Up ref
This commit is contained in:
Louis
parent
72041bc6ee
commit
56d8ddb492
1 changed files with 347 additions and 1 deletions
348
references.bib
348
references.bib
|
|
@ -21,6 +21,26 @@
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/CVXNL7SP/1990 - Agglomerative Nesting (Program AGNES).pdf}
|
||||
}
|
||||
|
||||
@article{agterbergJointSpectralClustering2025,
|
||||
title = {Joint {{Spectral Clustering}} in {{Multilayer Degree-Corrected Stochastic Blockmodels}}},
|
||||
author = {Agterberg, Joshua and Lubberts, Zachary and Arroyo, Jesús},
|
||||
date = {2025-04},
|
||||
journaltitle = {Journal of the American Statistical Association},
|
||||
volume = {0},
|
||||
number = {0},
|
||||
pages = {1--15},
|
||||
publisher = {ASA Website},
|
||||
issn = {0162-1459},
|
||||
doi = {10.1080/01621459.2025.2516201},
|
||||
url = {https://doi.org/10.1080/01621459.2025.2516201},
|
||||
urldate = {2025-09-19},
|
||||
abstract = {Modern network datasets are often composed of multiple layers, resulting in collections of networks over the same set of vertices but with potentially different connectivity patterns on each network. These data require models and methods that are flexible enough to capture local and global differences across the networks while at the same time being parsimonious and tractable to yield computationally efficient and theoretically sound solutions that are capable of aggregating information across the networks. This paper considers the multilayer degree-corrected stochastic blockmodel, where a collection of networks shares the same community structure, but degree corrections and block connection probability matrices are permitted to be different. We establish the identifiability of this model and propose a spectral clustering algorithm. Our theoretical results demonstrate that the misclustering error rate of the algorithm improves exponentially with multiple network realizations, even in the presence of significant layer heterogeneity. Simulation studies show that this approach improves on existing multilayer community detection methods in this challenging regime. Furthermore, in a case study of US airport data through January 2016 – September 2021, we find that this methodology identifies meaningful community structure and trends in airport popularity influenced by pandemic impacts on travel. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T13:53:26.541Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/C82RAE8U/Agterberg et al. - Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels.pdf}
|
||||
}
|
||||
|
||||
@online{anakokDisentanglingStructureEcological2022,
|
||||
title = {Disentangling the Structure of Ecological Bipartite Networks from Observation Processes},
|
||||
author = {Anakok, Emre and Barbillon, Pierre and Fontaine, Colin and Thebault, Elisa},
|
||||
|
|
@ -51,6 +71,24 @@
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/RPETTM3Z/1990 - Appendix.pdf}
|
||||
}
|
||||
|
||||
@article{arroyoInferenceMultipleHeterogeneous2021,
|
||||
title = {Inference for {{Multiple Heterogeneous Networks}} with a {{Common Invariant Subspace}}},
|
||||
author = {Arroyo, Jesús and Athreya, Avanti and Cape, Joshua and Chen, Guodong and Priebe, Carey E. and Vogelstein, Joshua T.},
|
||||
date = {2021},
|
||||
journaltitle = {Journal of Machine Learning Research},
|
||||
volume = {22},
|
||||
number = {142},
|
||||
pages = {1--49},
|
||||
issn = {1533-7928},
|
||||
url = {http://jmlr.org/papers/v22/19-558.html},
|
||||
urldate = {2025-09-19},
|
||||
abstract = {The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices---the multiple adjacency spectral embedding---leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T14:02:30.452Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/DAGYGPA3/Arroyo et al. - 2021 - Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/RPJ3SJGW/19-558.html}
|
||||
}
|
||||
|
||||
@article{aubertModelbasedBiclusteringOverdispersed2021,
|
||||
title = {Model-Based Biclustering for Overdispersed Count Data with Application in Microbial Ecology},
