hypergraphs can model higher-order relationships among data objects that are found in applications such as social networks and bioinformatics. However, recent studies on hypergraph learning that extend graph convolutional networks to hypergraphs cannot ...
Yumeng Song +6 more
semanticscholar +1 more source
From Hypergraph Energy Functions to Hypergraph Neural Networks [PDF]
International Conference on Machine Learning, 2023 Hypergraphs are a powerful abstraction for representing higher-order interactions between entities of interest. To exploit these relationships in making downstream predictions, a variety of hypergraph neural network architectures have recently been ...
Yuxin Wang +4 more
semanticscholar +1 more source
Spatio-Temporal Hypergraph Learning for Next POI Recommendation
Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 2023 Next Point-of-Interest (POI) recommendation task focuses on predicting the immediate next position a user would visit, thus providing appealing location advice.
Xiaodong Yan +6 more
semanticscholar +1 more source
Double-Scale Self-Supervised Hypergraph Learning for Group Recommendation [PDF]
International Conference on Information and Knowledge Management, 2021 With the prevalence of social media, there has recently been a proliferation of recommenders that shift their focus from individual modeling to group recommendation. Since the group preference is a mixture of various predilections from group members, the
Junwei Zhang +5 more
semanticscholar +1 more source
Trends in Biotechnology, 2014 Signaling pathways function as the information-passing mechanisms of cells. A number of databases with extensive manual curation represent the current knowledge base for signaling pathways. These databases motivate the development of computational approaches for prediction and analysis.
Anna, Ritz +4 more
openaire +2 more sources