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16 Nov 2024
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Discrepancies in the perception of social support relationships (Stage 1 Registered Report)

Social Support Discrepancies in Adolescence: Dual Perspectives on Perception, Gender Dynamics, and Mental Health

Recommended by ORCID_LOGO based on reviews by Zachary P. Neal and Alexandre Naud

Social support encompasses various functions within social networks, facilitating emotional, instrumental, and informational exchanges that promote well-being (House et al. 1988; Thoits 2011; Sueur et al. 2021). Emotional support, such as empathy and reassurance, directly contributes to psychological health and can buffer against stress. However, perceived social support often correlates more strongly with well-being than enacted support, which may sometimes yield contrary effects, as studies have shown (Haber et al. 2007; Chu et al. 2010). This discrepancy between perceived and provided support underscores the role of individual perception in social dynamics (Sueur et al. 2024).

The cognitive triad theory by Beck (1979) suggests that depressive thought patterns—negative views of self, environment, and future—distort perceptions, which may affect social support recognition. Individuals with depression often struggle to perceive or remember supportive behaviors accurately, filtering out positive feedback (Gotlib and Joormann 2010). These biases highlight the importance of subjective interpretation in social relationships, with social cognition research suggesting that social support exhibits trait-like stability and that pre-existing cognitive schemas shape support perception (Mankowski and Wyer 1997).

Gender differences in support perception have been widely documented, with young women generally perceiving and offering more social support than men (Rueger et al. 2016). Socialization influences may explain these discrepancies; for instance, girls often learn to express warmth and empathy more readily, enhancing both their recognition of and access to support (Brashears et al. 2016). Consequently, support dynamics are not only shaped by individual mental health and social network structure but also by sociocultural factors that influence emotional processing and relationship assessment.

Krüger et al. (2024) brings innovative elements to understanding social support discrepancies among adolescents by employing a dual-perspective network analysis. Unlike traditional studies that focus on either the support provider’s or receiver’s perspective, this research uses both perspectives within adolescent social networks to reveal the degree of mismatch in support perception. For example, “provided but not perceived” and “perceived but not provided” support discrepancies were identified, illuminating how gender influences support dynamics. Findings reveal that young men are more likely to experience unnoticed support provision, suggesting that gender norms around emotional expression could hinder recognition of support in male-provided interactions.

Additionally, the study finds that discrepancies are more common in opposite-sex dyads than same-sex ones, highlighting how gender-based socialization impacts support perceptions. Adolescents, especially in cross-gender interactions, may face interpretative challenges in recognizing support, possibly due to gendered expectations around emotional engagement. This gender-focused insight into social support perception is unique, providing a new layer of understanding for support network dynamics in adolescence.

Another innovative aspect is the study’s integration of mental health and loneliness as variables. Contrary to previous assumptions, these factors do not significantly impact support perception discrepancies, challenging the view that mental health primarily skews support perception. This finding suggests that social support recognition issues may be less about individual mental health status and more about relational dynamics and social norms.

In methodological terms, the use of multi-level modeling to account for school-level variations and individual differences further advances social support research by offering a more granular view of how environmental and personal factors intersect to shape support perceptions among adolescents​. It would be particularly interesting to explore how this methodology could be applied to animal social network analyses (Sueur et al. 2012; Battesti et al. 2015; Borgeaud et al. 2017; Romano et al. 2018), especially given evidence that loneliness exists in monkeys (Capitanio et al. 2014, 2019). For example, studies could investigate whether similar discrepancies exist in animal groups, such as unrecognized affiliative behaviors or mismatches in perceived versus actual social bonds. By adapting this approach, researchers could examine how social perception and interaction influence group cohesion, stress buffering, and overall well-being in animal societies, potentially offering a deeper understanding of the evolutionary and ecological drivers of social support in non-human species.

