Social Network Optimization Algorithm via Centrality and Influence-aware Learning SOCIAL is a structure-aware optimization project developed within the Applied Data Science and Artificial Intelligence environment at SRH University Heidelberg. It reframes optimization as networked intelligence: candidate solutions are modeled as agents in a small-world social graph, where centrality, influence diffusion, elite memory, and adaptive mutation… Continue reading

MOF-LENS is a bio-inspired optimisation framework developed in Applied Data Science and Artificial Intelligence at SRH University Heidelberg for accelerated discovery of metal-organic framework nanocarriers for doxorubicin delivery in cancer therapy. The method integrates structural descriptors, chemical fingerprints, and the Lotus Effect Algorithm to identify promising MOFs that balance drug loading, pH-responsive release, chemical affinity,… Continue reading

Gravity-Based Representative Sampling for Frugal Graph Learning on Metal-Organic Framework Networks The Black Hole Strategy is a structure-aware sampling project for large MOF similarity networks. It ranks nodes with a gravity score built from degree centrality, betweenness, and neighborhood influence, then keeps compact and representative subsets. In the paper, GNN models trained on Black Hole-sparsified… Continue reading