57 citations · 198 across the 19 of their papers we have counts for
7 papers · 1 filter
Efficient Estimation of Pairwise Effective Resistance
Renchi Yang, Jing Tang
Given an undirected graph G, the effective resistance r(s,t) measures the dissimilarity of node pair s,t in G, which finds numerous applications in real-world problems, such as rec…
Node-wise Diffusion for Scalable Graph Learning
Keke Huang, Jing Tang, Juncheng Liu +2
Graph Neural Networks (GNNs) have shown superior performance for semi-supervised learning of numerous web applications, such as classification on web services and pages, analysis o…
Efficient Approximation Algorithms for Adaptive Influence Maximization
Keke Huang, Jing Tang, Kai Han +5
Given a social network and an integer , the influence maximization (IM) problem asks for a seed set of nodes from to maximize the expected number of nodes influe…
Efficient Approximation Algorithms for Adaptive Target Profit Maximization
Keke Huang, Jing Tang, Xiaokui Xiao +2
Given a social network , the profit maximization (PM) problem asks for a set of seed nodes to maximize the profit, i.e., revenue of influence spread less the cost of seed select…
Efficient Approximation Algorithms for Adaptive Seed Minimization
Jing Tang, Keke Huang, Xiaokui Xiao +4
As a dual problem of influence maximization, the seed minimization problem asks for the minimum number of seed nodes to influence a required number of users in a given social n…
Towards Profit Maximization for Online Social Network Providers
Jing Tang, Xueyan Tang, Junsong Yuan
Online Social Networks (OSNs) attract billions of users to share information and communicate where viral marketing has emerged as a new way to promote the sales of products. An OSN…
Influence Maximization Meets Efficiency and Effectiveness: A Hop-Based Approach
Jing Tang, Xueyan Tang, Junsong Yuan
Influence Maximization is an extensively-studied problem that targets at selecting a set of initial seed nodes in the Online Social Networks (OSNs) to spread the influence as widel…