57 citations · 198 across the 19 of their papers we have counts for
20 papers
Data-dependent Evaluations for Budgeted Submodular Maximization
Lejian Zhang, Xueyan Tang, Jing Tang
Submodular maximization is an important building block for developing algorithms in many areas such as machine learning and data mining. Due to the NP-hardness of the problem, anal…
Optimal Streaming Algorithms for Multi-Armed Bandits
Tianyuan Jin, Keke Huang, Jing Tang +1
This paper studies two variants of the best arm identification (BAI) problem under the streaming model, where we have a stream of arms with reward distributions supported on $[…
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…
Practical Parallel Algorithms for Non-Monotone Submodular Maximization
Shuang Cui, Kai Han, Jing Tang +3
Submodular maximization has found extensive applications in various domains within the field of artificial intelligence, including but not limited to machine learning, computer vis…
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 Spanning Centrality
Shiqi Zhang, Renchi Yang, Jing Tang +2
Given a graph , the spanning centrality (SC) of an edge measures the importance of for to be connected. In practice, SC has seen extensive applic…
How Fragile is Relation Extraction under Entity Replacements?
Yiwei Wang, Bryan Hooi, Fei Wang +6
Relation extraction (RE) aims to extract the relations between entity names from the textual context. In principle, textual context determines the ground-truth relation and the RE…
Cost-Effective Algorithms for Average-Case Interactive Graph Search
Qianhao Cong, Jing Tang, Yuming Huang +2
Interactive graph search (IGS) uses human intelligence to locate the target node in hierarchy, which can be applied for image classification, product categorization and searching a…
Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer
Yining Ma, Jingwen Li, Zhiguang Cao +4
Recently, Transformer has become a prevailing deep architecture for solving vehicle routing problems (VRPs). However, it is less effective in learning improvement models for VRP be…
Efficient and Effective Algorithms for Revenue Maximization in Social Advertising
Kai Han, Benwei Wu, Jing Tang +3
We consider the revenue maximization problem in social advertising, where a social network platform owner needs to select seed users for a group of advertisers, each with a payment…
The Power of Randomization: Efficient and Effective Algorithms for Constrained Submodular Maximization
Kai Han, Shuang Cui, Tianshuai Zhu +3
Submodular optimization has numerous applications such as crowdsourcing and viral marketing. In this paper, we study the fundamental problem of non-negative submodular function max…
Do the Rich Get Richer? Fairness Analysis for Blockchain Incentives
Yuming Huang, Jing Tang, Qianhao Cong +2
Proof-of-Work (PoW) is the most widely adopted incentive model in current blockchain systems, which unfortunately is energy inefficient. Proof-of-Stake (PoS) is then proposed to ta…
Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene
Xinke Li, Chongshou Li, Zekun Tong +5
Learning on 3D scene-based point cloud has received extensive attention as its promising application in many fields, and well-annotated and multisource datasets can catalyze the de…
Revisiting Modified Greedy Algorithm for Monotone Submodular Maximization with a Knapsack Constraint
Jing Tang, Xueyan Tang, Andrew Lim +3
Monotone submodular maximization with a knapsack constraint is NP-hard. Various approximation algorithms have been devised to address this optimization problem. In this paper, we r…
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…
An Analysis of Blockchain Consistency in Asynchronous Networks: Deriving a Neat Bound
Jun Zhao, Jing Tang, Li Zengxiang +3
Formal analyses of blockchain protocols have received much attention recently. Consistency results of Nakamoto's blockchain protocol are often expressed in a quantity , which de…
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…