A Survey of Models and Algorithms for Social Influence Analysis

@inproceedings{Sun2011ASO,
  title={A Survey of Models and Algorithms for Social Influence Analysis},
  author={Jimeng Sun and Jie Tang},
  booktitle={Social Network Data Analytics},
  year={2011}
}
Social influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social influence has been a widely accepted phenomenon in social networks for decades. Many applications have been built based around the implicit notation of social influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online social network services such as Facebook and Twitter, social… 

Models and algorithms for social influence analysis

TLDR
This tutorial introduces how to verify the existence of social influence in various social networks, and presents computational models for quantifying social influence, and describes how social influence can help real applications.

Models of Influence in Online Social Networks

TLDR
This paper discusses metrics used to measure influence probabilities, and reveals means to maximize social influence by identifying and using the most influential users in a social network, and surveys existing social influence models, and classify them into an original categorization framework.

Influence analysis: A survey of the state-of-the-art

TLDR
This paper summarizes the state-of-art research results on social influence analysis in social networks in a broad sense and presents the applications including web services, marketing, and advertisement services which based on the influence analysis.

Leveraging the power of social propagations: Algorithm designs for social marketing

  • Yu Yang
  • Computer Science, Business
  • 2019
TLDR
This thesis investigates some crucial algorithmic problems in leveraging the propagation effect of social networks in social marketing and provides powerful algorithmic tools to solve these problems effectively, which can deal with large networks containing millions or even tens of millions of vertices in a single machine.

Overlapping Social Network Communities and Viral Marketing

  • S. Y. BhatM. Abulaish
  • Computer Science, Business
    2013 International Symposium on Computational and Business Intelligence
  • 2013
TLDR
The importance of identifying overlapping communities for the task of viral marketing in social networks is presented and some experimental results on an email network are provided to back the claims.

Event Prediction with Community Leaders

  • Jun PangYang Zhang
  • Computer Science
    2015 10th International Conference on Availability, Reliability and Security
  • 2015
TLDR
An algorithm based on events that users conduct to discover leaders in social communities is proposed, demonstrating the effectiveness of leaders' influence on users' behaviors by learning tasks.

On analyzing user preference dynamics with temporal social networks

TLDR
This work proposes a temporal preference model able to detect preference change events of a given user and uses temporal networks concepts to analyze the evolution of social relationships and proposes strategies to detect changes in the network structure based on node centrality.

State of Art Techniques for Social Influence Analysis: A Systematic Literature Review

TLDR
An overview of methods, techniques, and models being employed in influence measurement, their characteristics, dataset used as well as strengths and limitations along with several promising future directions in the subject from 9 research papers are provided.

INFERRING SOCIAL INFLUENCE IN DYNAMIC NETWORKS

An interesting problem in social network analysis is whether individuals’ behaviors or opinions spread from one to another, which is known as social influence. The degrees of influence describes how

The Impact of Social Diversity and Dynamic Influence Propagation for Identifying Influencers in Social Networks

TLDR
This work proposes a dynamic diversity-dependent algorithm for detecting the influencers by evaluating the influence of users throughout social networks, which implies that the pattern of the influence propagation should be updated dynamically to reflect the flow of influence spread.
...

References

SHOWING 1-10 OF 73 REFERENCES

Social influence analysis in large-scale networks

TLDR
Topical Affinity Propagation (TAP) is designed with efficient distributed learning algorithms that is implemented and tested under the Map-Reduce framework and can take results of any topic modeling and the existing network structure to perform topic-level influence propagation.

Influence and correlation in social networks

TLDR
Two simple tests are proposed that can identify influence as a source of social correlation when the time series of user actions is available and are applied to real tagging data on Flickr, exhibiting that while there is significant social correlation in tagging behavior on this system, this correlation cannot be attributed to social influence.

Learning influence probabilities in social networks

TLDR
This paper proposes models and algorithms for learning the model parameters and for testing the learned models to make predictions, and develops techniques for predicting the time by which a user may be expected to perform an action.

Feedback effects between similarity and social influence in online communities

TLDR
Clear feedback effects between the two factors are found, with rising similarity between two individuals serving, in aggregate, as an indicator of future interaction -- but with similarity then continuing to increase steadily, although at a slower rate, for long periods after initial interactions.

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.

Yes, there is a correlation: - from social networks to personal behavior on the web

TLDR
Data mining techniques are applied to study the relationship that exists between a person's social group and his/her personal behavior for a population of over 10 million people, by turning to online sources of data.

Social influence and the diffusion of user-created content

TLDR
An empirical study of user-to-user content transfer occurring in the context of a time-evolving social network in Second Life, a massively multiplayer virtual world finds that sharing among friends occurs more rapidly than sharing among strangers, but that content that diffuses primarily through social influence tends to have a more limited audience.

User grouping behavior in online forums

TLDR
This paper builds social selection models, Bipartite Markov Random Field (BiMRF), and shows that some features carry supplementary information, and the effectiveness of different features vary in different types of forums.

Modeling relationship strength in online social networks

TLDR
This work develops an unsupervised model to estimate relationship strength from interaction activity and user similarity and evaluates it on real-world data from Facebook and LinkedIn, showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy.

Scalable influence maximization for prevalent viral marketing in large-scale social networks

TLDR
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.
...