Inference of node attributes from social network assortativity

Mulders, Dounia; Bodt, Cyril de; Bjelland, Johannes; Pentland, Alex; Verleysen, Michel; Montjoye, Yves-Alexandre de

doi:10.1007/s00521-018-03967-z

Cited by 7 publications

(6 citation statements)

References 42 publications

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“…Influence on knowledge evolution breadth : duplicate questions negatively correlate with average shortest path length and assortativity coefficient (Mulders et al 2020), suggesting an expansion of the network diameter and increased connection of dissimilar degree nodes. This indicates that high‐degree nodes, such as popular questions, are more inclined to connect with low‐degree nodes, thus potentially expanding the knowledge spectrum.…”

Section: Resultsmentioning

confidence: 99%

Revisiting the Benefits of Duplicate Questions: Evidence from Knowledge Evolution on Stack Overflow

Zhang,

Jiang,

Liu

et al. 2023

Proceedings of the Association for Information Science and Tech

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Stack Overflow (SO) represents one of the most vibrant Question Answering Communities (QACs), providing a crucial platform for developers to pose and respond to questions. SO preserved duplicate questions due to their potential for furnishing additional insights or suggestions. In this paper, we delve into the study of duplicates within SO, with the objective of unraveling their positive value, particularly through the lens of knowledge networks and the evolution. We propose a categorization of knowledge evolution within QACs into two key dimensions: depth and breadth. Our exploration reveals that duplicate questions play a constructive role in fostering both the depth and breadth of knowledge evolution. This finding illuminates the underestimated value of duplicate questions, underlining their significance for the ongoing expansion of knowledge within QACs.

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Section: Resultsmentioning

confidence: 99%

Revisiting the Benefits of Duplicate Questions: Evidence from Knowledge Evolution on Stack Overflow

Zhang,

Jiang,

Liu

et al. 2023

Proceedings of the Association for Information Science and Tech

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“…Labeled assortativity [18], [19] is the tendency in networks that nodes with the same label ("metabolic") will have greater interconnectivity. We applied our approach to the metabolic coordination of 19 tissues validated on two human cohorts derived from the GTEx dataset.…”

Section: Introductionmentioning

confidence: 99%

“…1) includes the following steps: (1) Co-expression module generation using the Weighted Gene Coexpression Network Analysis (WGCNA) [15] algorithm, (2) Module annotation into eight categories (e.g., Metabolism) using enrichment analysis and plurality votes, (3) Community analysis using the CPM (K-Clique Percolation Method) algorithm2 [16] .We used the Monte-Carlo randomization tests [17] to evaluate the significance level of the degree of co-expression of eigengenes of functionally similar co-expression modules (e.g., metabolic), community size and connectivity across the whole human body. Our community analysis tests the metabolic positive labeled assortativity characteristic [18], [19] of our whole-body network, i.e., that tests if similar modules (metabolic modules) exhibit more robust positive connectivity and larger communities than randomly selected actual modules. Labeled assortativity [18], [19] is the tendency in networks that nodes with the same label (“metabolic”) will have greater interconnectivity.…”

Section: Introductionmentioning

confidence: 99%

Whole-body positive synchronization pattern of metabolic transcriptomes

Sussman,

Mor,

Somekh

2023

Preprint

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Understanding the relationship between transcriptomes in the entire body is crucial for comprehending health and disease. To achieve this, we have developed a framework that allows us to measure the general coordination pattern of functionally similar transcriptomes at a whole-body scale. Our approach employs meta-network level analysis to determine the interconnectivity level of metabolic co-expression modules throughout the body as opposed to randomly selected modules. We utilize dimensionality reduction of co-expression modules, inter-tissue connectivity, and clique community analysis. We applied our methodology to 19 distinct human tissues obtained from the GTEx project and detected 40/52 metabolic co-expression modules out of 609/652 cross-tissue generated modules in Cohort 1 and 2 respectively. We detected that the ratio of positive to negative inter-tissue co-expression of metabolic modules was significantly higher than control. We further validated these results for an additional cohort. Moreover, to detect a global concurrent synchronization pattern we performed graph theoretical analysis, specifically a clique community analysis of metabolic transcriptomes showing a significantly larger inter-tissue metabolic transcriptome community that control with a significantly higher connectivity for each metabolic module within the community. Additionally, we performed gene-level validation of our results. In summary, our findings reveal a significant global synchronization pattern of metabolic modules throughout the body. Our framework can be further used for other type of transcriptomes and serve as a measure detecting change in this global coordination pattern across conditions.

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“…Data mining of the users' information can be useful for identifying interaction patterns among the users [2], information credibility assessment [3], and other research purposes. However, it can also create a serious privacy breach to the OSN users by leaking a sensitive attribute or private information about the users [4]. Such data mining technology has reached the public eye through media reports (e.g.…”

Section: Introductionmentioning

confidence: 99%

Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

Reza

Islam

Estivill‐Castro

2021

Neural Comput & Applic

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A malicious data-miner can infer users' private information in online social networks (OSNs) by data mining the users' disclosed information. By exploring the public information about a target user (i.e. an individual or a group of OSN users whose privacy is under attack), attackers can prepare a training data set holding similar information about other users who openly disclosed their data. Using a machine learning classifier, the attacker can input released information about users under attack as non-class attributes and extract the private information as a class attribute. Some techniques offer some privacy protection against specific classifiers, however, the provided privacy can be under threat if an attacker uses a different classifier (rather than the one used by the privacy protection techniques) to infer sensitive information. In reality, it is difficult to predict the classifiers involved in a privacy attack. In this study, we propose a privacy-preserving technique which first prepares a training data set in a similar way that an attacker can prepare and then takes an approach independent of the classifiers to extract patterns (or logic rules) from the training data set. Based on the extracted rule set, it then suggests

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Inference of node attributes from social network assortativity

Cited by 7 publications

References 42 publications

Revisiting the Benefits of Duplicate Questions: Evidence from Knowledge Evolution on Stack Overflow

Revisiting the Benefits of Duplicate Questions: Evidence from Knowledge Evolution on Stack Overflow

Whole-body positive synchronization pattern of metabolic transcriptomes

Privacy protection of online social network users, against attribute inference attacks, through the use of a set of exhaustive rules

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