18 citations · 46 across the 7 of their papers we have counts for
7 papers
Automorphic Equivalence-aware Graph Neural Network
Fengli Xu, Quanming Yao, Pan Hui +1
Distinguishing the automorphic equivalence of nodes in a graph plays an essential role in many scientific domains, e.g., computational biologist and social network analysis. Howeve…
An Upper Bound on the Convergence Time for Quantized Consensus of Arbitrary Static Graphs
Shang Shang, Paul Cuff, Pan Hui +1
We analyze a class of distributed quantized consensus algorithms for arbitrary static networks. In the initial setting, each node in the network has an integer value. Nodes exchang…
Privacy Preserving Recommendation System Based on Groups
Shang Shang, Yuk Hui, Pan Hui +2
Recommendation systems have received considerable attention in the recent decades. Yet with the development of information technology and social media, the risk in revealing privat…
An Upper Bound on the Convergence Time for Quantized Consensus
Shang Shang, Paul W. Cuff, Pan Hui +1
We analyze a class of distributed quantized consen- sus algorithms for arbitrary networks. In the initial setting, each node in the network has an integer value. Nodes exchange the…
A Random Walk Based Model Incorporating Social Information for Recommendations
Shang Shang, Sanjeev R. Kulkarni, Paul W. Cuff +1
Collaborative filtering (CF) is one of the most popular approaches to build a recommendation system. In this paper, we propose a hybrid collaborative filtering model based on a Mak…
Wisdom of the Crowd: Incorporating Social Influence in Recommendation Models
Shang Shang, Pan Hui, Sanjeev R. Kulkarni +1
Recommendation systems have received considerable attention recently. However, most research has been focused on improving the performance of collaborative filtering (CF) technique…
An Upper Bound on the Convergence Time for Distributed Binary Consensus
Shang Shang, Paul W. Cuff, Sanjeev R. Kulkarni +1
The problem addressed in this paper is the analysis of a distributed consensus algorithm for arbitrary networks, proposed by Bénézit et al.. In the initial setting, each node in th…