Academic Citation and Trend Forecaster: A Simple Graph Neural Network Approach for Venue Prediction and Collaboration Forecasting

A working end-to-end graph pipeline on a large real-world academic dataset — builds a heterogeneous graph (papers, authors, institutions) directly from OGBN-MAG’s raw files (736K+ papers, 5.4M+ citation edges), rather than a toy/small dataset.
A direct, controlled comparison of graph scale vs. architecture — full-graph GraphSAGE (mini-batch trained) vs. subgraph-sampled GAT, on the same task and dataset, showing that graph completeness (26.14% vs 9.24% accuracy) matters more than the choice of aggregation method (mean vs. attention). This is a useful, generalizable finding for anyone doing GNN work at scale.
A working link-prediction pipeline for collaboration forecasting — an encoder-decoder GraphSAGE model that scores author pairs, reaching 0.9998 ROC-AUC.
Methodological honesty as a contribution in itself — the paper explicitly flags that the near-perfect link-prediction AUC is inflated by random negative sampling and randomly-initialized author features, rather than presenting it uncritically. This kind of transparent limitation analysis is genuinely valuable and often missing from student/applied ML papers.