Effects of Vertex Merging & Splitting on Large Coauthorship Networks: A Counterfactual Analysis

• URL: http://arxiv.org/abs/2605.31555v1
• Date: Fri, 29 May 2026 17:19:53 GMT
• Title: Effects of Vertex Merging & Splitting on Large Coauthorship Networks: A Counterfactual Analysis
• Abstract summary: This study investigates how different levels of vertices merging and splitting errors are induced by name ambiguity impact network measures.<n>Initial-based disambiguation generates coauthorship networks with specific network properties underestimated.<n>In contrast, other network metric values increase, making authors appear more collaborative and embedded within less fragmented research communities.
• Score: 2.351268385561458
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Researchers analyze coauthorship networks, but author name ambiguity in their network data remains a significant challenge as it can change the number of vertices, distorting network properties. Although many scholars use straightforward heuristics for author name disambiguation using author's forename initials, these techniques can skew our understanding of network properties by merging or splitting vertices, raising concerns about the reliability and validity of these methods. This study investigates how different levels of vertex merging and splitting errors that are induced by name ambiguity impact network measures, using three large coauthorship networks with highly accurate algorithmic author name disambiguation. As a counterfactual scenario, two initial-based disambiguation methods widely used in coauthorship network research were applied to these datasets. Nine coauthorship network metrics were computed while varying randomly the numbers of merged or split vertices. Results show that initial-based disambiguation generates coauthorship networks with specific network properties underestimated, leading to the discovery of coauthorship networks that are smaller and more closely connected than they genuinely are. In contrast, other network metric values increase, making authors appear more collaborative and embedded within less fragmented research communities than they are. The study emphasizes the importance of careful disambiguation of vertex names in analyzing coauthorship networks for rigorous and valid findings.

Related papers

• BotTrans: A Multi-Source Graph Domain Adaptation Approach for Social Bot Detection [55.31623652907614]
  We propose a multi-source graph domain adaptation model named textitBotTrans for detecting social bots.<n>We first leverage the labeling knowledge shared across multiple source networks to establish a cross-source-domain topology.<n>We then aggregate cross-domain neighbor information to enhance the discriminability of source node embeddings.
  arXiv  Detail & Related papers  (2025-06-12T02:10:36Z)
• Probing the statistical properties of enriched co-occurrence networks [0.0]
  This study investigates two key statistical properties of text-based network models.<n>We show that incorporating virtual edges can have positive and negative effects, depending on the specific network metric.<n>Our results can serve as a guideline for determining which network metrics are most appropriate for specific applications.
  arXiv  Detail & Related papers  (2024-12-03T18:38:14Z)
• Exploring Structural Dynamics in Retracted and Non-Retracted Author's Collaboration Networks: A Quantitative Analysis [0.5497663232622965]
  Retractions undermine the reliability of scientific literature and the foundation of future research. This study compared the network structures of retracted and non-retracted papers. Retracted networks showed hierarchical and centralized structures, while non-retracted networks exhibited distributed collaboration with stronger clustering and connectivity.
  arXiv  Detail & Related papers  (2024-11-26T14:04:36Z)
• Sifting out communities in large sparse networks [2.666294200266662]
  We introduce an intuitive objective function for quantifying the quality of clustering results in large sparse networks. We utilize a two-step method for identifying communities which is especially well-suited for this domain. We identify complex genetic interactions in large-scale networks comprised of tens of thousands of nodes.
  arXiv  Detail & Related papers  (2024-05-01T18:57:41Z)
• T-GAE: Transferable Graph Autoencoder for Network Alignment [79.89704126746204]
  T-GAE is a graph autoencoder framework that leverages transferability and stability of GNNs to achieve efficient network alignment without retraining. Our experiments demonstrate that T-GAE outperforms the state-of-the-art optimization method and the best GNN approach by up to 38.7% and 50.8%, respectively.
  arXiv  Detail & Related papers  (2023-10-05T02:58:29Z)
• TeKo: Text-Rich Graph Neural Networks with External Knowledge [75.91477450060808]
  We propose a novel text-rich graph neural network with external knowledge (TeKo) We first present a flexible heterogeneous semantic network that incorporates high-quality entities. We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
  arXiv  Detail & Related papers  (2022-06-15T02:33:10Z)
• Identifying Influential Nodes in Two-mode Data Networks using Formal Concept Analysis [0.0]
  Bi-face (BF) is a new bipartite centrality measurement for identifying important nodes in two-mode networks. Unlike off-the shelf centrality indices, it quantifies how a node has a cohesive-substructure influence on its neighbour nodes via bicliques. Our experiments on several real-world and synthetic networks show the efficiency of BF over existing prominent bipartite centrality measures.
  arXiv  Detail & Related papers  (2021-09-07T23:57:05Z)
• Multi-Source Domain Adaptation for Object Detection [52.87890831055648]
  We propose a unified Faster R-CNN based framework, termed Divide-and-Merge Spindle Network (DMSN) DMSN can simultaneously enhance domain innative and preserve discriminative power. We develop a novel pseudo learning algorithm to approximate optimal parameters of pseudo target subset.
  arXiv  Detail & Related papers  (2021-06-30T03:17:20Z)
• Unveiling Anomalous Edges and Nominal Connectivity of Attributed Networks [53.56901624204265]
  The present work deals with uncovering anomalous edges in attributed graphs using two distinct formulations with complementary strengths. The first relies on decomposing the graph data matrix into low rank plus sparse components to improve markedly performance. The second broadens the scope of the first by performing robust recovery of the unperturbed graph, which enhances the anomaly identification performance.
  arXiv  Detail & Related papers  (2021-04-17T20:00:40Z)
• Anomaly Detection on Attributed Networks via Contrastive Self-Supervised Learning [50.24174211654775]
  We present a novel contrastive self-supervised learning framework for anomaly detection on attributed networks. Our framework fully exploits the local information from network data by sampling a novel type of contrastive instance pair. A graph neural network-based contrastive learning model is proposed to learn informative embedding from high-dimensional attributes and local structure.
  arXiv  Detail & Related papers  (2021-02-27T03:17:20Z)
• Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks [81.00481125272098]
  We introduce Multi-view Attention-based Pairwise Recurrent Neural Network (MA-PairRNN) to solve the name disambiguation problem. MA-PairRNN combines heterogeneous graph embedding learning and pairwise similarity learning into a framework. Results on two real-world datasets demonstrate that our framework has a significant and consistent improvement of performance on the name disambiguation task.
  arXiv  Detail & Related papers  (2020-08-30T06:08:20Z)

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.