Search Results for author: Changhe Yuan

Found 7 papers, 1 papers with code

Geometric Matrix Completion via Sylvester Multi-Graph Neural Network

no code implementations19 Jun 2022 Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong

Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.

Association Graph Mining +1

Hypergraph Pre-training with Graph Neural Networks

no code implementations23 May 2021 Boxin Du, Changhe Yuan, Robert Barton, Tal Neiman, Hanghang Tong

Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.

hyperedge classification Representation Learning +1

Graph Neural Networks for Inconsistent Cluster Detection in Incremental Entity Resolution

no code implementations12 May 2021 Robert A. Barton, Tal Neiman, Changhe Yuan

In this case, the problem becomes a classification task on weighted graphs and represents an interesting application area for modern tools such as Graph Neural Networks (GNNs).

Entity Resolution Graph Classification +1

Most Relevant Explanation in Bayesian Networks

no code implementations16 Jan 2014 Changhe Yuan, Heejin Lim, Tsai-Ching Lu

In this paper, we introduce a method called Most Relevant Explanation (MRE) which finds a partial instantiation of the target variables that maximizes the generalized Bayes factor (GBF) as the best explanation for the given evidence.

Solving Limited-Memory Influence Diagrams Using Branch-and-Bound Search

no code implementations26 Sep 2013 Arindam Khaled, Eric A. Hansen, Changhe Yuan

A limited-memory influence diagram (LIMID) generalizes a traditional influence diagram by relaxing the assumptions of regularity and no-forgetting, allowing a wider range of decision problems to be modeled.

Evaluating Anytime Algorithms for Learning Optimal Bayesian Networks

no code implementations26 Sep 2013 Brandon Malone, Changhe Yuan

Exact algorithms for learning Bayesian networks guarantee to find provably optimal networks.

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