|
||||
author = {Aubert, Julie and Schbath, Sophie and Robin, Stéphane},
|
||||
|
|
@ -234,6 +272,29 @@
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9XBNTTWB/Chabert-Liddell et al. - 2024 - Learning common structures in a collection of netw.pdf}
|
||||
}
|
||||
|
||||
@article{chabert-liddellStochasticBlockModel2021,
|
||||
title = {A {{Stochastic Block Model Approach}} for the {{Analysis}} of {{Multilevel Networks}}: An {{Application}} to the {{Sociology}} of {{Organizations}}},
|
||||
shorttitle = {A {{Stochastic Block Model Approach}} for the {{Analysis}} of {{Multilevel Networks}}},
|
||||
author = {Chabert-Liddell, Saint-Clair and Barbillon, Pierre and Donnet, Sophie and Lazega, Emmanuel},
|
||||
date = {2021-06},
|
||||
journaltitle = {Computational Statistics \& Data Analysis},
|
||||
shortjournal = {Computational Statistics \& Data Analysis},
|
||||
volume = {158},
|
||||
eprint = {1910.10512},
|
||||
eprinttype = {arXiv},
|
||||
eprintclass = {stat},
|
||||
pages = {107179},
|
||||
issn = {01679473},
|
||||
doi = {10.1016/j.csda.2021.107179},
|
||||
url = {http://arxiv.org/abs/1910.10512},
|
||||
urldate = {2025-09-26},
|
||||
abstract = {A multilevel network is defined as the junction of two interaction networks, one level representing the interactions between individuals and the other the interactions between organizations. The levels are linked by an affiliation relationship, each individual belonging to a unique organization. A new Stochastic Block Model is proposed as a unified probalistic framework tailored for multilevel networks. This model contains latent blocks accounting for heterogeneity in the patterns of connection within each level and introducing dependencies between the levels. The sought connection patterns are not specified a priori which makes this approach flexible. Variational methods are used for the model inference and an Integrated Classified Likelihood criterion is developed for choosing the number of blocks and also for deciding whether the two levels are dependent or not. A comprehensive simulation study exhibits the benefit of considering this approach, illustrates the robustness of the clustering and highlights the reliability of the criterion used for model selection. This approach is applied on a sociological dataset collected during a television program trade fair, the inter-organizational level being the economic network between companies and the inter-individual level being the informal network between their representatives. It brings a synthetic representation of the two networks unraveling their intertwined structure and confirms the coopetition at stake.},
|
||||
keywords = {/unread,Computer Science - Social and Information Networks,Statistics - Applications,Statistics - Methodology},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-26T08:52:07.522Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/6XFLLTKL/Chabert-Liddell et al. - 2021 - A Stochastic Block Model Approach for the Analysis of Multilevel Networks an Application to the Soc.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/8DAMTVD8/1910.html}
|
||||
}
|
||||
|
||||
@software{chiquetSbmStochasticBlockmodels2024,
|
||||
title = {Sbm: {{Stochastic Blockmodels}}},
|
||||
shorttitle = {Sbm},
|
||||
|
|
@ -245,6 +306,27 @@
|
|||
version = {0.4.7}
|
||||
}
|
||||
|
||||
@article{clausetHierarchicalStructurePrediction2008,
|
||||
title = {Hierarchical Structure and the Prediction of Missing Links in Networks},
|
||||
author = {Clauset, Aaron and Moore, Cristopher and Newman, M. E. J.},
|
||||
date = {2008-05},
|
||||
journaltitle = {Nature},
|
||||
volume = {453},
|
||||
number = {7191},
|
||||
pages = {98--101},
|
||||
publisher = {Nature Publishing Group},
|
||||
issn = {1476-4687},
|
||||
doi = {10.1038/nature06830},
|
||||
url = {https://www.nature.com/articles/nature06830},
|
||||
urldate = {2025-09-19},
|
||||
