Modeling Social Network Interaction Graphs

Durr, Michael; Protschky, V.; Linnhoff‐Popien, Claudia

doi:10.1109/asonam.2012.110

Cited by 4 publications

(6 citation statements)

References 26 publications

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“…In OSN, higher degree nodes generate more interactions and high degree nodes cover the largest proportion of the total interactions on OSN [9]. In [10], a generic model, for synthetic social interaction graph generation, is presented. It is concluded that, in SN, social interactions have power law distribution.…”

Section: Social Network Synthetic Graph Generationmentioning

confidence: 99%

“…The w s and w deg are the weights of the similarity and degree-based attachments, respectively. The computational formula for degree based attachment [10], is shown in Equation 8:…”

Section: Preliminariesmentioning

confidence: 99%

“…According to [9], user activities also obey the power law distribution, i.e., there are only a small proportion of users who produce a significant proportion of activities over SN. In [10], it was observed that almost 80% of user activities, within an SN group, are produced by 20% of the users in the group. We produced different types of user activities/interactions with a proportion from [9,10,82].…”

Section: Preliminariesmentioning

confidence: 99%

“…In [10], it was observed that almost 80% of user activities, within an SN group, are produced by 20% of the users in the group. We produced different types of user activities/interactions with a proportion from [9,10,82].…”

Section: Preliminariesmentioning

confidence: 99%

“…In most SN evolution studies, the network topological structure is considered to be the basis of changes in the network. Topologically, due to continuous addition of new nodes and their preferential attachments with high degree nodes, the nodes connectivity follows the property of scale-free power law distribution [9,10].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Online Social Networks (OSN) Evolution Model Based on Homophily and Preferential Attachment

Khan

Lee

2018

Symmetry

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In this paper, we propose a new scale-free social networks (SNs) evolution model that is based on homophily combined with preferential attachments. Our model enables the SN researchers to generate SN synthetic data for the evaluation of multi-facet SN models that are dependent on users’ attributes and similarities. Homophily is one of the key factors for interactive relationship formation in SN. The synthetic graph generated by our model is scale-invariant and has symmetric relationships. The model is dynamic and sustainable to changes in input parameters, such as number of nodes and nodes’ attributes, by conserving its structural properties. Simulation and evaluation of models for large-scale SN applications need large datasets. One way to get SN data is to generate synthetic data by using SN evolution models. Various SN evolution models are proposed to approximate the real-life SN graphs in previous research. These models are based on SN structural properties such as preferential attachment. The data generated by these models is suitable to evaluate SN models that are structure dependent but not suitable to evaluate models which depend on the SN users’ attributes and similarities. In our proposed model, users’ attributes and similarities are utilized to synthesize SN graphs. We evaluated the resultant synthetic graph by analyzing its structural properties. In addition, we validated our model by comparing its measures with the publicly available real-life SN datasets and previous SN evolution models. Simulation results show our resultant graph to be a close representation of real-life SN graphs with users’ attributes.

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Section: Social Network Synthetic Graph Generationmentioning

confidence: 99%

“…The w s and w deg are the weights of the similarity and degree-based attachments, respectively. The computational formula for degree based attachment [10], is shown in Equation 8:…”

Section: Preliminariesmentioning

confidence: 99%