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| Id | Title * | Authors * | Abstract * | Picture * | Thematic fields * | Recommender | Reviewers | Submission date▲ | |
|---|---|---|---|---|---|---|---|---|---|
08 Mar 2024
Comparison of modularity-based approaches for nodes clustering in hypergraphsA theoretical and empirical evaluation of modularities for hypergraphsRecommended by Remy Cazabet based on reviews by salvatore citraro and 1 anonymous reviewer
Hypergraphs, as a framework to model higher-order interactions, have attracted a lot of attention in recent years. One particularly fruitful research direction consists of transposing well-defined notions in simple graphs to this new paradigm. A difficulty, but also an interesting opportunity, of this task is that a single concept in simple graphs might correspond to multiple ones in the domain to which it is transposed. The problem has for instance been discussed for link streams in Latapy et al. (2018), for notions as simple as node neighborhoods or the notion of shortest path. In the present article (Poda and Matias 2024), Poda and Matias focus on the concept of modularity and, indeed, they identify multiple definitions of modularity for hypergraphs in the literature (Chodrow et al., 2021, Kaminski et al., 2021). The first interesting contribution is the unification of these different representations using a common framework. They can thus compare, based solely on the definitions themselves, theoretical similarities and differences between those modularities for hypergraphs. In the second part of their contribution, they turn towards the empirical evaluation of these methods. Community detection has a long tradition of experimental articles, comparing on selected benchmarks the strengths and weaknesses of selected methods, from the seminal work from Lancichinetti and Fortunato (Lancichinetti et al., 2009), to recent works comparing, for instance, modularity methods in dynamic graphs (Cazabet et al., 2020). The authors thus point to existing implementations and start comparing them using existing benchmarks for hypergraphs (Brusa and Matias, 2022, Kaminski et al., 2023). This confrontation between theoretical definition and actual networks with varying properties allows them to identify methods that do not perform as expected. Furthermore, they do not solely focus on classification performance but also evaluate other factors such as scalability. Their findings reveal that all methods perform poorly in this aspect. These observations pave the way for future work. To conclude, this work is a very relevant contribution to the field. One could say that the first empirical comparison of methods in a particular field is a sign that it has become mature, and that is maybe one of the conclusions to draw from this article. References Poda, V., & Matias, C. (2024). Comparison of modularity-based approaches for nodes clustering in hypergraphs. arXiv preprint. HAL, https://hal.science/hal-04414337v2 | Comparison of modularity-based approaches for nodes clustering in hypergraphs | Veronica Poda, Catherine Matias | <p>Statistical analysis and node clustering in hypergraphs constitute an emerging topic suffering from a lack of standardization. In contrast to the case of graphs, the concept of nodes' community in hypergraphs is not unique and encompasses vario... | | Clustering in networks, Graph algorithms | Remy Cazabet | 2024-01-25 10:19:55 | ||
16 Nov 2024
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 HealthRecommended by Cédric Sueur 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 networks | Cédric Sueur | 2024-06-18 13:48:02 |