abstract = {Networks are now a ubiquitous tool for representing the structure of complex systems, including the Internet, social networks, food webs, and protein and genetic networks. Unfortunately, the data describing these networks are in many cases incomplete or biased. A new study provides a general technique to divide network vertices into groups and sub-groups. Revealing such underlying hierarchies makes it possible to predict missing links from partial data with higher accuracy than previous methods.},
|
||||
langid = {english},
|
||||
keywords = {/unread,Humanities and Social Sciences,multidisciplinary,Science},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T12:33:29.962Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/Y4FR2F3U/Clauset et al. - 2008 - Hierarchical structure and the prediction of missing links in networks.pdf}
|
||||
}
|
||||
|
||||
@incollection{ClusteringLargeApplications1990,
|
||||
title = {Clustering {{Large Applications}} ({{Program CLARA}})},
|
||||
booktitle = {Finding {{Groups}} in {{Data}}},
|
||||
|
|
@ -387,6 +469,50 @@ Read\_Status\_Date: 2025-05-27T16:20:41.925Z}
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/89ZXBJQP/10.1111@gcb.15474.pdf.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/IVR6RGG7/Doré et al. - 2021 - Relative effects of anthropogenic pressures, clima.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/WSJ4DV98/gcb.html}
|
||||
}
|
||||
|
||||
@article{dormannIndicesGraphsNull2009,
|
||||
title = {Indices, {{Graphs}} and {{Null Models}}: {{Analyzing Bipartite Ecological Networks}}},
|
||||
shorttitle = {Indices, {{Graphs}} and {{Null Models}}},
|
||||
author = {Dormann, Carsten F. and Frund, Jochen and Bluthgen, Nico and Gruber, Bernd},
|
||||
date = {2009-02-27},
|
||||
journaltitle = {The Open Ecology Journal},
|
||||
shortjournal = {TOECOLJ},
|
||||
volume = {2},
|
||||
number = {1},
|
||||
pages = {7--24},
|
||||
issn = {18742130},
|
||||
doi = {10.2174/1874213000902010007},
|
||||
url = {http://benthamopen.com/ABSTRACT/TOECOLJ-2-1-7},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Many analyses of ecological networks in recent years have introduced new indices to describe network properties. As a consequence, tens of indices are available to address similar questions, differing in specific detail, sensitivity in detecting the property in question, and robustness with respect to network size and sampling intensity. Furthermore, some indices merely reflect the number of species participating in a network, but not their interrelationship, requiring a null model approach. Here we introduce a new, free software calculating a large spectrum of network indices, visualizing bipartite networks and generating null models. We use this tool to explore the sensitivity of 26 network indices to network dimensions, sampling intensity and singleton observations. Based on observed data, we investigate the interrelationship of these indices, and show that they are highly correlated, and heavily influenced by network dimensions and connectance. Finally, we re-evaluate five common hypotheses about network properties, comparing 19 pollination networks with three differently complex null models: 1. The number of links per species (“degree”) follow (truncated) power law distributions. 2. Generalist pollinators interact with specialist plants, and vice versa (dependence asymmetry). 3. Ecological networks are nested. 4. Pollinators display complementarity, owing to specialization within the network. 5. Plant-pollinator networks are more robust to extinction than random networks. Our results indicate that while some hypotheses hold up against our null models, others are to a large extent understandable on the basis of network size, rather than ecological interrelationships. In particular, null model pattern of dependence asymmetry and robustness to extinction are opposite to what current network paradigms suggest. Our analysis, and the tools we provide, enables ecologists to readily contrast their findings with null model expectations for many different questions, thus separating statistical inevitability from ecological process.},
|
||||
