A Study of Information Diffusion over a Realistic Social Network Model

Apolloni, Andrea; Channakeshava, Karthik; Durbeck, Lisa; Khan, Maleq; Kuhlman, Chris J.; Lewis, Bryan; Swarup, Samarth

doi:10.1109/cse.2009.240

Cited by 28 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…, ( )). Virtually, the adjacency of the relation matrix should be included in the dynamic analysis as well, but we here assume that the network remained changeless in a short period (e.g., an event), which is proposed by previous research [21]. In contrast to the individual attributes ℎ , we are more interested in the collective state, for example, the statistics result of the agents' attributes, which describe the characteristics of the whole collective behaviors and states, and they are also called covariates [20].…”

Section: Problem Description Andmentioning

confidence: 99%

See 1 more Smart Citation

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Sun

Guo

et al. 2014

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

Agent based simulation method has become a prominent approach in computational modeling and analysis of public emergency management in social science research. The group emotions evolution, information diffusion, and collective behavior selection make extreme incidents studies a complex system problem, which requires new methods for incidents management and strategy evaluation. This paper studies the group emotion evolution and intervention strategy effectiveness using agent based simulation method. By employing a computational experimentation methodology, we construct the group emotion evolution as a complex system and test the effects of three strategies. In addition, the events-chain model is proposed to model the accumulation influence of the temporal successive events. Each strategy is examined through three simulation experiments, including two make-up scenarios and a real case study. We show how various strategies could impact the group emotion evolution in terms of the complex emergence and emotion accumulation influence in extreme events. This paper also provides an effective method of how to use agent-based simulation for the study of complex collective behavior evolution problem in extreme incidents, emergency, and security study domains.

show abstract

Section: Problem Description Andmentioning

confidence: 99%

“…In this method, the BA network model [22] is used as the relation structure, which can be seen as the interaction environment rather than the behavior model, given it can format the rules of agent selection in interaction. During the period of an event, the network structure can be treated as a static state [21].…”

Section: Model Descriptionmentioning

confidence: 99%

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Sun

Guo

et al. 2014

Discrete Dynamics in Nature and Society

View full text Add to dashboard Cite

show abstract

“…Each such clique can be represented as a node of an otherwise uncorrelated network. As it was indicated in a recent analysis of information spreading, two small and strongly connected groups are linked by individuals who belong to both of them and contribute in the information transfer between them [20]. Once we are interested in a construction of a conventional social network, where humans play the role of nodes, the network should be constructed as the line graph of the initial network of families, school classes etc.…”

Section: Introductionmentioning

confidence: 99%

Line graphs as social networks

Krawczyk

Muchnik

Mańka-Krasoń

et al. 2011

Physica A: Statistical Mechanics and its Applications

View full text Add to dashboard Cite

The line graphs are clustered and assortative. They share these topological features with some social networks. We argue that this similarity reveals the cliquey character of the social networks. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8 x 10^6 people. In particular, sharp maxima of the observed data of the degree dependence of the clustering coefficient C(k) are associated with cliques in the social network.Comment: 11 pages, 4 figure

show abstract

“…This set of nodes can be discovered using probabilistic approaches (e.g., [2,17]) or optimization-based techniques. [12,21] treat influence maximization as a convex optimization problem; this is feasible for influencing small communities but does not scale to larger scale problems.…”

Section: Related Workmentioning

confidence: 99%

Scaling Influence Maximization with Network Abstractions

Maghami

Sukthankar

2014

Lecture Notes in Social Networks

View full text Add to dashboard Cite

Abstract. Maximizing product adoption within a customer social network under a constrained advertising budget is an important special case of the general influence maximization problem. Specialized optimization techniques that account for product correlations and community effects can outperform network-based techniques that do not model interactions that arise from marketing multiple products to the same consumer base. However, it can be infeasible to use exact optimization methods that utilize expensive matrix operations on larger networks without parallel computation techniques. In this chapter, we present a hierarchical influence maximization approach for product marketing that constructs an abstraction hierarchy for scaling optimization techniques to larger networks. An exact solution is computed on smaller partitions of the network, and a candidate set of influential nodes is propagated upward to an abstract representation of the original network that maintains distance information. This process of abstraction, solution, and propagation is repeated until the resulting abstract network is small enough to be solved exactly.

show abstract

A Study of Information Diffusion over a Realistic Social Network Model

Cited by 28 publications

References 14 publications

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Agent Based Simulation of Group Emotions Evolution and Strategy Intervention in Extreme Events

Line graphs as social networks

Scaling Influence Maximization with Network Abstractions

Contact Info

Product

Resources

About