1,395 research outputs found
Sciunits: Reusable Research Objects
Science is conducted collaboratively, often requiring knowledge sharing about
computational experiments. When experiments include only datasets, they can be
shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers
(DOIs). An experiment, however, seldom includes only datasets, but more often
includes software, its past execution, provenance, and associated
documentation. The Research Object has recently emerged as a comprehensive and
systematic method for aggregation and identification of diverse elements of
computational experiments. While a necessary method, mere aggregation is not
sufficient for the sharing of computational experiments. Other users must be
able to easily recompute on these shared research objects. In this paper, we
present the sciunit, a reusable research object in which aggregated content is
recomputable. We describe a Git-like client that efficiently creates, stores,
and repeats sciunits. We show through analysis that sciunits repeat
computational experiments with minimal storage and processing overhead.
Finally, we provide an overview of sharing and reproducible cyberinfrastructure
based on sciunits gaining adoption in the domain of geosciences
The Expressive Power of Graph Neural Networks: A Survey
Graph neural networks (GNNs) are effective machine learning models for many
graph-related applications. Despite their empirical success, many research
efforts focus on the theoretical limitations of GNNs, i.e., the GNNs expressive
power. Early works in this domain mainly focus on studying the graph
isomorphism recognition ability of GNNs, and recent works try to leverage the
properties such as subgraph counting and connectivity learning to characterize
the expressive power of GNNs, which are more practical and closer to
real-world. However, no survey papers and open-source repositories
comprehensively summarize and discuss models in this important direction. To
fill the gap, we conduct a first survey for models for enhancing expressive
power under different forms of definition. Concretely, the models are reviewed
based on three categories, i.e., Graph feature enhancement, Graph topology
enhancement, and GNNs architecture enhancement
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