langid = {english},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T13:47:50.536Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/IQWYN2GQ/Dormann et al. - 2009 - Indices, Graphs and Null Models Analyzing Bipartite Ecological Networks.pdf}
|
||||
}
|
||||
|
||||
@article{elleUsePollinationNetworks2012,
|
||||
title = {The Use of Pollination Networks in Conservation{\textsuperscript{1}} {{This}} Article Is Part of a {{Special Issue}} Entitled “{{Pollination}} Biology Research in {{Canada}}: {{Perspectives}} on a Mutualism at Different Scales”.},
|
||||
shorttitle = {The Use of Pollination Networks in Conservation{\textsuperscript{1}} {{This}} Article Is Part of a {{Special Issue}} Entitled “{{Pollination}} Biology Research in {{Canada}}},
|
||||
author = {Elle, Elizabeth and Elwell, Sherri L. and Gielens, Grahame A.},
|
||||
date = {2012-07},
|
||||
journaltitle = {Botany},
|
||||
shortjournal = {Botany},
|
||||
volume = {90},
|
||||
number = {7},
|
||||
pages = {525--534},
|
||||
issn = {1916-2790, 1916-2804},
|
||||
doi = {10.1139/b11-111},
|
||||
url = {http://www.nrcresearchpress.com/doi/10.1139/b11-111},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Recent concern about declines in pollinating insects highlights the need for better understanding of plant–pollinator interactions. One promising approach at the community scale is network analysis, which allows actual interactions to be assessed, unlike biodiversity surveys, which only identify the potentially interacting organisms. We highlight useful network properties for conservation research and examples of their use in the study of rare species, invasive species, responses of communities to climate change, and habitat loss and restoration. We suggest that nestedness, degree, and interaction strength asymmetry are the most useful network properties for applied research on plant–pollinator interactions, but also highlight practical concerns regarding their measurement. We encourage the adoption of a network approach when an understanding of function within communities, rather than simple community composition, is useful for management.},
|
||||
langid = {english},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T15:39:13.968Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/EVUPKFLQ/Elle et al. - 2012 - The use of pollination networks in conservation1 This article is part of a Special Issue.pdf}
|
||||
}
|
||||
|
||||
@article{erdosRandomGraphs1959,
|
||||
title = {On Random Graphs. {{I}}.},
|
||||
author = {Erdős, P. and Rényi, A.},
|
||||
|
|
@ -565,6 +691,19 @@ Read\_Status\_Date: 2025-05-14T20:18:00.025Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/PPHP33Z9/Govaert et Nadif - 2010 - Latent Block Model for Contingency Table.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/UT8TARCX/govaert2010.pdf.pdf}
|
||||
}
|
||||
|
||||
@software{GrossSBMColSBM2025,
|
||||
title = {{{GrossSBM}}/{{colSBM}}},
|
||||
date = {2025-07-16T10:36:01Z},
|
||||
origdate = {2021-12-30T11:52:03Z},
|
||||
url = {https://github.com/GrossSBM/colSBM},
|
||||
urldate = {2025-09-25},
|
||||
abstract = {R package for the joint stochastic blockmodeling of collection of networks},
|
||||
organization = {GroßBM},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-25T11:59:36.376Z}
|
||||
}
|
||||
|
||||
@online{hamiltonInductiveRepresentationLearning2018,
|
||||
title = {Inductive {{Representation Learning}} on {{Large Graphs}}},
|
||||
author = {Hamilton, William L. and Ying, Rex and Leskovec, Jure},
|
||||
|
|
@ -655,6 +794,30 @@ Read\_Status\_Date: 2025-07-01T13:24:50.464Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZPWRCT6C/1990 - Introduction.pdf}
|
||||
}
|
||||
|
||||
@article{karrerStochasticBlockmodelsCommunity2011,
|
||||
title = {Stochastic Blockmodels and Community Structure in Networks},
|
||||
author = {Karrer, Brian and Newman, M. E. J.},
|
||||
date = {2011-01-21},
|
||||
journaltitle = {Physical Review E},
|
||||
shortjournal = {Phys. Rev. E},
|
||||
volume = {83},
|
||||
number = {1},
|
||||
eprint = {1008.3926},
|
||||
eprinttype = {arXiv},
|
||||
