Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

Humski, Luka; Pintar, Damir; Vranić, Mihaela

doi:10.1109/access.2018.2886468

Cited by 7 publications

(13 citation statements)

References 45 publications

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“…This paper is a continuation of the research presented in [7], which describes a conceptual solution for an expanded social graph generator. That solution, amongst other prerequisites, requires input in the form of a generated synthetic list of total amounts of users' interactions on online social networks summarized at the ego-user level.…”

Section: Introductionmentioning

confidence: 97%

“…The research described in this paper, building upon [7], sets out to examine whether the proposed method really is applicable to the described problem, and whether other, more adequate methods can be found. A graphical representation of this approach is shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…researching existing methods for synthetic data generation and carrying out a comparative analysis of them, with emphasis on applicability to the posed data generation challenge. Analysis showed that the method proposed in [7] is the best solution that could be found to date, and therefore it is selected as the method this paper will be based upon. However, it still requires certain modifications to suit the posed data generation problem.…”

Section: Introductionmentioning

confidence: 99%

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Data Set Synthesis Based on Known Correlations and Distributions for Expanded Social Graph Generation

et al. 2020

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Nowadays, data created through the usage of different services are most commonly not available to the average researcher. Security and privacy have become a top concern, which has further restricted access to certain real-life data, especially holding true for social networks. This is why synthetic data generators have become a very important area of research, particularly synthetic social graph generators. However, even today, such generators mostly create graphs that contain just the information whether two nodes are connected. Fortunately, there is an existing conceptual solution for an expanded social graph generator that aims to generate synthetic graphs containing multiple weighted edges between nodes, thus showing various types of relationships among those nodes, all based on known real-life data characteristics. One of its proposed steps is the generation of necessary data according to provided distributions and correlations. This paper focuses on the generation of such data by adapting an existing iterative algorithm for non-normal multivariate data simulation to generate synthetic data based on the publicly available distributions and correlations of Facebook interaction parameters. It is shown that the characteristics of the generated synthetic data are similar to the known characteristics of the real-life data, proving that the chosen algorithm, along with the accompanying alterations, can be used as one of the steps within the process of generating a synthetic expanded social graph.

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Section: Introductionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Set Synthesis Based on Known Correlations and Distributions for Expanded Social Graph Generation

et al. 2020

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“…• Study the evolution of Online Relationships over time (Humski et al, 2019) Multiple edges in ESG All the different representations of OSN and their findings, as discussed in the previous section, give an insight to an integrated framework that leaves immense scope for exploration. As stated earlier, a well-represented data can be well analysed.…”

Section: Referencesmentioning

confidence: 99%

“…Designing a graph mining tool that helps in discovery of cluster association is important for further research. The previous research work has been extended in (Humski, Pintar, & Vranic, 2019) that dealt with the generation of synthetic datasets. Facebook data isn't readily available because of privacy concerns, so it becomes important to expand upon a small data set and generate a large one.…”

Section: Introductionmentioning

confidence: 99%

An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

Ganguli

Mehta²,

Debnath

et al.

EPiC Series in Computing

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Study of relationships established in social media is an emerging area of research. Online Social Network (OSN) is a collection of social entities carrying a lot of information that enriches the network. A structured modelling of the OSN dataset is required for informative knowledge mining and efficient Social Network Analysis (SNA). Graphical representation of data helps in analysing the structural properties, study of dense substructure, cluster formation and identifying the numerous types of entities exhibiting associations based on different activity fields. This paper discusses about various ways of graph theoretic representations of OSN including structure-based and content or interaction-based approaches. An integrated framework is proposed in this paper that learns from various user attributes and its associated interactions, network structure, timeline history, etc from a polarized OSN Graph for generating an efficient Friend Suggestion Recommender System.

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Designing AI-Support VR by Self-supervised and Initiative Selective Supports

Mukherjee,

Lu,

Ochiai

2022

Lecture Notes in Computer Science

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Analysis of Facebook Interaction as Basis for Synthetic Expanded Social Graph Generation

Abstract: The work was supported by the Croatian Science Foundation under the Project UIP-2014-09-2051 eduMINE-Leveraging Data Mining Methods and Open Technologies for Enhancement of the e-Learning Infrastructure.

Cited by 7 publications

References 45 publications

Data Set Synthesis Based on Known Correlations and Distributions for Expanded Social Graph Generation

Data Set Synthesis Based on Known Correlations and Distributions for Expanded Social Graph Generation

An Integrated Framework for Friend Recommender System based on Graph Theoretic Approach

Designing AI-Support VR by Self-supervised and Initiative Selective Supports

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