hypergraph

In simplifying a hypergraph, we allow vertices to be combined if they belong to almost the same set of hyperedges, and hyperedges to be merged if they share ...

www.sci.utah.edu·33.3 MB

In mathematics, a hypergraph is a generalization of a graph in which a pair H= (X, E) where X is a set of elements called nodes or vertices, and. E is a set of ...

117.211.166.25·14.0 MB

by L Zhang · 2022 · Cited by 30 — The application of hypergraphs to network analysis tasks is focusing on how to map each node in a network to a low-dimensional and distributed ...

pdfs.semanticscholar.org·5.7 MB

Hypergraphs naturally represent group interactions, which are omnipresent in many domains: collaborations of re- searchers, co-purchases of items, ...

www.vldb.org·2.7 MB

Hypergraphs are systems of finite sets and form, probably, the most general concept in discrete mathematics. This branch of mathematics has developed very.

www.math.ucdavis.edu·2.1 MB

Higher-order interactions are commonly modeled as a hypergraph. • A hypergraph consists of a node set and a hyperedge set.

dmlab.kaist.ac.kr·1.7 MB

Abstract: Graphs are very specialized structures for modelling and representing data objects and their relationships in real-world applications.

www.scitepress.org·1.4 MB

ABSTRACT. Hypergraph Neural Network (HyperGNN) is an emerging type of Graph Neural Networks (GNNs) that can utilize hyperedges to model high-order ...

proceedings.mlsys.org·1.4 MB

by ZB Yilma · 2011 · Cited by 2 — A central problem in extremal (hyper)graph theory is that of finding the maximum number of edges in a (hyper)graph that does not contain a specified forbidden ...

www.math.cmu.edu·1.1 MB

Hypergraphs are natural and expressive modeling tools to encode high-order relationships among en- tities. Several variations of Hypergraph Neural. Networks ( ...

www.ijcai.org·1014.4 KB

Hypergraphs mathematically express higher-order networks or networks of HOIs [11], where nodes and hyperedges respec- tively represent entities ...

arxiv.org·983.0 KB

We employ these sheaf hypergraph Laplacians to design two categories of models: Sheaf Hypergraph Neural Networks and Sheaf Hyper- graph Convolutional Networks.

proceedings.neurips.cc·894.8 KB

Vision Graph Neural Networks (ViGs) offer an alternative by leveraging graph-based methodologies but are hindered by the computational bottlenecks of clustering ...

openaccess.thecvf.com·747.5 KB

Hypergraph neural networks, which rely on message-passing between nodes over hyperedges to learn latent representations, have emerged as the ...

arxiv.org·738.3 KB

(2) By efficiently learning complex information from directed hypergraphs to ob- tain high-quality representations, we develop a framework DHGNN for link ...

proceedings.mlr.press·707.9 KB

Hypergraphs can be thought of as networks of polyadic relationships and have many applications in social sciences, biology, computer science, and engineering ...

web.engr.oregonstate.edu·700.3 KB

In a directed hypergraph a hyperarc is defined by a pair (X.i) where X is an arbitrary nonempty set of nodes and i is a node. Directed hypergraphs have a wide ...

mice.cs.columbia.edu·659.8 KB

In this article we introduce the notion of heterogeneous hypergraphs from an algebraic point of view, which have traditional directed hypergraphs as a ...

arxiv.org·619.1 KB

Hypergraphs provide an effective abstraction for modeling multi-way group interactions among nodes, where each hyperedge can connect any number of nodes.

www.ijcai.org·539.4 KB

Abstract. Hypergraphs require higher-dimensional representations, which makes it more difficult to compute and interpret their spectral ...

arxiv.org·483.9 KB