According to mathematical legend, Peter Sarnak and Noga Alon made a bet about optimal graphs in the late 1980s. They’ve now ...

This framework formulates the graph generation task as a probabilistic observation-inference process: using the self-learning adjacency matrix and time delayed self-attention (TDSA) methods to ...

The new method, Weighted Graph Anomalous Node Detection (WGAND), takes inspiration from social network analysis and is designed to identify proteins with significant roles in various human tissues.

To address these problems, we propose a self-weighted multi-view fuzzy clustering algorithm that incorporates multiple graph learning. Specifically, we automatically allocate weights corresponding to ...

Background: With the rapid advancement of gene sequencing technologies, Traditional weighted gene co-expression network ... based on the correlation between pairs of genes, using an adjacency matrix ...

Our results indicate that the strength by which nodes influence each other depends less on the local node model and more on the global network structure, permitting us to apply a wide range of graph .

of the graph. The self-attention mechanism assigns scores/weights to different node embeddings. The large weighted nodes are likely to contribute more towards the model prediction. Multi-class ...

Notably, the measure that best predicted a behavioural signature of implicit knowledge and blood oxygen level-dependent adaptation was a weighted ... The matrix exponential is small for nodes that are ...