Influence maximization in near-linear time: A martingale approach
Given a social network G and a positive integer k, the influence maximization problem asks
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …
for k nodes (in G) whose adoptions of a certain idea or product can trigger the largest …
Influence maximization: Near-optimal time complexity meets practical efficiency
Given a social network G and a constant k, the influence maximization problem asks for k
nodes in G that (directly and indirectly) influence the largest number of nodes under a pre …
nodes in G that (directly and indirectly) influence the largest number of nodes under a pre …
Continuous influence maximization: What discounts should we offer to social network users?
Imagine we are introducing a new product through a social network, where we know for
each user in the network the purchase probability curve with respect to discount. Then, what …
each user in the network the purchase probability curve with respect to discount. Then, what …
Scalable influence maximization for independent cascade model in large-scale social networks
Influence maximization, defined by Kempe et al.(SIGKDD 2003), is the problem of finding a
small set of seed nodes in a social network that maximizes the spread of influence under …
small set of seed nodes in a social network that maximizes the spread of influence under …
Staticgreedy: solving the scalability-accuracy dilemma in influence maximization
S Cheng, H Shen, J Huang, G Zhang… - Proceedings of the 22nd …, 2013 - dl.acm.org
Influence maximization, defined as a problem of finding a set of seed nodes to trigger a
maximized spread of influence, is crucial to viral marketing on social networks. For practical …
maximized spread of influence, is crucial to viral marketing on social networks. For practical …
On the upper bounds of spread for greedy algorithms in social network influence maximization
Influence maximization, defined as finding a small subset of nodes that maximizes spread of
influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear …
influence in social networks, is NP-hard under both Independent Cascade (IC) and Linear …
Time-critical influence maximization in social networks with time-delayed diffusion process
Influence maximization is a problem of finding a small set of highly influential users in a
social network such that the spread of influence under certain propagation models is …
social network such that the spread of influence under certain propagation models is …
Fast and accurate influence maximization on large networks with pruned monte-carlo simulations
Influence maximization is a problem to find small sets of highly influential individuals in a
social network to maximize the spread of influence under stochastic cascade models of …
social network to maximize the spread of influence under stochastic cascade models of …
A two-stage stochastic programming approach for influence maximization in social networks
HH Wu, S Küçükyavuz - Computational Optimization and Applications, 2018 - Springer
We consider stochastic influence maximization problems arising in social networks. In
contrast to existing studies that involve greedy approximation algorithms with a 63 …
contrast to existing studies that involve greedy approximation algorithms with a 63 …
From competition to complementarity: comparative influence diffusion and maximization
W Lu, W Chen, LVS Lakshmanan - arXiv preprint arXiv:1507.00317, 2015 - arxiv.org
Influence maximization is a well-studied problem that asks for a small set of influential users
from a social network, such that by targeting them as early adopters, the expected total …
from a social network, such that by targeting them as early adopters, the expected total …