IEEE

Reconstruction of Time-Varying Graph Signals via Sobolev Smoothness

Abstract: Graph Signal Processing (GSP) is an emerging research field that extends the concepts of digital signal processing to graphs. GSP has numerous applications in different areas such as sensor ...
IEEE

Fault-Tolerant Practical Prescribed-Time Formation-Containment Control of Multi-Agent Systems on Directed Graphs

Abstract: This work solves the analysis and protocol design problems of the prescribed-time formation-containment (FC) control of double-integrator multi-agent systems (MASs) on directed graphs in the ...
HackerNoon

The Race to Build AI’s Context Layer Is Really About Meaning

Context graphs, graph memory, and ontologies for AI are converging. What does this mean for enterprise AI in 2026?
Quanta Magazine

After 80 Years, Mathematicians Give Famed ‘Erdős Method’ an Upgrade

Decades ago, Paul Erdős used randomness to illuminate the vast and weird world of networks. Now mathematicians are making his ...
GitHub

Financial time series forecasting with multi-modality graph neural network

We recommend you to create a new python environment for MAGNN. You might run this command in your anaconda prompt in order to create a new environment: ...