References

Battesti M, Pasquaretta C, Moreno C, et al (2015) Ecology of information: social transmission dynamics within groups of non-social insects. Proc R Soc Lond B Biol Sci 282:20142480. https://doi.org/10.1098/rspb.2014.2480

Beck AT (1979) Cognitive Therapy and the Emotional Disorders. Penguin

Borgeaud C, Sosa S, Sueur C, Bshary R (2017) The influence of demographic variation on social network stability in wild vervet monkeys. Anim Behav 134:155–165. https://doi.org/10.1016/j.anbehav.2017.09.028

Brashears ME, Hoagland E, Quintane E (2016) Sex and network recall accuracy. Soc Netw 44:74–84. https://doi.org/10.1016/j.socnet.2015.06.002

Capitanio JP, Cacioppo S, Cole SW (2019) Loneliness in monkeys: neuroimmune mechanisms. Curr Opin Behav Sci 28:51–57. https://doi.org/10.1016/j.cobeha.2019.01.013

Capitanio JP, Hawkley LC, Cole SW, Cacioppo JT (2014) A Behavioral Taxonomy of Loneliness in Humans and Rhesus Monkeys (Macaca mulatta). PLOS ONE 9:e110307. https://doi.org/10.1371/journal.pone.0110307

Chu PS, Saucier DA, Hafner E (2010) Meta-Analysis of the Relationships Between Social Support and Well-Being in Children and Adolescents. J Soc Clin Psychol 29:624–645. https://doi.org/10.1521/jscp.2010.29.6.624

Gotlib IH, Joormann J (2010) Cognition and Depression: Current Status and Future Directions. Annu Rev Clin Psychol 6:285–312. https://doi.org/10.1146/annurev.clinpsy.121208.131305

Haber MG, Cohen JL, Lucas T, Baltes BB (2007) The relationship between self-reported received and perceived social support: A meta-analytic review. Am J Community Psychol 39:133–144. https://doi.org/10.1007/s10464-007-9100-9

House JS, Umberson D, Landis KR (1988) Structures and processes of social support. Annu Rev Sociol 14:293–318. https://doi.org/10.1146/annurev.so.14.080188.001453

Heike Krüger, Thomas Grund, Srebrenka Letina, Emily Long, Julie Riddell, Claudia Zucca, Mark McCann (2024) Discrepancies in the perception of social support relationships (Stage 1 Registered Report). OSF preprints, ver.5 peer-reviewed and recommended by PCI Network Science https://doi.org/10.31219/osf.io/uc2qy

Mankowski ES, Wyer RS (1997) Cognitive Causes and Consequences of Perceived Social Support. In: Pierce GR, Lakey B, Sarason IG, Sarason BR (eds) Sourcebook of Social Support and Personality. Springer US, Boston, MA, pp 141–165

Romano V, Shen M, Pansanel J, et al (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

Rueger SY, Malecki CK, Pyun Y, et al (2016) A meta-analytic review of the association between perceived social support and depression in childhood and adolescence. Psychol Bull 142:1017–1067. https://doi.org/10.1037/bul0000058

Sueur C, Fancello G, Naud A, et al (2024) The Complexity of Social Networks in Healthy Aging: Novel Metrics and Their Associations with Psychological Well-Being. Peer Community J 4:. https://doi.org/10.24072/pcjournal.388

Sueur C, King AJ, Pelé M, Petit O (2012) Fast and accurate decisions as a result of scale-free network properties in two primate species. In: Lecture Notes in Computer Science

Sueur C, Quque M, Naud A, et al (2021) Social capital: an independent dimension of healthy ageing. Peer Community J 1:. https://doi.org/10.24072/pcjournal.33

Thoits PA (2011) Mechanisms Linking Social Ties and Support to Physical and Mental Health. J Health Soc Behav 52:145–161. https://doi.org/10.1177/0022146510395592

 

Discrepancies in the perception of social support relationships (Stage 1 Registered Report)Heike Krüger, Thomas Grund, Srebrenka Letina, Emily Long, Julie Riddell, Claudia Zucca, Mark McCann<p>Objective: Prior research in the area of social support suggests that it is an important influential factor of mental health. Yet, it often remains unclear how much overlap there is between provided support, the perceived availability of suppor...Social networksCédric Sueur2024-06-18 13:48:02 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