Pratyush Gupta | Searcharxiv

arXiv2026

Trust-Aware Citation Cartel Ranking in Scholarly Knowledge Graphs

Citation-based systems usually treat each citation as an equal signal of scholarly influence, although citations can express very different relationships: direct method use, result comparison, broad background, or weak ceremonial acknowledgement. This distinction is crucial for citation-cartel analysis because dense internal citation alone is not suspicious; legitimate research communities are also densely connected. We present a trust-aware pipeline that combines citation graph structure with semantic citation intent to rank suspicious paper-level communities for audit. On a DBLP-derived graph with 500,000 papers and 4.87M citation edges, we use an LLM teacher to label 205,897 citation pairs, train a SciBERT student, and scale citation-intent typing to 2.04M unique graph edges. We then compute a Composite Cartel Index (CCI) that integrates internal density, citation inflation, reciprocity, semantic superficiality, degree assortativity, and trust-weighted PageRank shift. The highest-ranked community contains 1,079 papers and 8,603 internal citations, with 254.3x more internal citations than expected and 64.2% of them superficial. Comparisons against density-only, inflation-only, semantic-only, and random baselines show that CCI cannot be reduced to a single heuristic. Edge excision validation further shows that CCI-selected communities behave differently from matched random removals. The result is a reproducible, curator-facing ranking framework for prioritising communities that warrant closer inspection.

cs.SI↗

arXiv2024

Movie Revenue Prediction using Machine Learning Models

In the contemporary film industry, accurately predicting a movie's earnings is paramount for maximizing profitability. This project aims to develop a machine learning model for predicting movie earnings based on input features like the movie name, the MPAA rating of the movie, the genre of the movie, the year of release of the movie, the IMDb Rating, the votes by the watchers, the director, the writer and the leading cast, the country of production of the movie, the budget of the movie, the production company and the runtime of the movie. Through a structured methodology involving data collection, preprocessing, analysis, model selection, evaluation, and improvement, a robust predictive model is constructed. Linear Regression, Decision Trees, Random Forest Regression, Bagging, XGBoosting and Gradient Boosting have been trained and tested. Model improvement strategies include hyperparameter tuning and cross-validation. The resulting model offers promising accuracy and generalization, facilitating informed decision-making in the film industry to maximize profits.

cs.LG↗