Resource for "A Survey on Self-Supervised Graph Foundation Models: Knowledge-Based Perspective"
Layer5
A Python toolbox to compute topological metrics and statistics for Knowledge Graphs
Multigraph generation from a source graph.
Brain Graph Super-Resolution: how to generate high-resolution graphs from low-resolution graphs?
A software framework to prepare and perform a large-scale graph-based analysis on the graph topology of RDF datasets.
SM-NetFusion for supervised multi-topology network cross-diffusion.
Deterministic Document Integrity Engine for Markdown/MDX graphs.
MLKN.lab — Multi-Layered Knowledge Network Ideas Laboratory — MLKN.lab is a computational metascience initiative dedicated to mapping, modeling, and quantifying the epistemic topology of global scientific discovery.
Scripts for tumor microenvironment metabolic network analysis: random flux sampling of genome-scale models and multifractal geometric topology of metabolic graphs
Adaptive routing algorithm implementation and performance analysis on simulated networks – Distributed Systems and Networking course project – Computer Science @ FAMAF (UNC)
Research project to use graphs and graph transformation in software engineering
Conceptual path finding in high-dimensional embedding space via Grassmann manifold geometry. Replaces cosine similarity with tangent space distance — paths follow domain-coherent lanes instead of converging to vocabulary hubs. GPU-accelerated Julia compute worker
The Census-Stub invariant descriptor is a data structure that captures meaningful structural hallmarks of graph topology, especially when traditional visualizations like node-link views are insufficient. The Census-Stub approach provides a framework for analyzing complex networks, supporting tasks such as network comparison and classification.
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