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

Abstract

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.

Community Detection for Access-Control Decisions: Analysing the Role of Homophily and Information Diffusion in Online Social Networks

“…number of connections to other nodes) widely exceeds the average [47,48]. Up to now, scholars have proposed several evolution models for constructing scale-free networks [22,[49][50][51]. Among those models, one of the most prominent ones is the one introduced by Barabasi and Albert [51].…”

“…In principle, the preferential attachment mechanism proposed by Barabasi and Albert does not take attribute similarity into consideration. Nevertheless, this approach has been enriched with homophily characteristics by other researchers resulting in more specific network evolution models [17,22,49]. For example, Kim et al [22] introduced a group-openness mechanism for modelling the homophily and attachment probability between two nodes in a network.…”

Comparative study of Existing Models for Online Social Network

“…Influence models play very important role for deciding either how people can influence each other on the basis of action strategy policies they have implemented on social network sites and E-commerce sites. But implementing predictive analytics and prescriptive analytics on influence models becomes powerful strategy to increase the revenue and predict customer behavior retention policy [19] [20].…”

Implicit User Trust Modeling Based on User Attributes and Behavior in Online Social Networks

“…Our model is one of such type, and the data we need for our model evaluation was not available in a single dataset. Therefore, in this work for our model evaluation, we used synthetic data generated by using our previously proposed model in [64], as mentioned in Table 4. The dataset descriptions are available in our previously published paper [64].…”

“…Therefore, in this work for our model evaluation, we used synthetic data generated by using our previously proposed model in [64], as mentioned in Table 4. The dataset descriptions are available in our previously published paper [64]. For verification of our results, we used twitter data, crawled using Gephi, and two publicly available datasets, i.e., Movielens dataset [65], and Filmtrust dataset [66].…”