Can LLMs Predict Academic Collaboration? Topology Heuristics vs. LLM-Based Link Prediction on Real Co-authorship Networks

• URL: http://arxiv.org/abs/2604.01379v1
• Date: Wed, 01 Apr 2026 20:39:12 GMT
• Title: Can LLMs Predict Academic Collaboration? Topology Heuristics vs. LLM-Based Link Prediction on Real Co-authorship Networks
• Abstract summary: We study whether large language models (LLMs) can predict future scientific collaborations using only author profiles, without access to graph structure.<n>Using Qwen-722.5B-Instruct across three historical eras of AI research, we find that LLMs and topologys capture distinct signals and are strongest in complementary settings.
• Score: 4.675202949988315
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Can large language models (LLMs) predict which researchers will collaborate? We study this question through link prediction on real-world co-authorship networks from OpenAlex (9.96M authors, 108.7M edges), evaluating whether LLMs can predict future scientific collaborations using only author profiles, without access to graph structure. Using Qwen2.5-72B-Instruct across three historical eras of AI research, we find that LLMs and topology heuristics capture distinct signals and are strongest in complementary settings. On new-edge prediction under natural class imbalance, the LLM achieves AUROC 0.714--0.789, outperforming Common Neighbors, Jaccard, and Preferential Attachment, with recall up to 92.9\%; under balanced evaluation, the LLM outperforms \emph{all} topology heuristics in every era (AUROC 0.601--0.658 vs.\ best-heuristic 0.525--0.538); on continued edges, the LLM (0.687) is competitive with Adamic-Adar (0.684). Critically, 78.6--82.7\% of new collaborations occur between authors with no common neighbor -- a blind spot where all topology heuristics score zero but the LLM still achieves AUROC 0.652 by reasoning from author metadata alone. A temporal metadata ablation reveals that research concepts are the dominant signal (removing concepts drops AUROC by 0.047--0.084). Providing pre-computed graph features to the LLM \emph{degrades} performance due to anchoring effects, confirming that LLMs and topology methods should operate as separate, complementary channels. A socio-cultural ablation finds that name-inferred ethnicity and institutional country do not predict collaboration beyond topology, reflecting the demographic homogeneity of AI research. A node2vec baseline achieves AUROC comparable to Adamic-Adar, establishing that LLMs access a fundamentally different information channel -- author metadata -- rather than encoding the same structural signal differently.

