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.

本研究提出了GeneticFlow（GF），一组新颖的基于图的学者概况，通过包括一个新的非监督型指导者-顾问检测算法、一个良好设计的可解释特征的引文类型分类器以及一个经过微调的图神经网络（GNN）模型，计算大规模学术数据源上的GF，实现了“结构化上下文”、“以学者为中心”和“演化丰富”三个基本要求，在真实世界的科学奖推断任务方面进行了评估。实验结果表明，GF概况最好的F1得分在所有考虑的6个计算机科学领域中显着优于影响因子和文献计量网络的替代方法。 此外，63.6％-66.5％节点和12.5％-29.9％边的核心GF概况仍显著优于现有方法中的5个研究领域。 

利用自引图进行面向影响的上下文学者画像