eprintclass = {physics},
|
||||
pages = {016107},
|
||||
issn = {1539-3755, 1550-2376},
|
||||
doi = {10.1103/PhysRevE.83.016107},
|
||||
url = {http://arxiv.org/abs/1008.3926},
|
||||
urldate = {2025-09-26},
|
||||
abstract = {Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.},
|
||||
langid = {english},
|
||||
keywords = {/unread,Computer Science - Social and Information Networks,Condensed Matter - Statistical Mechanics,Physics - Data Analysis Statistics and Probability,Physics - Physics and Society},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-26T08:18:22.155Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/HDPN46QR/Karrer et Newman - 2011 - Stochastic blockmodels and community structure in networks.pdf}
|
||||
}
|
||||
|
||||
@article{kaszewska-gilasGlobalStudiesHostParasite2021,
|
||||
title = {Global {{Studies}} of the {{Host-Parasite Relationships}} between {{Ectoparasitic Mites}} of the {{Family Syringophilidae}} and {{Birds}} of the {{Order Columbiformes}}},
|
||||
author = {Kaszewska-Gilas, Katarzyna and Kosicki, Jakub Ziemowit and Hromada, Martin and Skoracki, Maciej},
|
||||
|
|
@ -841,6 +1004,26 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
version = {1.1.5}
|
||||
}
|
||||
|
||||
@article{llopis-belenguerSensitivityBipartiteNetwork2023,
|
||||
title = {Sensitivity of Bipartite Network Analyses to Incomplete Sampling and Taxonomic Uncertainty},
|
||||
author = {Llopis-Belenguer, Cristina and Balbuena, Juan Antonio and Blasco-Costa, Isabel and Karvonen, Anssi and Sarabeev, Volodimir and Jokela, Jukka},
|
||||
date = {2023-04},
|
||||
journaltitle = {Ecology},
|
||||
volume = {104},
|
||||
number = {4},
|
||||
pages = {e3974},
|
||||
publisher = {John Wiley \& Sons, Ltd},
|
||||
issn = {0012-9658},
|
||||
doi = {10.1002/ecy.3974},
|
||||
url = {https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/ecy.3974},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Abstract Bipartite network analysis is a powerful tool to study the processes structuring interactions in ecological communities. In applying the method, it is assumed that the sampled interactions provide an accurate representation of the actual community. However, acquiring a representative sample may be difficult as not all species are equally abundant or easily identifiable. Two potential sampling issues can compromise the conclusions of bipartite network analyses: failure to capture the full range of interactions (sampling completeness) and use of a taxonomic level higher than species to evaluate the network (taxonomic resolution). We asked how commonly used descriptors of bipartite antagonistic communities (modularity, nestedness, connectance, and specialization [H2?]) are affected by reduced host sampling completeness, parasite taxonomic resolution, and their crossed effect, as they are likely to co-occur. We used a quantitative niche model to generate weighted bipartite networks that resembled natural host?parasite communities. The descriptors were more sensitive to uncertainty in parasite taxonomic resolution than to host sampling completeness. When only 10\% of parasite taxonomic resolution was retained, modularity and specialization decreased by \textasciitilde 76\% and \textasciitilde 12\%, respectively, and nestedness and connectance increased by \textasciitilde 114\% and \textasciitilde 345\% respectively. The loss of taxonomic resolution led to a wide range of possible communities, which made it difficult to predict its effects on a given network. With regards to host sampling completeness, standardized nestedness, connectance, and specialization were robust, whereas modularity was sensitive (\textasciitilde 30\% decrease). The combination of both sampling issues had an additive effect on modularity. In communities with low effort for both sampling issues (50\%?10\% of sampling completeness and taxonomic resolution), estimators of modularity, and nestedness could not be distinguished from those of random assemblages. Thus, the categorical description of communities with low sampling effort (e.g., if a community is modular or not) should be done with caution. We recommend evaluating both sampling completeness and taxonomic certainty when conducting bipartite network analyses. Care should also be exercised when using nonrobust descriptors (the four descriptors for parasite taxonomic resolution; modularity for host sampling completeness) when sampling issues are likely to affect a dataset.},
