通过语义网络发现交通研究中的虚拟合作者，揭示合作与主题的脱节。
Beyond coauthorship: semantic structure and phantom collaborators in transportation research, 1967--2025
- 构建作者级语义近邻图，用社区划分识别研究主题结构。
- 发现23个主题社区与172个合作者社区重合度低（互信息0.23）。
- 提出'虚拟合作者'概念，其后续实际合作率高出基准16-33倍。
我们基于1967至2025年间34本同行评审期刊的120,323篇论文，构建了交通研究的语义结构图谱，规模约为Sun和Rahwan（2017）合作者研究的十倍且时间跨度更长。利用OpenAlex和Crossref数据源，通过OpenAlex作者ID、ORCID记录及人工别名解析实现作者身份归一化，并采用SPECTER2结合阿罗拉风格去相关、概念TF-IDF及期刊线性判别投影对每篇论文进行嵌入。在此基础上，报告三项发现：第一，在作者级语义k近邻图上使用Leiden算法得到23个主题社区，与172个合作者社区仅弱相关（标准化互信息0.23），表明单一来源无法完全捕捉研究结构；第二，融合两种边类型的多层Leiden划分恢复出181个社区，定位了合作与主题结构解耦的区域；第三——本文核心方法论贡献——定义‘虚拟合作者’为语义上高度相似但在合作者图中相距≥3跳的作者对，通过时间留出测试（训练截止于2019年）显示，这些组合在2020–2025年间成为真实合作者的概率比随机、流行度加权及同期刊基线高出16至33倍，且高相似度组与低相似度组之间存在68倍的单调增长梯度。所有结果均以可复现的在线图谱形式发布于 https://choi-seongjin.github.io/transport-atlas/。
原文摘要 · Abstract (English)
We present a semantic-structural atlas of transportation research built from 120{,}323 papers across 34 peer-reviewed journals published between 1967 and 2025, roughly an order of magnitude larger than and a decade beyond Sun and Rahwan's~(2017) coauthorship study. We use OpenAlex and Crossref as open, CC0-licensed data sources, resolve author identity through OpenAlex author IDs, ORCID records, and manual alias resolution, and embed every paper with SPECTER2 with Arora-style whitening concatenated with concept TF--IDF and venue linear-discriminant projections. On this substrate we report three findings. First, Leiden on the author-level semantic k-nearest-neighbor graph yields 23 topic communities that agree only weakly with the 172 coauthor communities (normalized mutual information $0.23$), opening room for a predictive layer that neither source encodes alone. Second, a multiplex Leiden partition combining both edge types recovers 181 communities and localizes where collaboration and topic structure decouple. Third -- the paper's core methodological contribution -- we define \emph{phantom collaborators}, pairs of authors who are top-$K$ semantic neighbors yet $\geq 3$ hops apart in the coauthor graph, and show via a temporal hold-out (training cutoff 2019) that phantom pairs become real coauthors in 2020--2025 at a rate $16$ to $33$ times above random, popularity-weighted, and same-venue baselines, with a $68$-fold monotone gradient between the highest- and lowest-similarity buckets. All artifacts are released as a live, reproducible web atlas at https://choi-seongjin.github.io/transport-atlas/.
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