Community detection algorithms in healthcare applications: a systematic review
Over the past few years, the number and volume of data sources in healthcare databases
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
has grown exponentially. Analyzing these voluminous medical data is both opportunity and …
Social influence analysis in social networking big data: Opportunities and challenges
S Peng, G Wang, D Xie - IEEE network, 2016 - ieeexplore.ieee.org
Social influence analysis has become one of the most important technologies in modern
information and service industries. It will definitely become an essential mechanism to …
information and service industries. It will definitely become an essential mechanism to …
A survey of heterogeneous information network analysis
Most real systems consist of a large number of interacting, multi-typed components, while
most contemporary researches model them as homogeneous information networks, without …
most contemporary researches model them as homogeneous information networks, without …
Fast federated machine unlearning with nonlinear functional theory
Federated machine unlearning (FMU) aims to remove the influence of a specified subset of
training data upon request from a trained federated learning model. Despite achieving …
training data upon request from a trained federated learning model. Despite achieving …
Effective and efficient community search over large heterogeneous information networks
Recently, the topic of community search (CS) has gained plenty of attention. Given a query
vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on …
vertex, CS looks for a dense subgraph that contains it. Existing studies mainly focus on …
Federated learning of large language models with parameter-efficient prompt tuning and adaptive optimization
Federated learning (FL) is a promising paradigm to enable collaborative model training with
decentralized data. However, the training process of Large Language Models (LLMs) …
decentralized data. However, the training process of Large Language Models (LLMs) …
Prompt certified machine unlearning with randomized gradient smoothing and quantization
The right to be forgotten calls for efficient machine unlearning techniques that make trained
machine learning models forget a cohort of data. The combination of training and unlearning …
machine learning models forget a cohort of data. The combination of training and unlearning …
Community detection in multiplex networks
A multiplex network models different modes of interaction among same-type entities. In this
article, we provide a taxonomy of community detection algorithms in multiplex networks. We …
article, we provide a taxonomy of community detection algorithms in multiplex networks. We …
[HTML][HTML] A new attributed graph clustering by using label propagation in complex networks
The diffusion method is one of the main methods of community detection in complex
networks. In this method, the use of the concept that diffusion within the nodes that are …
networks. In this method, the use of the concept that diffusion within the nodes that are …
Fedasmu: Efficient asynchronous federated learning with dynamic staleness-aware model update
As a promising approach to deal with distributed data, Federated Learning (FL) achieves
major advancements in recent years. FL enables collaborative model training by exploiting …
major advancements in recent years. FL enables collaborative model training by exploiting …