Tractable Models for Information Diffusion in Social Networks
By Masahiro Kimura, Kazumi Saito. European Conference on Principles of Data Mining and Knowledge Discovery, 2006
480 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics
View Paper: https://www.semanticscholar.org/paper/460a9dbc3bc1434d1999362427f70e96be741b08
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In Computer Networks and Communications
In Statistical and Nonlinear Physics
Citations in the corpus
- Scalable Influence Maximization in Social Networks under the Linear Threshold Model (2010, methodology): “The new heuristic algorithms proposed in [3], [6], [7] are orders of magnitude faster than”
- A Shapley Value-Based Approach to Discover Influential Nodes in Social Networks (2011, background): “Kimura and Saito [30] propose a shortest-path-based influence cascade model and provide efficient algorithms for finding the most influential nodes under these models.”
- Influence Blocking Maximization in Social Networks under the Competitive Linear Threshold Model (2011, background): “One topic in influence diffusion that has been extensively studied is influence maximization [14, 15, 16, 19, 6, 5, 25, 7].”
- Efficient influence maximization in social networks (2009, background): “In [6], Kimura and Saito propose shortest-path based influence cascade models and provide efficient algorithms of compute influence spread under these models.”
- 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.”
- Maximizing Social Influence in Nearly Optimal Time (2012, background): “These have focused largely on heuristics, such as assuming that all nodes have relatively low influence or that the input graph is clustered [12, 14, 25, 38], as well as empirically-motivated implementation improvements [27, 13].”
- Scalable influence maximization for prevalent viral marketing in large-scale social networks (2010, methodology): “Our simulation results show that: (a) the greedy algorithm of [10, 14, 4] and the shortest-path based heuristic [11] have poor scalability: they take hours or days to select 50 seeds when the graph size reaches a few hundred thousands and become infeasible for larger sized graphs, while in the same…”
- A Data-Based Approach to Social Influence Maximization (2011, methodology): “In spite of this big improvement their method still faces serious scalability issues [3], which has motivated recent works on efficient heuristics for overcoming the efficiency and scalability limits of the greedy algorithm [11, 3, 2, 4].”
- CELF++: optimizing the greedy algorithm for influence maximization in social networks (2011, unknown): “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.”
- SIMPATH: An Efficient Algorithm for Influence Maximization under the Linear Threshold Model (2011, background): “Considerable work has been done to address the first limitation in the case of the IC model by developing heuristic solutions for seed selection [8], [9], [6].”
- Influence Maximization on Social Graphs: A Survey (2018, methodology): “Nonetheless, this approach believes that the complex influence model can be effectively reduced to proxy models, e.g., PageRank [53] or the shortest path [54] in practice.”
- Community-based greedy algorithm for mining top-K influential nodes in mobile social networks (2010, background): “Examples of social networks include email networks, online FaceBook, and scientific collaboration networks[11].”