What is he best known for?
The fields of study he is best known for:
- Operating system
- Computer network
- Artificial intelligence
Minyi Guo focuses on Distributed computing, Computer network, Artificial intelligence, Wireless sensor network and Key distribution in wireless sensor networks.
His work carried out in the field of Distributed computing brings together such families of science as Ubiquitous computing, Wireless Routing Protocol, Overhead and Cloud computing.
Minyi Guo combines subjects such as Real-time computing and Software deployment with his study of Computer network.
His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Pattern recognition.
His Wireless sensor network research is multidisciplinary, incorporating perspectives in Routing, Distributed database, Vehicular ad hoc network, Scheme and Adaptive control.
The concepts of his Key distribution in wireless sensor networks study are interwoven with issues in Automatic repeat request, Secure communication and Cyber-physical system.
His most cited work include:
- DKN: Deep Knowledge-Aware Network for News Recommendation (227 citations)
- RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systems (210 citations)
- Supervised hashing with latent factor models (196 citations)
What are the main themes of his work throughout his whole career to date?
His primary scientific interests are in Distributed computing, Computer network, Parallel computing, Scheduling and Ubiquitous computing.
His studies deal with areas such as Quality of service, Scalability, Cloud computing and Server as well as Distributed computing.
His research is interdisciplinary, bridging the disciplines of Wireless network and Computer network.
Parallel computing and Computation are commonly linked in his work.
As a part of the same scientific family, Minyi Guo mostly works in the field of Wireless Routing Protocol, focusing on Link-state routing protocol and, on occasion, Optimized Link State Routing Protocol.
His research integrates issues of Sensor node and Key distribution in wireless sensor networks in his study of Wireless sensor network.
He most often published in these fields:
- Distributed computing (29.55%)
- Computer network (20.08%)
- Parallel computing (14.77%)
What were the highlights of his more recent work (between 2018-2021)?
- Distributed computing (29.55%)
- Artificial intelligence (8.71%)
- Cloud computing (5.87%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Distributed computing, Artificial intelligence, Cloud computing, Parallel computing and Quality of service.
His Distributed computing study incorporates themes from Cloud storage, Workload, Shared resource, Server and Erasure code.
His Artificial intelligence study deals with Machine learning intersecting with Feature extraction.
In the subject of general Cloud computing, his work in Microservices is often linked to Resource management, thereby combining diverse domains of study.
His studies in Parallel computing integrate themes in fields like Computation and Kernel.
His Computer network research includes themes of Enhanced Data Rates for GSM Evolution, Memory bandwidth and Shared memory.
Between 2018 and 2021, his most popular works were:
- Knowledge Graph Convolutional Networks for Recommender Systems (86 citations)
- Making Big Data Open in Edges: A Resource-Efficient Blockchain-Based Approach (85 citations)
- Multi-Task Feature Learning for Knowledge Graph Enhanced Recommendation (84 citations)
In his most recent research, the most cited papers focused on:
- Operating system
- Computer network
- Artificial intelligence
Artificial intelligence, Distributed computing, Recommender system, Parallel computing and Overhead are his primary areas of study.
His Artificial intelligence research is multidisciplinary, incorporating elements of Frame and Machine learning.
His study of Fault tolerance is a part of Distributed computing.
The subject of his Recommender system research is within the realm of Information retrieval.
His study in Parallel computing is interdisciplinary in nature, drawing from both Scheduling and Computation.
The various areas that Minyi Guo examines in his Collaborative filtering study include Embedding and Representation.
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