  Quantitatively profiling a scholar's scientific impact is important to modern
research society. Current practices with bibliometric indicators (e.g.,
h-index), lists, and networks perform well at scholar ranking, but do not
provide structured context for scholar-centric, analytical tasks such as
profile reasoning and understanding. This work presents GeneticFlow (GF), a
suite of novel graph-based scholar profiles that fulfill three essential
requirements: structured-context, scholar-centric, and evolution-rich. We
propose a framework to compute GF over large-scale academic data sources with
millions of scholars. The framework encompasses a new unsupervised
advisor-advisee detection algorithm, a well-engineered citation type classifier
using interpretable features, and a fine-tuned graph neural network (GNN)
model. Evaluations are conducted on the real-world task of scientific award
inference. Experiment outcomes show that the F1 score of best GF profile
significantly outperforms alternative methods of impact indicators and
bibliometric networks in all the 6 computer science fields considered.
Moreover, the core GF profiles, with 63.6%-66.5% nodes and 12.5%-29.9% edges of
the full profile, still significantly outrun existing methods in 5 out of 6
fields studied. Visualization of GF profiling result also reveals human
explainable patterns for high-impact scholars.
