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

Social relationships are recognized as an important age-related mediator of health in humans and fitness-related traits in animals (Sueur et al., 2021). Vandeleest et al. (2024) is a pioneering exploration into the complex interplay between social relationships and health in rhesus macaques. It breaks new ground by differentiating between two types of affiliative relationships – multiplex (engaging in multiple types of affiliative behaviors like grooming and contact sitting) and uniplex (involving only one type of behavior, such as grooming) (Beisner et al., 2020). The study's crux lies in its novel approach to understanding how these differing social interactions correlate with biomarkers of inflammation, namely pro-inflammatory cytokines like IL-6 and TNF-alpha.

The research is innovative in its use of social network analysis (Sosa et al., 2021), allowing for a nuanced view of the rhesus macaques' social dynamics. It reveals that multiplex grooming networks, characterized by more modular structures and kin bias, are associated with lower inflammation levels. This is in contrast to uniplex grooming networks, where a stronger link to social status correlates with higher inflammation. These findings suggest that multiplex relationships could serve as supportive, health-promoting bonds, while uniplex relationships might be more transactional, with possible physiological costs.

Moreover, the study's results highlight the importance of the diversity of affiliative interactions within a dyad. It posits that relationships involving multiple types of affiliative behaviors may have different implications for health and well-being compared to those based on a single behavior type, even if interaction rates are similar. This insight opens up new avenues for understanding the health implications of social behaviors in non-human primates and potentially in humans (Sueur et al., 2021).

Furthermore, the paper provides a comprehensive analysis of the network structures, examining kin bias, clustering, modularity, and associations with dominance rank. It also evaluates the correlations between individual network positions and health markers, offering a multifaceted understanding of how social networks influence physical well-being.

In essence, this research makes a significant contribution to our understanding of the link between sociality and health. It underscores the complexity of social relationships (Moscovice et al., 2020) and their varied impacts on health, suggesting that the nature of social bonds (multiplex vs. uniplex) plays a critical role in determining their health consequences. This study not only enhances our comprehension of primate social behavior but also has broader implications for the fields of social neuroscience, behavioral ecology, and health psychology.

References

Beisner, B., Braun, N., Pósfai, M., Vandeleest, J., D’Souza, R., & McCowan, B. (2020). A multiplex centrality metric for complex social networks: Sex, social status, and family structure predict multiplex centrality in rhesus macaques. PeerJ, 8, e8712. https://doi.org/10.7717/peerj.8712

Moscovice, L. R., Sueur, C., & Aureli, F. (2020). How socio-ecological factors influence the differentiation of social relationships: An integrated conceptual framework. Biology Letters, 16(9), 20200384. https://doi.org/10.1098/rsbl.2020.0384

Sosa, S., Sueur, C., & Puga-Gonzalez, I. (2021). Network measures in animal social network analysis: Their strengths, limits, interpretations and uses. Methods in Ecology and Evolution, 12(1), 10–21. https://doi.org/10.1111/2041-210X.13366

Sueur, C., Quque, M., Naud, A., Bergouignan, A., & Criscuolo, F. (2021). Social capital: An independent dimension of healthy ageing. Peer Community Journal, 1. https://doi.org/10.24072/pcjournal.33

Vandeleest, J. J., Wooddell, L. J., Nathman, A. C., Beisner, B. A., & McCowan, B. (2024). Differential effects of multiplex and uniplex affiliative relationships on biomarkers of inflammation. bioRxiv, ver. 4 peer-reviewed and recommended by Peer Community in Network Science. https://doi.org/10.1101/2022.11.01.514247