In a manner of keyword search, a software developer could develop mashup applications with more sophisticated functions by choosing a set of Web application programming interfaces (APIs) from an extensive pool of available options, which can surprisingly save the development costs to ensure up-to-date mashup development. However, when faced with a large and high variety of Web APIs, developers often encounter several challenges, such as functional incompatibility and limited diversity. Moreover, although the number of Web APIs is enormous, available interaction datasets are extremely sparse, which may increase the risk of development failures. Recently, contrastive learning (CL) performs well in dealing with data sparsity problems by comparing the original and augmented representations learned from a bipartite graph. Thus, we propose a CL-based diversity-aware web APIs recommendation (C-DAWAR) approach to recommend diversified and compatible Web APIs for mashup creation. Specifically, C-DAWAR first constructs a Web APIs correlation graph to identify the minimal group Steiner trees within the constructed graph. It then learns node representation using contrastive learning in a self-supervised manner. In particular, after the graph convolution operation in the contrastive learning pipeline, C-DAWAR applies the self-attention mechanism to better capture global features. Finally, determinantal point processes (DPP) is employed to enhance the diversity of the recommended results. Comprehensive experimental results on widely used real-world datasets demonstrate the effectiveness of C-DAWAR.

In the digital era, social media platforms play a crucial role in forming user communities, yet the challenge of protecting user privacy remains paramount. This paper proposes a novel framework for identifying and analyzing user communities within social media networks, emphasizing privacy protection. In detail, we implement a social media-driven user community finding approach with hashing named MCF to ensure that the extracted information cannot be traced back to specific users, thereby maintaining confidentiality. Finally, we design a set of experiments to verify the effectiveness and efficiency of our proposed MCF approach by comparing it with other existing approaches, demonstrating its effectiveness in community detection while upholding stringent privacy standards. This research contributes to the growing field of social network analysis by providing a balanced solution that respects user privacy while uncovering valuable insights into community dynamics on social media platforms.