Interlocking Authorship Networks

• Interlocking authorship is the formation of collaborative networks via shared coauthorship ties that foster common research agendas and intellectual exchange.
• Network analysis methods such as dual-mode construction, centrality measures, and Fruchterman-Reingold visualizations are used to identify cohesive subgroups and interdisciplinary bridges.
• The structure of these authorship networks impacts research practices by boosting citation counts, guiding innovation diffusion, and raising ethical challenges in credit attribution.

Interlocking authorship describes the phenomenon where individual researchers participate as coauthors on multiple academic papers, thereby forming interconnected collaborative networks among scholars and research outputs. Analogous to "interlocking editorship" in journal governance, interlocking authorship reflects both the social and intellectual interdependence of research communities through shared authorship ties, influencing scientific practices, knowledge dissemination, and innovation propagation.

1. Theoretical Foundations and Analogies

The concept draws its theoretical basis from network analysis approaches used to characterize overlapping memberships in formal academic structures. In "Interlocking editorship. A network analysis of the links between economic journals" (Baccini et al., 2011), the authors hypothesize that editors exert power over editorial policy, and that overlap in editorial board membership connects journals via shared perspectives and policy orientations. Translating this to authorship, the fundamental postulate is that scholars connected via coauthorship cultivate shared research agendas, methodological standards, and theoretical inclinations, producing observable clusters in the academic landscape.

A pertinent analog is the paper of "interlocking directorates" in corporate networks (Saavedra et al., 2014). Just as overlapping corporate directorships correlate with amplified information transmission and synchronized market actions, interlocking authorship networks facilitate the diffusion of research ideas, collective paradigms, and reputational capital among distinct yet connected academic groups. This structure-driven influence is made explicit by measures such as degree, centrality, and path-based proximity in collaboration networks.

2. Network Analysis Methodologies

The analysis of interlocking authorship relies on the formal construction and interrogation of coauthorship networks, where nodes correspond to authors (or alternatively, publications, institutions, or departments) and edges represent shared paper authorship. Adapting principles from editorship networks (Baccini et al., 2011), the workflow typically includes:

• Dual-mode (affiliation) network construction, followed by projection into a one-mode network among authors or publications.
• Degree and normalized degree: capturing direct collaborative reach; $$\text{normalized degree} = \frac{\text{degree}}{N-1}$$, where $N$ is network size.
• Closeness centrality: $$C(v) = \frac{\text{number of accessible nodes}}{\sum_w d(v,w)}$$, where $d(v,w)$ is the shortest path length between $v$ and $w$.
• Betweenness centrality: $$B(v) = \sum_{s \neq v \neq t} \frac{\sigma_{st}(v)}{\sigma_{st}}$$, where $\sigma_{st}$ is the count of shortest paths from node $s$ to $t$ and $\sigma_{st}(v)$ those passing through $v$.
• Weighted networks and thresholding (e.g. m-slices): identifying cohesive subgroups that embody thematic or methodological orientation.

Software such as Pajek and layout algorithms including Fruchterman-Reingold are applied for graphical representation, facilitating the visualization of central clusters and peripheral subnetworks according to these metrics.

3. Structural Components of Authorship Networks

Empirical studies repeatedly show the emergence of "giant components," wherein the majority of authors or journals are linked via multistep coauthorship or editorial ties (Baccini et al., 2011). In economic journal networks, 90% of journals form a large, connected component with thematic subgroups, each reflective of dense collaboration within macroeconomics, econometrics, urban studies, and related specialties.

A plausible implication is that interlocking authorship networks will similarly demonstrate giant components characterized by disciplinary, institutional, or methodological sub-clusters. For example, application of the m-slice technique (e.g., $m=6$, selecting only edges with six or more shared memberships) isolates highly cohesive groups potentially corresponding to "schools of thought" or leading collaborative teams. Smaller weak components may exist for niche fields or peripheral contributors.

4. Influence on Research Direction, Policy, and Impact

Interlocking authorship not only signals collaborative reach but can drive the direction and integration of research. The overlap of key individuals—whether editors or authors—acts as a conduit for shared standards and intellectual exchange. In interlocking editorship (Baccini et al., 2011), overlapping editors promote convergence in journal policy and interdisciplinary exchange. By analogy, overlapping authors catalyze the spread of particular theoretical or methodological approaches, amplify collective reputation, and may steer citation flows and institutional research agendas (Saavedra et al., 2014).

Central authors in these networks can influence innovation diffusion and the consolidation of emerging scientific trends. Conversely, tightly coupled collaboration networks may also propagate entrenched paradigms, potentially limiting intellectual diversification.

Network position further correlates with tangible research impact. Citation analyses show that collaboratively authored papers—especially those involving multiple institutions or departments—yield higher mean citation counts than solo efforts, exemplifying the positive association of interlocking authorship with scholarly visibility and recognition (Aulck et al., 2018).

5. Identification and Visualization of Collaborative Clusters

Advancements in network analytics and visualization have enabled granular mapping of interlocking authorship patterns. Tools and procedures developed for editorship networks—including Fruchterman-Reingold layouts—extend naturally to coauthorship data, revealing:

• Central "stars" or hubs: dominated by major authors or leading research collectives.
• Peripheral clusters: corresponding to specialized themes, emerging fields, or less connected contributors.
• Bridges: individuals whose collaborative ties span multiple clusters, acting as agents of interdisciplinary cross-pollination.

Such representations facilitate the identification of collaboration bottlenecks, intellectual silos, and potential areas for integrative research policy.

6. Policy, Ethics, and Evaluation Implications

Interlocking authorship raises substantive questions regarding credit attribution, evaluation practices, and ethical standards. The conflation of collaborative reach with genuine scientific contribution poses challenges for the use of bibliometric indicators (e.g., publication count, $h$-index), which may be artificially inflated by honorary, paid, or cartel authorship practices (Khan et al., 3 Apr 2025). The paper on curbing authorship abuse advocates the use of credit allocation schemes—such as Fibonacci reciprocal-based weighting—to mitigate metric inflation and emphasize substantive contribution over mere participation: $P' = \sum_{k=1}^P \frac{1}{F_{R(k)}}$ where $P'$ is adjusted publication credit and $F_{R(k)}$ is the Fibonacci number for author rank $R(k)$. The associated $T'$-index,

$T' = \frac{P'}{P}$

quantifies the extent of leading vs. supporting roles, offering a correction for interlocking abuse.

Research on integrating authorship and acknowledgements shows that expanded measures of collaboration—including credited but non-author contributors—provide a more accurate portrait of research effort and can challenge disciplinary stereotypes about collaboration patterns (Paul-Hus et al., 2016). Such metrics have implications for funding allocation, contributor recognition, and the evolution of team science models.

7. Future Directions and Research Opportunities

There remains a need for further investigation into the interplay between network centrality, research output quality, and innovation propagation in interlocking authorship networks. Proposed avenues include:

• Extending network proximity and centrality modeling from editorship and directorship studies to academic author networks (Saavedra et al., 2014).
• Exploring cross-domain dynamics: how collaborative and interlocking structures facilitate transfer between academia and industry.
• Rigorous empirical testing of whether centrality in coauthorship networks predicts research impact, field shifts, or reputation changes.
• Development of refined credit and authorship attribution standards to safeguard ethical evaluation and fair reward distribution.

Such research can inform the long-term evolution of credit systems, collaborative infrastructure, and science policy frameworks in an increasingly interconnected scholarly environment.