Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs
Autor: | Luo, Yuankai, Shi, Lei, Xu, Mufan, Ji, Yuwen, Xiao, Fengli, Hu, Chunming, Shan, Zhiguang |
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Rok vydání: | 2023 |
Předmět: | |
Druh dokumentu: | Working Paper |
DOI: | 10.1145/3580305.3599845 |
Popis: | 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. Comment: KDD 2023 |
Databáze: | arXiv |
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