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.