What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Machine learning
Wen-Chih Peng mostly deals with Data mining, Mobile computing, Distributed computing, Scalability and Computer network.
Wen-Chih Peng has included themes like Representation, Key and Cluster analysis in his Data mining study.
In his study, Reduction and Replication is strongly linked to Wireless, which falls under the umbrella field of Mobile computing.
His Distributed computing research incorporates themes from Key distribution in wireless sensor networks, Wireless sensor network and Mobile database.
His biological study spans a wide range of topics, including Maximization, Social network, Artificial intelligence, Machine learning and Community structure.
His Inference and Collective intelligence study in the realm of Artificial intelligence connects with subjects such as Collaborative learning and Graph.
His most cited work include:
- Constructing popular routes from uncertain trajectories (250 citations)
- Efficient in-network moving object tracking in wireless sensor networks (214 citations)
- Particle Swarm Optimization With Recombination and Dynamic Linkage Discovery (153 citations)
What are the main themes of his work throughout his whole career to date?
Wen-Chih Peng spends much of his time researching Data mining, Mobile computing, Cluster analysis, Artificial intelligence and Distributed computing.
His Data mining research integrates issues from Scalability and Global Positioning System.
His Scalability study combines topics in areas such as Maximization and Social network.
The concepts of his Mobile computing study are interwoven with issues in Server, Atomic broadcast and Mobile search.
Wen-Chih Peng has researched Artificial intelligence in several fields, including Machine learning and Pattern recognition.
His research investigates the connection with Distributed computing and areas like Wireless sensor network which intersect with concerns in Wireless network.
He most often published in these fields:
- Data mining (48.03%)
- Mobile computing (21.71%)
- Cluster analysis (17.11%)
What were the highlights of his more recent work (between 2015-2020)?
- Data mining (48.03%)
- Social network (8.55%)
- Set (5.92%)
In recent papers he was focusing on the following fields of study:
Wen-Chih Peng mainly investigates Data mining, Social network, Set, Machine learning and Artificial intelligence.
In his papers, he integrates diverse fields, such as Data mining and Interval.
His studies deal with areas such as Skyline, Reliability and Data science as well as Social network.
He combines subjects such as Iterative method, Scalability, Relation and Warning system with his study of Machine learning.
His study in the field of Semi-supervised learning is also linked to topics like Health care, Health examination, Real-time bidding and Graph.
His Maximization research is multidisciplinary, relying on both Key and Mobility model.
Between 2015 and 2020, his most popular works were:
- Exploiting Centrality Information with Graph Convolutions for Network Representation Learning (24 citations)
- Efficient Keyword-Aware Representative Travel Route Recommendation (20 citations)
- Exploiting Viral Marketing for Location Promotion in Location-Based Social Networks (15 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Machine learning
His scientific interests lie mostly in Data mining, Graph, Social network, Machine learning and Artificial intelligence.
Wen-Chih Peng interconnects Representation and Maximization in the investigation of issues within Data mining.
His Graph study spans across into areas like PageRank, Centrality, Theoretical computer science, Closeness and Betweenness centrality.
His research integrates issues of Key and Mobility model in his study of Social network.
His studies in Machine learning integrate themes in fields like Iterative method and Warning system.
His research in Artificial intelligence intersects with topics in Scalability and Relation.
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