which provides information about the graph's structure. Laplacian Matrix: A matrix representation of a graph that captures its connectivity, defined as the difference between the degree matrix and ...

The researchers validate SySTeC’s effectiveness through extensive performance evaluations on common tensor operations, including symmetric sparse matrix-vector multiplication (SSYMV), ...

2017). Both of these two methods learn node representation by aggregating node neighbor features, but there are also some limitations. Specifically, GCN relies on the calculation of the adjacency ...

This study proposes a method that combines the Wasserstein non-negative matrix factorization (W-NMF) with line graphs to obtain low-dimensional representations of multi-layered graphs. A line graph is ...

Our goal is to build a high-performance Knowledge Graph tailored for Large Language Models (LLMs), prioritizing exceptionally low latency to ensure fast and efficient information delivery through our ...

This process is detailed in our recent publication, QirK: Question Answering via Intermediate Representation on Knowledge Graphs, which outlines a framework for combining LLM capabilities with the ...

The interplay between graph analytics and large language models (LLMs) represents a promising frontier for advancing ...

Laboratory for Research on the Structure of Matter (LRSM), University of Pennsylvania, Philadelphia, United States ...