Scalable Influence Maximization in Social Networks under the Linear Threshold Model

By Wei Chen, Yifei Yuan, Li Zhang. 2010

962 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Networks and Communications, Statistical and Nonlinear Physics

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Citations in the corpus

Scalable Influence Estimation in Continuous-Time Diffusion Networks (2013, background): “Most previous work in the literature tackled the influence estimation and maximization problems for infinite time window [1, 2, 3, 4, 5, 6].”
Recommender Systems (2016, neutral): “Other recent models for influence analysis in social networks are discussed in [153, 154, 369, 589].”
Limiting the spread of misinformation in social networks (2011, background): “Work has been done to improve the performance of this greedy algorithm [8, 31, 26, 9], but scalability remains a significant challenge.”
Influence maximization: near-optimal time complexity meets practical efficiency (2014, methodology): “Following previous work [8], we construct T (v) for each node v, by first assigning a random probability in [0, 1] to each of v’s incoming neighbors, and then normalizing the probabilities so that they sum up to 1.”
SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, methodology): “However, computing exact marginal gain (or exact expected spread) under both the IC and LT models is #P-hard [6], [7].”
CELF++: optimizing the greedy algorithm for influence maximization in social networks (2011, neutral): “Considerable work has been done on tackling the ﬁrst issue, by using eﬃcient heuristics for estimating the spread [2, 5, 1, 3] to register huge gains on this front.”
Maximizing Social Influence in Nearly Optimal Time (2012, background): “One particular approach of note involves first attempting to sparsify the input graph, then estimating influence on the reduced network [14, 30].”
Influence Maximization in Social Networks When Negative Opinions May Emerge and Propagate (2011, methodology): “…with a standard greedy approximation algorithm while being orders of magnitude faster. keywords: influence maximization; social networks; negative opinions; independent cascade model; ∗Author affiliations and emails: W. Chen (contact author), Microsoft Research Asia, China, weic@microsoft.com.”
S3: Social-network Simulation System with Large Language Model-Empowered Agents (2023, neutral): “We compare our approach with two widely-used baseline models: the Linear Threshold (LT) model [8] and the Independent Cascade (IC) model [18].”
Scalable diffusion-aware optimization of network topology (2014, background): “Their result was followed by a whole line of research on source node selection in various related information diffusion contexts [4, 7, 9].”
IRIE: Scalable and Robust Influence Maximization in Social Networks (2011, methodology): “The best heuristic algorithm so far is believed to be the PMIA algorithm proposed by Chen et al. [3], which provides matching inﬂuence spread while running at three orders of magnitude faster than the optimized greedy algorithm.”
A Data-Based Approach to Social Influence Maximization (2011, methodology): “[4] W. Chen et al. Scalable influence maximization in social networks under the linear threshold model.”
Influence Maximization on Social Graphs: A Survey (2018, methodology): “LDAG [45] (cid:129) t u is the time complexity for constructing the LDAG for one node; n u and m u are the maximum number of nodes and edges in one LDAG.”
Time-Critical Influence Maximization in Social Networks with Time-Delayed Diffusion Process (2012, methodology): “We first run the Find-LDAG algorithm due to [4] (Algorithm 3).”