Impact-Oriented Contextual Scholar Profiling using Self-Citation Graphs

TL;DR

This paper introduces GeneticFlow, a novel graph-based framework for detailed, evolving scholar profiles that outperform traditional impact metrics in identifying high-impact researchers across multiple computer science fields.

Contribution

The work presents a new framework with unsupervised detection, interpretable citation classification, and GNN modeling to create structured, impact-oriented scholar profiles from large-scale data.

Findings

GF profiles significantly outperform impact indicators in award inference tasks.

Core GF profiles retain most nodes and edges while maintaining high performance.

Visualization reveals human-understandable patterns for high-impact scholars.

Abstract

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.