English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Among the many variants of graph neural network (GNN) architectures capableof modeling data with cross-instance relations, an important subclass involveslayers designed such that the forward pass iteratively reduces agraph-regularized energy function of interest. In this way, node embeddingsproduced at the output layer dually serve as both predictive features forsolving downstream tasks (e.g., node classification) and energy functionminimizers that inherit transparent, exploitable inductive biases andinterpretability. However, scaling GNN architectures constructed in this wayremains challenging, in part because the convergence of the forward pass mayinvolve models with considerable depth. To tackle this limitation, we propose asampling-based energy function and scalable GNN layers that iteratively reduceit, guided by convergence guarantees in certain settings. We also instantiate afull GNN architecture based on these designs, and the model achievescompetitive accuracy and scalability when applied to the largestpublicly-available node classification benchmark exceeding 1TB in size. Oursource code is available at https://github.com/haitian-jiang/MuseGNN.

MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy