Review of David Fraser, Nazi Antisemitism and Jewish Legal Self-Defense: The Turn to Law in Liberal Democracies, 1932–39 (Abingdon, Oxon: Routledge 2024)

© 2025 Taylor & Francis. This is an author produced version of a paper published in Comparative Legal History uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

FDI Spillovers, Innovation and the Role of Industrial Clusters: Evidence from Innovative Indian Manufacturing Firms

© 2025, Elsevier B.V. The attached document (embargoed until 08/09/2026) is an author produced version of a paper published in Economic Modelling uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Factors related to Internet Gaming Disorder: Comparing reports of adolescents and their parents in Türkiye and the United Kingdom.

©2025, Taylor & Francis. The attached document (embargoed until 18/08/2026) is an author produced version of a paper published in European Journal of Developmental Psychology uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Should I Stay or Should I Go? The Effect of Allied Brands Negative Publicity on Brand Managers' Decision-Making

© 2023, Elsevier. The attached document (embargoed until 13/07/2026) is an author produced version of a paper published in Industrial Marketing Management uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

‘Alberico Gentili’s Ghost’::Review of Claire Vergerio’s War, States, and International Order: Joseph Fletcher Prize Forum, Cambridge Review of International Affairs

©2025, Taylor & Francis. This is an author produced version of a paper published in Cambridge Review of International Affairs uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Multiple GRAphs-oriented Random wAlk (MulGRA2) for Social Link Prediction

Current link prediction methods in Location-Based Social Networks (LBSNs) fuse graphs derived from users’ check-in data and their social links to form a single graph or network. Then, they learn node representations and link probabilities from a fused graph that undermines the distinctive characteristics of each user’s spatiotemporal mobility and social links. Consequently, the input datasets are heavily contaminated with noise, which makes it challenging for these algorithms to make accurate predictions. Our study use the proposed Multiple GRAphs-oriented Random wAlk (MulGRA2) to model graphs while maintaining the distinctive characteristics of the data to address the issue of noisy data in the learning process. Specifically, we use three graphs: a social graph constructed from social links data, a user co-occurrence graph derived from users’ check-in data to capture spatiotemporal co-occurrence, and a user-location bipartite graph that links users to specific locations based on the same check-in data. After traversing all three graphs, it learns node representation and infers links effectively. Extensive experiments on both Foursquare and synthetic datasets demonstrate that our algorithm significantly improves.© 2024, Elsevier. The attached document (embargoed until 06/04/2026) is an author produced version of a paper published in Information Sciences uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Personalized Eicosapentaenoic Acid Therapy for Clinical Depression

© 2025, [Slack]. The attached document (embargoed until 26/02/2026) is an author produced version of a paper published in Psychiatric Annals uploaded in accordance with the publisher’s self-archiving policy. The final published version (version of record) is available online at the link [https://doi.org/10.3928/00485713-20250114-03]. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it

Heterogeneous Graph Neural Networks for Fraud Detection and Explanation in Supply Chain Finance

It is a critical mission for financial service providers to discover fraudulent borrowers in a supply chain. The borrowers’ transactions in anongoing business are inspected to support the providers’ decision on whether to lend the money. Considering multiple participants in a supply chain business, the borrowers may use sophisticated tricks to cheat, making fraud detection challenging. In this work, we propose a multitask learning framework, MultiFraud, for complex fraud detection with reasonable explanation. The heterogeneous information from multi-view around the entities is leveraged in the detection framework based on heterogeneous graph neural networks. MultiFraud enables multiple domains to share embeddings and enhance modeling capabilities for fraud detection. The developed explainer provides comprehensive explanations across multiple graphs. Experimental results on five datasets demonstrate the framework’s effectiveness in fraud detection and explanation across domains.© 2023, Elsevier. This is an author produced version of a paper published in Information Systems uploaded in accordance with the publisher’s self- archiving policy. The final published version (version of record) is available online at the link. Some minor differences between this version and the final published version may remain. We suggest you refer to the final published version should you wish to cite from it