X
Xiangjie Kong
Researcher at Zhejiang University of Technology
Publications - 161
Citations - 6003
Xiangjie Kong is an academic researcher from Zhejiang University of Technology. The author has contributed to research in topics: Computer science & The Internet. The author has an hindex of 37, co-authored 152 publications receiving 3929 citations. Previous affiliations of Xiangjie Kong include Dalian University of Technology & Zhejiang University.
Papers
More filters
Posted Content
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
TL;DR: A novel model based on Network Representation Learning (NRL), namely Shifu2, is proposed, which takes the collaboration network as input and the identified advisor-advisee relationship as output and considers not only the network structure but also the semantic information of nodes and edges.
Journal ArticleDOI
How does collaboration affect researchers’ positions in co-authorship networks?
TL;DR: This paper evaluates three aspects of the researchers’ influence: friendship paradox validation, social circle, and structure of a researcher's ego network and shows that collaboration can help researchers increase their influence to some extent.
Journal ArticleDOI
BeeCup: A bio-inspired energy-efficient clustering protocol for mobile learning
TL;DR: This work proposes a new clustering protocol, namely BeeCup, to save the energy of mobile devices while guaranteeing the quality of learning, based on the artificial bee colony (ABC) algorithm.
Journal ArticleDOI
Social-Oriented Resource Management in Cloud-Based Mobile Networks
TL;DR: It is found that software-defined networking technology is promising for resource management in cloud-based networks, allowing different clients to access the network effectively and consideration of users' social associations can improve link connectivity and service delivery.
Journal ArticleDOI
Exploring time factors in measuring the scientific impact of scholars
TL;DR: The experimental results demonstrate that the proposed Time-aware Ranking algorithm (TRank) outperforms other methods in selecting outstanding scholars and the evaluation results on the overall impact of scholars.