Graph unlearning has emerged as a pivotal method to delete information from an already trained graph neural network (GNN). One may delete nodes, a class of nodes, edges, or a class of edges. An unlearning method enables the GNN model to comply with data protection regulations (i.e., the right to be forgotten), adapt to evolving data distributions, and reduce the GPU-hours carbon footprint by avoiding repetitive retraining. Removing specific graph elements from graph data is challenging due to the inherent intricate relationships and neighborhood dependencies. Existing partitioning and aggregation-based methods have limitations due to their poor handling of local graph dependencies and additional overhead costs. Our work takes a novel approach to address these challenges in graph unlearning through knowledge distillation, as it distills to delete in GNN (D2DGN). It is an efficient model-agnostic distillation framework where the complete graph knowledge is divided and marked for retention and deletion. It performs distillation with response-based soft targets and feature-based node embedding while minimizing KL-divergence. The unlearned model effectively removes the influence of the deleted graph elements while preserving knowledge about the retained graph elements. D2DGN surpasses the performance of existing methods when evaluated on various real-world graph datasets by up to $\mathbf {43.1\%}$ (AUC) in edge and node unlearning tasks. Other notable advantages include better efficiency, better performance in removing target elements, preservation of performance for the retained elements, and zero overhead costs. Source code: https://github.com/MachineUnlearn/D2DGN.

User data spread across multiple modalities has popularized multi-modal recommender systems (MMRS). They recommend diverse content such as products, social media posts, TikTok reels, etc., based on a user-item interaction graph. With rising data privacy demands, recent methods propose unlearning private user data from uni-modal recommender systems (RS). However, methods for unlearning item data related to outdated user preferences, revoked licenses, and legally requested removals are still largely unexplored. Previous RS unlearning methods are unsuitable for MMRS due to the incompatibility of their matrix-based representation with the multi-modal user-item interaction graph. Moreover, their data partitioning step degrades performance on each shard due to poor data heterogeneity and requires costly performance aggregation across shards. This paper introduces MMRecUn, the first approach known to us for unlearning in MMRS and unlearning item data. Given a trained RS model, MMRecUn employs a novel Reverse Bayesian Personalized Ranking (BPR) objective to enable the model to forget marked data. The reverse BPR attenuates the impact of user-item interactions within the forget set, while the forward BPR reinforces the significance of user-item interactions within the retain set. Our experiments demonstrate that MMRecUn outperforms baseline methods across various unlearning requests when evaluated on benchmark MMRS datasets. MMRecUn achieves recall performance improvements of up to 49.85% compared to baseline methods and is up to 1.3× faster than the Gold model, which is trained on retain set from scratch. MMRecUn offers significant advantages, including superiority in removing target interactions, preserving retained interactions, and zero overhead costs compared to previous methods.

Most of the content sharing applications use the client/server model in which all of group managements are done by the server and this sometimes becomes a communication bottleneck. Installing specialized software for different purposes such as file sharing, video conferencing etc., becomes a barrier for the user. Recent technologies like NodeJs and Socket.io have fostered new ideas the ways web browsers can be used. Moreover, the emerging standards of WebRTC open up new paradigm of direct communication channel between web browsers without relaying the data through a web server. But there are certain issues such as lack of full-fledged threading/concurrency support in the JavaScript language, reliance on synchronous loading etc. that restricts modern day browsers to take full advantage of current multiprocessing capabilities. Although, on one hand there are advantages of using web browsers, such as no requirement of specialized software, benefits of emerging technologies etc., the aforementioned issues pose challenges in implementation in certain areas. In this paper, we have tried to couple the benefits of peer-to-peer (P2P) architecture (elimination of centralized dependency, better scalability, shareability etc.) along with the advantages of recent web technologies (NodeJs, WebRTC etc.) by designing and implementing a browser based P2P content sharing framework. We have addressed the aforementioned challenges of a browser based P2P architecture by providing a mechanism to exchange messages asynchronously and facilitating new peer joins via existing peers in the network, thus reducing the dependency on bootstrap server. Our prototypical implementation demonstrates the feasibility, efficiency and scalability of this lightweight framework, on the top of which a variety of applications can be added as a layer of functionality.