• Corpus ID: 941104

Shaping Social Activity by Incentivizing Users

@article{Farajtabar2014ShapingSA,
  title={Shaping Social Activity by Incentivizing Users},
  author={Mehrdad Farajtabar and Nan Du and Manuel Gomez-Rodriguez and Isabel Valera and Hongyuan Zha and Le Song},
  journal={Advances in neural information processing systems},
  year={2014},
  volume={27}
}
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… 

Figures and Tables from this paper

Steering Social Activity: A Stochastic Optimal Control Point Of View
TLDR
This paper model social activity using the framework of marked temporal point processes, derive an alternate representation of these processes using stochastic differential equations (SDEs) with jumps and develop two efficient online algorithms with provable guarantees to steer social activity both at a user and at a network level.
Cheshire: An Online Algorithm for Activity Maximization in Social Networks
TLDR
This paper model the number of online actions over time using multidimensional Hawkes processes, derive an alternate representation of these processes based on stochastic differential equations (SDEs) with jumps and address the above question from the perspective of stochastically optimal control of SDEs with jumps.
Common Growth Patterns for Regional Social Networks: A Point Process Approach
TLDR
Empirical findings suggest that the startup phase of a regional network can be modeled by a self-exciting point process and the growth of the links can be modeling by a non-homogeneous Poisson process.
Multistage Campaigning in Social Networks
TLDR
Theoretical foundations of optimal campaigning over social networks are established where the user activities are modeled as a multivariate Hawkes process, and a time dependent linear relation between the intensity of exogenous events and several commonly used objective functions of campaigning is derived.
Social Event Magnitudes via Background Influences and Engagement Capacities and its Applications
TLDR
Inspired by event detection algorithms, this study proposes an alternative measure, social event magnitude, by using the product of background influence and cooperation value, which does not just integrate multisource information, but also gives a holistic view about activity levels in a network.
RedQueen: An Online Algorithm for Smart Broadcasting in Social Networks
TLDR
This paper develops a simple and highly efficient online algorithm, RedQueen, to sample the optimal times for the user to post, which is able to consistently make a user's posts more visible over time, is robust to volume changes on her followers' feeds, and significantly outperforms the state of the art.
Correlated Cascades: Compete or Cooperate
TLDR
This work uses a multidimensional marked Hawkes process to model users product adoption and consequently spread of cascades in social networks, and models correlated cascades and also learns the latent diffusion network.
Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularity
TLDR
A novel mathematical model is developed, the Hawkes intensity process, which can explain the complex popularity history of each video according to its type of content, network of diffusion, and sensitivity to promotion, and is used to forecast future popularity given promotions on a large 5-months feed of the most-tweeted videos.
Learning and Forecasting Opinion Dynamics in Social Networks
TLDR
SLANT is introduced, a probabilistic modeling framework of opinion dynamics, which represents users opinions over time by means of marked jump diffusion stochastic differential equations, and allows for efficient model simulation and parameter estimation from historical fine grained event data.
Learning Opinion Dynamics in Social Networks
TLDR
This paper introduces SLANT, a probabilistic modeling framework of opinion dynamics, which allows the underlying opinion of a user to be modulated by those expressed by her neighbors over time, and identifies a set of conditions under which users’ opinions converge to a steady state.
...
...

References

SHOWING 1-10 OF 30 REFERENCES
Modeling Diffusion of Competing Products and Conventions in Social Media
TLDR
This paper proposes a data-driven model, based on continuous-time Hawkes processes, for the adoption and frequency of use of competing products and conventions, and develops an inference method to efficiently fit the model parameters by solving a convex program.
Maximizing the Spread of Influence through a Social Network
TLDR
An analysis framework based on submodular functions shows that a natural greedy strategy obtains a solution that is provably within 63% of optimal for several classes of models, and suggests a general approach for reasoning about the performance guarantees of algorithms for these types of influence problems in social networks.
Modeling Adoption and Usage of Competing Products
TLDR
This model allows us to efficiently simulate the adoption and recurrent usages of competing products, and generate traces in which the authors can easily recognize the effect of social influence, recency and competition, and provides interpretable model parameters.
Learning Social Infectivity in Sparse Low-rank Networks Using Multi-dimensional Hawkes Processes
TLDR
This paper proposes a convex optimization approach to discover the hidden network of social influence by modeling the recurrent events at different individuals as multidimensional Hawkes processes, emphasizing the mutual-excitation nature of the dynamics of event occurrence.
Measuring User Influence in Twitter: The Million Follower Fallacy
TLDR
An in-depth comparison of three measures of influence, using a large amount of data collected from Twitter, is presented, suggesting that topological measures such as indegree alone reveals very little about the influence of a user.
Discovering latent influence in online social activities via shared cascade poisson processes
TLDR
The proposed probabilistic model for discovering latent influence from sequences of item adoption events based on the stochastic EM algorithm can be used for finding influential users, discovering relations between users and predicting item popularity in the future.
Discovering Latent Network Structure in Point Process Data
TLDR
A probabilistic model is developed that combines mutually-exciting point processes with random graph models and shows how the Poisson superposition principle enables an elegant auxiliary variable formulation and a fully-Bayesian, parallel inference algorithm.
Modelling Reciprocating Relationships with Hawkes Processes
TLDR
A Bayesian nonparametric model is presented that discovers implicit social structure from interaction time-series data and outperforms general, unstructured Hawkes processes as well as structured Poisson process-based models at predicting verbal and email turn-taking, and military conflicts among nations.
Quantifying Information Overload in Social Media and Its Impact on Social Contagions
TLDR
A large scale quantitative study of information overload and its impact on information dissemination in the Twitter social media site is evaluated, finding that the rate at which users receive information impacts their processing behavior, including how they prioritize information from different sources, how much information they process, and how quickly they process information.
Influence Maximization in Continuous Time Diffusion Networks
TLDR
It is shown that selecting the set of most influential source nodes in the continuous time influence maximization problem is NP-hard and an efficient approximation algorithm with provable near-optimal performance is developed.
...
...