Related papers

• PersonalBench: Measuring the Authorship Gap in LLM Personalization [0.0]
  We introduce PersonalBench, a benchmark that evaluates inference-time personalization methods.<n>Across 50 authors, 1,000 generations, and two model families, we find that personalization methods do produce author-differentiated output.<n>All methods achieve LUAR similarity to real authors in the range 0.484-0.508, below the cross-author human floor of 0.626.
  arXiv  Detail & Related papers  (2026-08-20T07:48:20Z)
• CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery [51.07538881798502]
  Large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference.<n>We propose CauTion, a framework that reliably integrates LLM domain knowledge into an ensemble of statistical causal discovery algorithms.<n>CauTion consistently outperforms both data-centric and LLM-augmented baselines.
  arXiv  Detail & Related papers  (2026-06-02T13:07:43Z)
• From Code to Prediction: Fine-Tuning LLMs for Neural Network Performance Classification in NNGPT [48.83701310501069]
  We present a classification task integrated into the NNGPT framework.<n>A fine-tuned LLM predicts which of two image classification datasets a given neural network architecture achieves higher accuracy on.<n>The results establish that LLMs can be fine-tuned to predict cross-dataset suitability from neural network code.
  arXiv  Detail & Related papers  (2026-05-05T12:30:19Z)
• Theory-Grounded Evaluation Exposes the Authorship Gap in LLM Personalization [0.0]
  We show that grounding evaluation in authorship verification theory transforms what benchmarks can measure.<n>We evaluate four inference-time personalization methods across 50 authors and 1,000 generations.
  arXiv  Detail & Related papers  (2026-04-29T09:17:01Z)
• HCRE: LLM-based Hierarchical Classification for Cross-Document Relation Extraction with a Prediction-then-Verification Strategy [54.91468501159335]
  Cross-document relation extraction (RE) aims to identify relations between the head tail entities located in different documents.<n>We propose an inference strategy that improves reliability through multi-view verification at each level.
  arXiv  Detail & Related papers  (2026-04-09T07:55:27Z)
• Evaluating Large Language Models for Gait Classification Using Text-Encoded Kinematic Waveforms [1.1635287453977645]
  Large Language Models (LLMs) may offer explanatory capabilities and confidence-aware outputs when applied to structured kinematic data.<n>This study evaluated whether general-purpose LLMs can classify continuous gait kinematics when represented as textual numeric sequences.
  arXiv  Detail & Related papers  (2026-03-04T10:23:35Z)
• Assessing LLM Reliability on Temporally Recent Open-Domain Questions [15.456770184839726]
  Large Language Models (LLMs) are increasingly deployed for open-domain question answering.<n>We investigate how four open-source LLMs respond to 15,000 recent Reddit questions.<n>All models achieve over 99% cosine similarity with references despite less than 8% BLEU-1 overlap.
  arXiv  Detail & Related papers  (2026-01-17T21:33:27Z)
• From Memorization to Creativity: LLM as a Designer of Novel Neural-Architectures [48.83701310501069]
  Large language models (LLMs) excel in program synthesis, yet their ability to autonomously navigate neural architecture design-balancing reliability, performance, and structural novelty--remains underexplored.<n>We address this by placing a code-oriented LLM within a closed-loop synthesis framework, analyzing its evolution over 22 supervised fine-tuning cycles.
  arXiv  Detail & Related papers  (2026-01-06T13:20:28Z)
• Realizing LLMs' Causal Potential Requires Science-Grounded, Novel Benchmarks [20.409472830397455]
  Recent claims of strong performance by Large Language Models (LLMs) on causal discovery are undermined by a key flaw: many evaluations rely on benchmarks likely included in pretraining corpora.<n>We challenge this narrative by asking: Do LLMs truly reason about causal structure, and how can we measure it without memorization concerns?<n>We argue that realizing LLMs' potential for causal analysis requires two shifts: (P.1) developing robust evaluation protocols based on recent scientific studies to guard against dataset leakage, and (P.2) designing hybrid methods that combine LLM-derived knowledge with data-driven statistics.
  arXiv  Detail & Related papers  (2025-10-18T14:58:04Z)
• Evaluating the Promise and Pitfalls of LLMs in Hiring Decisions [1.1883838320818292]
  Large language models (LLMs) in hiring promise to streamline candidate screening, but it also raises serious concerns regarding accuracy and algorithmic bias.<n>We benchmark several state-of-the-art foundational LLMs and compare them with our proprietary domain-specific hiring model (Match Score) for job candidate matching.<n>Our experiments show that Match Score outperforms the general-purpose LLMs on accuracy (ROC AUC 0.85 vs 0.77) and achieves significantly more equitable outcomes across demographic groups.
  arXiv  Detail & Related papers  (2025-07-02T19:02:18Z)
• SCAN: Structured Capability Assessment and Navigation for LLMs [54.54085382131134]
  textbfSCAN (Structured Capability Assessment and Navigation) is a practical framework that enables detailed characterization of Large Language Models.<n>SCAN incorporates four key components:.<n>TaxBuilder, which extracts capability-indicating tags from queries to construct a hierarchical taxonomy;.<n>RealMix, a query synthesis and filtering mechanism that ensures sufficient evaluation data for each capability tag;.<n>A PC$2$-based (Pre-Comparison-derived Criteria) LLM-as-a-Judge approach achieves significantly higher accuracy compared to classic LLM-as-a-Judge method
  arXiv  Detail & Related papers  (2025-05-10T16:52:40Z)
• Latent Factor Models Meets Instructions: Goal-conditioned Latent Factor Discovery without Task Supervision [50.45597801390757]
  Instruct-LF is a goal-oriented latent factor discovery system.<n>It integrates instruction-following ability with statistical models to handle noisy datasets.
  arXiv  Detail & Related papers  (2025-02-21T02:03:08Z)
• A Bayesian Approach to Harnessing the Power of LLMs in Authorship Attribution [57.309390098903]
  Authorship attribution aims to identify the origin or author of a document. Large Language Models (LLMs) with their deep reasoning capabilities and ability to maintain long-range textual associations offer a promising alternative. Our results on the IMDb and blog datasets show an impressive 85% accuracy in one-shot authorship classification across ten authors.
  arXiv  Detail & Related papers  (2024-10-29T04:14:23Z)
• PiCO: Peer Review in LLMs based on the Consistency Optimization [48.48819141999387]
  We use peer-review mechanisms to measure large language models (LLMs) automatically.<n>We formalize it as a constrained optimization problem, intending to maximize the consistency of each LLM's capabilities and scores.<n>We propose three metrics called PEN, CIN, and LIS to evaluate the gap in aligning human rankings.
  arXiv  Detail & Related papers  (2024-02-02T18:49:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.