IEEE

NAGphormer+: A Tokenized Graph Transformer With Neighborhood Augmentation for Node Classification in Large Graphs

Abstract: Graph Transformers, emerging as a new architecture for graph representation learning, suffer from the quadratic complexity and can only handle graphs with at most thousands of nodes. To this ...
marktechpost

KGGen: Advancing Knowledge Graph Extraction with Language Models and Clustering Techniques

Knowledge graphs (KGs) are the foundation of artificial intelligence applications but are incomplete and sparse, affecting their effectiveness. Well-established KGs such as DBpedia and Wikidata lack ...
IEEE

Targeted Discrepancy Attacks: Crafting Selective Adversarial Examples in Graph Neural Networks

Abstract: In this study, we present a novel approach to adversarial attacks for graph neural networks (GNNs), specifically addressing the unique challenges posed by graphical data. Unlike traditional ...
C&EN

Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.jctc.4c00948. RMSEs of the free energy differences between end states ...
CounterPunch

JD Vance is a Classic Example of “Poornography”

JD Vance has climbed to his current position as former President Donald Trump’s running mate, in part, by selling himself as a hillbilly, calling on his Appalachian background to bolster his ...
The Conversation

JD Vance is no pauper − he’s a classic example of ‘poornography,’ in which the rich try to speak on behalf of the poor

Lennard J. Davis does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond ...
Salon

“Poornography”: Expert says JD Vance a classic example of the rich trying to speak for the poor

JD Vance has climbed to his current position as former President Donald Trump’s running mate, in part, by selling himself as a hillbilly, calling on his Appalachian background to bolster his ...
Forbes

What Companies Should Know About The Shift To Graph Databases

As enterprises continue to navigate the complexities of digital transformation, connected data is becoming an increasingly common necessity. Connected data is when data assets are linked together to ...
C&EN

DPMGCDA: Deciphering circRNA–Drug Sensitivity Associations with Dual Perspective Learning and Path-Masked Graph Autoencoder

Accumulating evidence has indicated that the expression of circular RNAs (circRNAs) can affect the cellular sensitivity to drugs and significantly influence drug efficacy. However, traditional ...
marktechpost

Integrating Large Language Models with Graph Machine Learning: A Comprehensive Review

Graphs are important in representing complex relationships in various domains like social networks, knowledge graphs, and molecular discovery. Alongside topological structure, nodes often possess ...
BioPharma Dive

Using knowledge graphs to drive drug discovery

Have you ever done a Google search to find a restaurant or look up what your favorite actor is up to? Most of us have, and therefore understand the benefit of knowledge graphs, possibly without even ...