Shaping Social Activity by Incentivizing Users
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Abstract (arXiv)
Events in an online social network can be categorized roughly into endogenous events, where users just respond to the actions of their neighbors within the network, or exogenous events, where users take actions due to drives external to the network. How much external drive should be provided to each user, such that the network activity can be steered towards a target state? In this paper, we model social events using multivariate Hawkes processes, which can capture both endogenous and exogenous event intensities, and derive a time dependent linear relation between the intensity of exogenous events and the overall network activity. Exploiting this connection, we develop a convex optimization framework for determining the required level of external drive in order for the network to reach a desired activity level. We experimented with event data gathered from Twitter, and show that our method can steer the activity of the network more accurately than alternatives.
Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 2 Aug 2014.
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Citing papers per year: 2021: 3; 2022: 4; 2023: 3; 2024: 3; 2025: 2.
15 papers.
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- 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
- 2021THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequencescited by 19
- 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
- 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processescited by 7
- 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processescited by 7
- 2023A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphscited by 5
- 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Processcited by 3
- 2021Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernelscited by 2
- 2022GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximizationcited by 2
- 2022Online Learning for Mixture of Multivariate Hawkes Processescited by 2
- 2021Survival Analysis of the Compressor Station Based on Hawkes Process with Weibull Base Intensitycited by 0
- 2022Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfecited by 0
- 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks15 Aug 2025
- 2025Looking around you: external information enhances representations for event sequences14 Feb 2025
- 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Process11 May 2024
- 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges1 May 2024
- 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes9 Jan 2024
- 2023A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs26 Dec 2023
- 2023Amortized Network Intervention to Steer the Excitatory Point Processes6 Oct 2023
- 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences10 May 2023
- 2022Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe12 Dec 2022
- 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes27 Oct 2022
- 2022Online Learning for Mixture of Multivariate Hawkes Processes16 Aug 2022
- 2022GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization30 May 2022
3 more
- 2021Survival Analysis of the Compressor Station Based on Hawkes Process with Weibull Base Intensity27 Dec 2021
- 2021THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequences23 May 2021
- 2021Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernels2 Apr 2021
- 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
- 2021THP: Topological Hawkes Processes for Learning Causal Structure on Event Sequencescited by 19
- 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
- 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processescited by 7
- 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processescited by 7
- 2023A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphscited by 5
- 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Processcited by 3
- 2021Surfacing Estimation Uncertainty in the Decay Parameters of Hawkes Processes with Exponential Kernelscited by 2
- 2022GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximizationcited by 2
- 2022Online Learning for Mixture of Multivariate Hawkes Processescited by 2
- 2021Survival Analysis of the Compressor Station Based on Hawkes Process with Weibull Base Intensitycited by 0
- 2022Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfecited by 0
12 most cited of 15 All 15 (JSON)
- 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networks15 Aug 2025
- 2025Looking around you: external information enhances representations for event sequences14 Feb 2025
- 2024RoTHP: Rotary Position Embedding-based Transformer Hawkes Process11 May 2024
- 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challenges1 May 2024
- 2024Distribution-Free Conformal Joint Prediction Regions for Neural Marked Temporal Point Processes9 Jan 2024
- 2023A Variational Autoencoder for Neural Temporal Point Processes with Dynamic Latent Graphs26 Dec 2023
- 2023Amortized Network Intervention to Steer the Excitatory Point Processes6 Oct 2023
- 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequences10 May 2023
- 2022Fast Learning of Multidimensional Hawkes Processes via Frank-Wolfe12 Dec 2022
- 2022Modeling Inter-Dependence Between Time and Mark in Multivariate Temporal Point Processes27 Oct 2022
- 2022Online Learning for Mixture of Multivariate Hawkes Processes16 Aug 2022
- 2022GraMeR: Graph Meta Reinforcement Learning for Multi-Objective Influence Maximization30 May 2022
12 newest of 15 All 15 (JSON)