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22 Feb 2024
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The Complexity of Social Networks in Healthy Aging: Novel Metrics and Their Associations with Psychological Well-Being

An application of PCA to social networks and healthy ageing

Recommended by based on reviews by Christophe Prieur, Paul Rochet and 1 anonymous reviewer

Sueur et al. (2024) investigate the influence of an individual’s social network structure on various aspects of healthy ageing, including depressive symptoms, life satisfaction, and overall well-being. The primary dataset comprises 73 adults aged 60 and above, residing in the Paris region from 2019 to 2020, who completed a VERITAS socioeconomic/demographic questionnaire; and is augmented with official data on the characteristics of residential neighbourhoods. The authors apply principal component analysis (PCA) to network structure metrics including degree centrality, density, and global clustering, and identify four dimensions that they argue have social significance: homophily, social integration, social support, and perceived accessibility to local services. Unexpectedly, the authors’ statistical analysis reveals that none of the PCA dimensions are linked to healthy ageing.

Although network-based PCA dimensions have been used as explanatory variables in other settings, this paper may be the first to apply the technique to healthy ageing. The main result stands in contrast to related literature which indicates that positive social relationships (engagement, sense of community) are related to more favourable mortality and disease outcomes and that these effects persist as people become older. The paper is a useful contribution to an issue that has considerable public health policy importance. It will motivate further research to understand the negative main result, including potential information loss from PCA, issues of small sample bias and identification (relatively few of the respondents were depressed or anxious), specificity of the survey to the Paris region, and more advanced econometric modelling to better understand causal relationships (rather than correlations) between social networks and well-being in older people.

Reference

C. Sueur, G. Fancello, A. Naud, Y. Kestens, and B. Chaix (2024) The complexity of social
networks in healthy aging: novel metrics and their associations with psychological wellbeing. OSF Preprints, ver. 3 peer-reviewed and recommended by Peer Community in Network Science.
https://doi.org/10.31219/osf.io/j9uz8

The Complexity of Social Networks in Healthy Aging: Novel Metrics and Their Associations with Psychological Well-Being Sueur Cédric, Giovanna Fancello, Alexandre Naud, Yan Kestens, Basile Chaix<p>Social networks play a crucial role in promoting healthy aging, yet the intricate mechanisms connecting social capital to health present a complex challenge. Additionally, the majority of social network analysis studies focusing on older adults...Contact networks, Network measures, Personal network analysis, Social networksSteve Lawford2023-03-23 14:41:55 View
05 Jun 2024
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The Structure and Dynamics of Knowledge Graphs, with Superficiality

Unveiling the Hidden Dynamics of Knowledge Graphs: The Role of Superficiality in Structuring Information

Recommended by ORCID_LOGO based on reviews by Mateusz Wilinski, Tamao Maeda and Abiola Akinnubi

Knowledge graphs [1–4] represent structured knowledge using nodes and edges, where nodes signify entities and edges denote relationships between these entities. These graphs have become essential in various fields such as cultural heritage [5], life sciences [6], and encyclopedic knowledge bases, thanks to projects like Yago [7], DBpedia [8], and Wikidata [9]. These knowledge graphs have enabled significant advancements in data integration and semantic understanding, leading to more informed scientific hypotheses and enhanced data exploration.

Despite their importance, understanding the topology and dynamics of knowledge graphs remains a challenge due to their complex and often chaotic nature. Current models, like the preferential attachment mechanism, are limited to simpler networks and fail to capture the intricate interplay of diverse relationships in knowledge graphs. There is a pressing need for models that can accurately represent the structure and dynamics of knowledge graphs, allowing for better understanding, prediction, and utilisation of the knowledge contained within them.

The paper by Lhote, Markhoff, and Soulet [10] introduces a novel approach to modelling the structure and dynamics of knowledge graphs through the concept of superficiality. This model aims to control the overlap between relationships, providing a mechanism to balance the distribution of knowledge and reduce the proportion of misdescribed entities. This is the first model tailored specifically to knowledge graphs, addressing the unique challenges posed by their complexity and diverse relationship types. The innovation lies in the introduction of superficiality, a parameter that governs the probability of adding new entities versus enriching existing ones within the graph. This model not only addresses the multimodal probability distributions observed in real KGs but also offers a more granular understanding of the knowledge distribution, particularly the presence of misdescribed entities. The authors validated their model against three major knowledge graphs: BnF, ChEMBL, and Wikidata. The results demonstrated that the generative model accurately reproduces the observed distributions of incoming and outgoing degrees in these knowledge graphs. The model successfully captures the multimodal nature and the irregularities in the degree distributions, especially for entities with low connectivity, which are typically the majority in a knowledge graphs.

One significant finding is the impact of superficiality on the level of misdescribed entities. The study revealed that lower superficiality leads to a more uniform distribution of relationships across entities, thus reducing the number of entities described by few relationships. Conversely, higher superficiality results in a higher proportion of entities with minimal descriptive facts, reflecting a paradox where increasing the volume of knowledge does not necessarily reduce the level of ignorance. The authors also conducted an ablation study comparing their model to traditional models like Barabási-Albert [11] and Bollobás [12]. The results showed that the proposed multiplex model with superficiality parameters consistently outperformed these traditional models in accurately reflecting the characteristics of real-world knowledge graphs. 

