Latest recommendations
| 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 | ||
22 Feb 2024
The Complexity of Social Networks in Healthy Aging: Novel Metrics and Their Associations with Psychological Well-BeingAn application of PCA to social networks and healthy ageingRecommended by Steve Lawford based on reviews by Christophe Prieur, Paul Rochet and 1 anonymous reviewerSueur 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 | 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 networks | Steve Lawford | 2023-03-23 14:41:55 |
