OFFER Referees Suggester for the Journal Editors Assigning appropriate referees to a journal or conference paper is a vital task for many reasons, including enhancing the journal venue quality and reliance, fair judgement of the papers, and among many others. While assigning the referees to the papers, the editors of a journal venue need to find suitable referees who are both related to field of the given paper and have no conflict of interest with the authors of the paper. Editorialwise this referee assignment process is implemented in a handcrafted manner, i.e., the editor needs to find the most suitable referees to the paper via a search engine and manually refines the all unrelated and having conflict of interest authors to the paper authors. Clearly, such a manual referee searching process is tedious and time consuming for the editors.In this paper, we present an alternate automated approach for assigning referees problem using intrinsic random walk with restart proximity measure. In our experiments based on a comprehensive DBLP networks, we show that our approach, called OFFER, significantly outperforms stateoftheart the random walk with restart based method.

AMENDER: An Attentive and Aggregate Multilayered Network for Dataset Recommendation In this paper, we study the problem of recommending the appropriate datasets for authors, which is implemented to infer the proximity between authors and datasets by leveraging the information from a threelayered network, composed by authors, papers and datasets. To link authordataset semantically by taking advantage of the rich content information of papers in the intermediate layer, we design an attentive and aggregate multilayer network learning model. The aggregation is for integrating the intralayer information of paper content and citations, while the attention is used for coordinating authors at the toplayer and datasets at the bottomlayer in the semantic space learned from papers in the intermediate layer. The experimental study demonstrates the superiority of our method compared with the solutions that extend existing models to our problem.