• Publications
  • Influence
LightGBM: A Highly Efficient Gradient Boosting Decision Tree
TLDR
We propose two novel techniques: Gradient-based One-Side Sampling (GOSS) and Exclusive Feature Bundling (EFB). Expand
Efficient influence maximization in social networks
TLDR
We improve the original greedy algorithm of [5] and its improvement [7] to further reduce its running time, and propose new degree discount heuristics that improves influence spread. Expand
Scalable influence maximization for prevalent viral marketing in large-scale social networks
TLDR
In this paper, we design a new heuristic algorithm that is scalable to millions of nodes and edges in our experiments. Expand
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
  • Wei Chen, Y. Yuan, L. Zhang
  • Mathematics, Computer Science
  • IEEE International Conference on Data Mining
  • 13 December 2010
TLDR
We show that computing influence in general networks in the linear threshold model is #P-hard, which closes an open problem left in the seminal work on influence maximization by Kempe, Kleinberg, and Tardos, 2003. Expand
A Draft Sequence of the Rice Genome (Oryza sativa L. ssp. japonica)
The genome of the japonica subspecies of rice, an important cereal and model monocot, was sequenced and assembled by whole-genome shotgun sequencing. The assembled sequence covers 93% of theExpand
IRIE: Scalable and Robust Influence Maximization in Social Networks
TLDR
We propose a novel algorithm IRIE that integrates the advantages of influence ranking (IR) and influence estimation (IE) methods for influence maximization in both the independent cascade (IC) model and its extension IC-N that incorporates negative opinion propagations. Expand
Prominent Features of Rumor Propagation in Online Social Media
TLDR
The problem of identifying rumors is of practical importance especially in online social networks, since information can diffuse more rapidly and widely than offline counterpart. Expand
Combinatorial Pure Exploration of Multi-Armed Bandits
TLDR
We study the combinatorial pure exploration (CPE) problem in the stochastic multi-armed bandit setting, where a learner explores a set of arms with the objective of identifying the optimal member of a decision class, which is a collection of subsets of arms. Expand
Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model
TLDR
We study competitive influence propagation in social networks under the competitive linear threshold (CLT) model, an extension to the classic linear threshold model. Expand
...
1
2
3
4
5
...