112 peer-reviewed papers on Graph Neural Networks and Graph Machine Learning across Architecture, Engineering & Construction — interlinked through 8 named lineages, 9 connection modes, and 1,558 typed edges (lineage / category / domain / methodology / infrastructure / temporal / NetworkX cluster / architecture / framework).
Four ways to navigate the corpus — pick the one that fits your question.
D3.js force-directed graph of every paper across 9 connection modes — citation lineage, category, domain, methodology, infrastructure, temporal, NetworkX cluster, and two deep-PDF detection modes.
Open the graph →Markdown wiki with 112 paper pages, 7 category pages, 9 domain pages, and 8 lineage families — every paper is interlinked with its bibliometric neighbours and infrastructure dependencies.
Browse the wiki →8 hand-curated paper families (Floorplan Generation, Topologic Infrastructure, Energy Digital Twin, BIM Classification, …) cross-validated against Connected Papers .bib exports.
See lineages →Heuristic catalogue match across all 112 PDFs (80 pages each) detecting 23 GNN architectures, 33 frameworks, and 35 benchmarks — surfaced with a yellow "(detected from PDF)" provenance tag.
About the methods →Six static configurations rendered from the underlying graph data — click any image to enlarge, or open the interactive version →
Latest additions to the corpus. Browse all 112 →