SGR turns adversarial shortcuts into teachers, isolating invariant subgraph rationales and delivering the first XGL framework ...
Graph labeling is a central topic in combinatorial optimisation that involves assigning numerical or categorical labels to vertices or edges of a graph subject to specific constraints. This framework ...
Graph partitioning and bisection problems occupy a central position in combinatorial optimisation and theoretical computer science. These issues involve dividing a graph’s vertex set into distinct ...
No-code Graph RAG employs autonomous agents to integrate enterprise data and domain knowledge with LLMs for context-rich, explainable conversations Graphwise, a leading Graph AI provider, announced ...
The analysis of cancer biology data involves extremely heterogeneous data sets, including information from RNA sequencing, genome-wide copy number, DNA methylation data reporting on epigenetic ...
Adapting to the Stream: An Instance-Attention GNN Method for Irregular Multivariate Time Series Data
DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...
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