This research provides a groundbreaking approach to understanding and modelling the structure and dynamics of knowledge graphs. By introducing superficiality, the authors offer a new lens through which to examine the distribution and organisation of knowledge within these complex structures. The model not only enhances our theoretical understanding of knowledge graphs but also has practical implications for improving data storage, query optimisation, and the robustness of knowledge induction processes.

The introduction of superficiality opens several avenues for future research and application. One potential direction is refining the model to account for localised perturbations in smaller knowledge graphs or specific domains within larger knowledge graphs. Additionally, longitudinal studies could further elucidate the evolution of superficiality over time and its impact on the quality of knowledge representation. Another promising area is the application of this model in real-time knowledge graphs management systems. By adjusting superficiality parameters dynamically, it may be possible to optimise the balance between entity enrichment and the introduction of new entities, leading to more robust and accurate knowledge graphs. In the broader context of knowledge engineering and data science, this model offers a framework for exploring the vulnerability of knowledge graphs and their susceptibility to various types of biases and inaccuracies. This understanding could lead to the development of more resilient knowledge systems capable of adapting to new information while maintaining a high level of accuracy and coherence.

Overall, the concept of superficiality and the associated generative model represent significant advancements in the study and application of knowledge graphs, promising to enhance both our theoretical understanding and practical capabilities in managing and utilising these complex data structures. It would be interesting to see how this can be extended to domains in social network analyses [13,14].

References

​1.     Nickel M, Murphy K, Tresp V, Gabrilovich E. 2015 A review of relational machine learning for knowledge graphs. Proceedings of the IEEE 104, 11-33. https://doi.org/10.1109/JPROC.2015.2483592

2.     Ehrlinger L, Wöß W. 2016 Towards a definition of knowledge graphs. SEMANTiCS (Posters, Demos, SuCCESS) 48, 2.

3.     Hogan A et al. 2021 Knowledge graphs. ACM Computing Surveys (Csur) 54, 1-37.

4.     Ji S, Pan S, Cambria E, Marttinen P, Philip SY. 2021 A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems 33, 494-514. https://doi.org/10.1109/TNNLS.2021.3070843

5.     Bikakis A, Hyvönen E, Jean S, Markhoff B, Mosca A. 2021 Special issue on semantic web for cultural heritage. Semantic Web 12, 163-167. https://doi.org/10.3233/SW-210425

6.     Santos A et al. 2022 A knowledge graph to interpret clinical proteomics data. Nature biotechnology 40, 692-702. https://doi.org/10.1038/s41587-021-01145-6

7.     Suchanek FM, Kasneci G, Weikum G. 2007 Yago: a core of semantic knowledge. pp. 697-706. https://doi.org/10.1145/1242572.1242667

8.     Auer S, Bizer C, Kobilarov G, Lehmann J, Cyganiak R, Ives Z. 2007 Dbpedia: A nucleus for a web of open data. pp. 722-735. Springer. https://doi.org/10.1007/978-3-540-76298-0_52

9.     Mora-Cantallops M, Sánchez-Alonso S, García-Barriocanal E. 2019 A systematic literature review on Wikidata. Data Technologies and Applications 53, 250-268. https://doi.org/10.1108/DTA-12-2018-0110

10.  Lhote L, Markhoff B, Soulet A. 2023 The Structure and Dynamics of Knowledge Graphs, with Superficiality. arXiv, ver. 3 peer-reviewed and recommended by Peer Community in Network Science. https://doi.org/10.48550/arXiv.2305.08116

11.  Barabási A-L, Albert R. 1999 Emergence of scaling in random networks. science 286, 509-512. https://doi.org/10.1126/science.286.5439.509

12.  Bollobás B, Borgs C, Chayes JT, Riordan O. 2003 Directed scale-free graphs. pp. 132-139. Baltimore, MD, United States.

13.  Sueur C, King AJ, Pelé M, Petit O. 2013 Fast and accurate decisions as a result of scale-free network properties in two primate species. In Proceedings of the European conference on complex systems 2012 (eds T Gilbert, M Kirkilionis, G Nicolis), pp. 579-584. https://doi.org/10.1007/978-3-319-00395-5_71

14.  Romano V, Shen M, Pansanel J, MacIntosh AJJ, Sueur C. 2018 Social transmission in networks: global efficiency peaks with intermediate levels of modularity. Behav Ecol Sociobiol 72, 154. https://doi.org/10.1007/s00265-018-2564-9

The Structure and Dynamics of Knowledge Graphs, with SuperficialityLoïck Lhote, Béatrice Markhoff, Arnaud Soulet<p>Large knowledge graphs combine human knowledge garnered from projects ranging from academia and institutions to enterprises and crowdsourcing. Within such graphs, each relationship between two nodes represents a basic fact involving these two e...Dynamics on networks, Knowledge and innovation networks, Multilayer, multiplex or multilevel Networks, Random graphs, Self-organization in complex networksCédric Sueur2023-05-16 14:26:33 View