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Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process
Wei Chen,Wei Lu,Ning Zhang +2 more
TLDR
In this article, the authors consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline, and extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks.Abstract:
Influence maximization is a problem of finding a small set of highly influential users, also known as seeds, in a social network such that the spread of influence under certain propagation models is maximized. In this paper, we consider time-critical influence maximization, in which one wants to maximize influence spread within a given deadline. Since timing is considered in the optimization, we also extend the Independent Cascade (IC) model and the Linear Threshold (LT) model to incorporate the time delay aspect of influence diffusion among individuals in social networks. We show that time-critical influence maximization under the time-delayed IC and LT models maintains desired properties such as submodularity, which allows a greedy approximation algorithm to achieve an approximation ratio of $1-1/e$. To overcome the inefficiency of the greedy algorithm, we design two heuristic algorithms: the first one is based on a dynamic programming procedure that computes exact influence in tree structures and directed acyclic subgraphs, while the second one converts the problem to one in the original models and then applies existing fast heuristic algorithms to it. Our simulation results demonstrate that our algorithms achieve the same level of influence spread as the greedy algorithm while running a few orders of magnitude faster, and they also outperform existing fast heuristics that disregard the deadline constraint and delays in diffusion.read more
Citations
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Proceedings ArticleDOI
Influence Maximization in Near-Linear Time: A Martingale Approach
TL;DR: The proposed influence maximization algorithm is a set of estimation techniques based on martingales, a classic statistical tool that provides the same worst-case guarantees as the state of the art, but offers significantly improved empirical efficiency.
Proceedings ArticleDOI
Influence maximization: near-optimal time complexity meets practical efficiency
TL;DR: TIM as discussed by the authors is an algorithm for influence maximization that runs in O((k+ l) (n+m) log n/e2) expected time and returns a (1-1/e-e)-approximate solution with at least 1 - n-l probability.
Book
Information and Influence Propagation in Social Networks
TL;DR: A detailed description of well-established diffusion models, including the independent cascade model and the linear threshold model, that have been successful at explaining propagation phenomena are described as well as numerous extensions to them, introducing aspects such as competition, budget, and time-criticality, among many others.
Journal ArticleDOI
Who Influenced You? Predicting Retweet via Social Influence Locality
TL;DR: A novel notion of social influence locality is proposed and two instantiation functions based on pairwise influence and structural diversity are developed, which can further leverage the network-based correlation.
Journal ArticleDOI
A Sword with Two Edges: Propagation Studies on Both Positive and Negative Information in Online Social Networks
TL;DR: The social parameters impact on propagation are studied and it is found that some factors such as people's preference and the injection time of the opposing information are critical to the propagation but some others such as the hearsay forwarding intention have little impact on it.
References
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An analysis of approximations for maximizing submodular set functions--I
TL;DR: It is shown that a “greedy” heuristic always produces a solution whose value is at least 1 −[(K − 1/K]K times the optimal value, which can be achieved for eachK and has a limiting value of (e − 1)/e, where e is the base of the natural logarithm.
Proceedings ArticleDOI
Mining the network value of customers
TL;DR: It is proposed to model also the customer's network value: the expected profit from sales to other customers she may influence to buy, the customers those may influence, and so on recursively, taking advantage of the availability of large relevant databases.
Proceedings ArticleDOI
Mining knowledge-sharing sites for viral marketing
TL;DR: This research optimize the amount of marketing funds spent on each customer, rather than just making a binary decision on whether to market to him, and takes into account the fact that knowledge of the network is partial, and that gathering that knowledge can itself have a cost.
Proceedings ArticleDOI
Scalable influence maximization for prevalent viral marketing in large-scale social networks
Wei Chen,Chi Wang,Yajun Wang +2 more
TL;DR: The results from extensive simulations demonstrate that the proposed algorithm is currently the best scalable solution to the influence maximization problem and significantly outperforms all other scalable heuristics to as much as 100%--260% increase in influence spread.
Proceedings ArticleDOI
Scalable Influence Maximization in Social Networks under the Linear Threshold Model
Wei Chen,Yifei Yuan,Li Zhang +2 more
TL;DR: This paper proposes the first scalable influence maximization algorithm tailored for the linear threshold model, which is scalable to networks with millions of nodes and edges, is orders of magnitude faster than the greedy approximation algorithm proposed by Kempe et al. and its optimized versions, and performs consistently among the best algorithms.