27 citations · 59 across the 10 of their papers we have counts for
8 papers · 1 filter
Parameterized Fair Resource Allocation under Diversity Constraints
Keke Huang, Yik Yu Ng, Laks V. S. Lakshmanan +1
Resource allocation across multiple agent groups arises in many applications including e-commerce recommendation systems, housing assignment, and course allocation, and is commonly…
Scalable Continuous-time Diffusion Framework for Network Inference and Influence Estimation
Keke Huang, Ruize Gao, Bogdan Cautis +1
The study of continuous-time information diffusion has been an important area of research for many applications in recent years. When only the diffusion traces (cascades) are acces…
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…
Effective and Scalable Clustering on Massive Attributed Graphs
Renchi Yang, Jieming Shi, Yin Yang +3
Given a graph G where each node is associated with a set of attributes, and a parameter k specifying the number of output clusters, k-attributed graph clustering (k-AGC) groups nod…
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…
Refutations on "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study"
Wei Lu, Xiaokui Xiao, Amit Goyal +2
In a recent SIGMOD paper titled "Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study", Arora et al. [1] undertake a performance benchmarking study of seve…