Communication Dynamics of Blog Networks

Goldberg, Mark; Kelley, Stephen; Magdon‐Ismail, Malik; Mertsalov, Konstantin; Wallace, William A.

doi:10.1007/978-3-642-14929-0_3

Cited by 14 publications

(17 citation statements)

References 22 publications

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“…When diffusion occurs over a social network, the dynamics of the social network determine who is interacting at each time step (e.g. [14]), which in turn determines how the diffusion may spread at that particular time step. In addition, the network may change due to the diffusion that occurs through the network.…”

Section: Network Structuresmentioning

confidence: 99%

Agent-based simulation of the diffusion of warnings

Hui

Goldberg

Magdon‐Ismail

et al. 2010

Proceedings of the 2010 Spring Simulation Multiconference

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Diffusion occurs in various contexts and generally involves a network of entities and interactions between entities. Through these interactions, some property, e.g. information, ideas, etc., is spread through the network. The network may become dynamic as entities in the network interact and information, ideas, etc. flow through the network. This paper presents a general model of diffusion in dynamic networks. We use the model to examine how network structure, seeding strategy, and population inhomogeneity as defined with trust, affects the diffusion process. We simulate an evacuation scenario where the network structure represents a network of households. There are multiple sources that initiate the broadcasts of evacuation warnings and the goals are for the households to propagate the message and perform evacuation. The network dynamics observed are the result of the diffusion, where households may leave the network some time after receiving the warning. The results provide interesting observations on the effects of trust asymmetry and trust differentials. When we introduce population inhomogeneity using trust, the diffusion was more effective. The network structure and the seeding strategy used in delivering the initial broadcast also affect the effectiveness of the diffusion.

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Section: Network Structuresmentioning

confidence: 99%

Agent-based simulation of the diffusion of warnings

Hui

Goldberg

Magdon‐Ismail

et al. 2010

Proceedings of the 2010 Spring Simulation Multiconference

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“…These works provide very limited insights into the traffic characteristics as in reality, the influence scale of the flows vary considerably over both time and space. This paper is motivated by the studies of social influence among human beings in social communities [24]. We investigate the dynamic relationships among the road points and propose a traffic clustering algorithm to partition them into time variant clusters, where the traffic within the same cluster are strongly spatially correlated.…”

Section: Related Workmentioning

confidence: 99%

Traffic clustering and online traffic prediction in vehicle networks: A social influence perspective

Zhang

Xing

Cheng

et al. 2012

2012 Proceedings IEEE INFOCOM

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Abstract-In this paper we investigate the dynamic traffic relationship characterized by a similarity value from one road point to another in vehicle networks. Due to the regularity of human mobility, traffic exhibits strong correlations in both temporal domain and spatial domain. By exploiting the similarity values, we derive application-specific message update rules for affinity propagation, based on which we propose an instant traffic clustering algorithm to partition the road points into time variant clusters, where the traffics within the same cluster are strongly spatially correlated. Online traffic clustering is also considered by clustering combination via evidence accumulation for further influence study. We also present a neural network based traffic prediction algorithm to predict the traffic conditions cluster by cluster for a future time based on the current and historical traffic data. Simulation study on real traffic data demonstrates that our proposed algorithms are able to identify the true influences among road points and provide accurate traffic predictions.

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“…Vertices are evaluated in order of increasing degree, reconsidering vertices from low degree to high repetitively. This algorithm has been previously used for a variety of applications with interesting results (see [11]). A similar method based on greedy local optimization was also given in [1].…”

Section: Connected Iterative Scanmentioning

confidence: 99%

“…We empirically analyze several social networks, including a small, commonly used benchmark dataset, Zachary's Karate Club ( [21]), and a large, real-life dataset, the network of communication in the blog-provider LiveJournal ( [11]). We present a heuristic algorithm which outputs a collection of communities that satisfy our axioms.…”

Section: Introductionmentioning

confidence: 99%

Overlapping communities in social networks

Kelley

Goldberg

Mertsalov³

et al. 2011

IJSCCPS

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Identifying communities is essential for understanding the dynamics of a social network. The prevailing approach to the problem of community discovery is to partition the network into disjoint groups of members that exhibit a high degree of internal communication. This approach ignores the possibility that an individual may belong to two or more groups. Increasingly, researchers have begun to explore new methods which allow groups to overlap. One problem with existing approaches is that the definition of a community comes as the result of a particular algorithm. Such an approach to "defining" communities has been extended to overlapping communities with some success.Our goals in this paper are twofold: first, to present an axiomatic approach to defining overlapping communities in terms of the properties a group should satisfy to be a community; and second, to justify the existence of overlapping in the structure of social communities experimentally using LiveJournal Blog data. Historically, the justification for overlapping groups has been primarily intuitive rather than quantitative. We present a heuristic algorithm which outputs a collection of communities that satisfy the required minimal properties and demonstrate that, in real-life social networks, a large number of individuals are members of communities which have non-trivial overlap with other communities.

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Communication Dynamics of Blog Networks

Cited by 14 publications

References 22 publications

Agent-based simulation of the diffusion of warnings

Agent-based simulation of the diffusion of warnings

Traffic clustering and online traffic prediction in vehicle networks: A social influence perspective

Overlapping communities in social networks

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