|
||||
keywords = {/unread,bipartite networks,host–parasite interactions,sampling completeness,sampling issues,taxonomic resolution},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T14:55:12.700Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/XC8KSS5S/Llopis-Belenguer et al. - 2023 - Sensitivity of bipartite network analyses to incomplete sampling and taxonomic uncertainty.pdf}
|
||||
}
|
||||
|
||||
@article{maeldoreMaelDorePollination_networksScripts2020,
|
||||
title = {{{MaelDore}}/{{Pollination}}\_networks: {{R}} Scripts for {{Doré}} et al., 2020 - {{Relative}} Effects of Anthropogenic Pressures, Climate, and Sampling Design on the Structure of Pollination Networks at the Global Scale},
|
||||
shorttitle = {{{MaelDore}}/{{Pollination}}\_networks},
|
||||
|
|
@ -854,6 +1037,26 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
keywords = {data,plant-pollinator}
|
||||
}
|
||||
|
||||
@article{matiasStatisticalClusteringTemporal2017,
|
||||
title = {Statistical {{Clustering}} of {{Temporal Networks Through}} a {{Dynamic Stochastic Block Model}}},
|
||||
author = {Matias, Catherine and Miele, Vincent},
|
||||
date = {2017-09-01},
|
||||
journaltitle = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
|
||||
shortjournal = {J. R. Stat. Soc. Ser. B. Stat. Methodol.},
|
||||
volume = {79},
|
||||
number = {4},
|
||||
pages = {1119--1141},
|
||||
issn = {1369-7412},
|
||||
doi = {10.1111/rssb.12200},
|
||||
url = {https://doi.org/10.1111/rssb.12200},
|
||||
urldate = {2025-09-19},
|
||||
abstract = {Statistical node clustering in discrete time dynamic networks is an emerging field that raises many challenges. Here, we explore statistical properties and frequentist inference in a model that combines a stochastic block model for its static part with independent Markov chains for the evolution of the nodes groups through time. We model binary data as well as weighted dynamic random graphs (with discrete or continuous edges values). Our approach, motivated by the importance of controlling for label switching issues across the different time steps, focuses on detecting groups characterized by a stable within-group connectivity behaviour. We study identifiability of the model parameters and propose an inference procedure based on a variational expectation–maximization algorithm as well as a model selection criterion to select the number of groups. We carefully discuss our initialization strategy which plays an important role in the method and we compare our procedure with existing procedures on synthetic data sets. We also illustrate our approach on dynamic contact networks: one of encounters between high school students and two others on animal interactions. An implementation of the method is available as an R package called dynsbm.},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T14:13:35.038Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/DHG8XRWV/Matias et Miele - 2017 - Statistical Clustering of Temporal Networks Through a Dynamic Stochastic Block Model.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/4YPTDS8N/rssb.html}
|
||||
}
|
||||
|
||||
@article{michalska-smithTellingEcologicalNetworks2019,
|
||||
title = {Telling Ecological Networks Apart by Their Structure: {{A}} Computational Challenge},
|
||||
shorttitle = {Telling Ecological Networks Apart by Their Structure},
|
||||
|
|
@ -891,6 +1094,22 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/UU46RRAJ/1990 - Monothetic Analysis (Program MONA).pdf}
|
||||
}
|
||||
|
||||
@inproceedings{ngSpectralClusteringAnalysis2001,
|
||||
title = {On {{Spectral Clustering}}: {{Analysis}} and an Algorithm},
|
||||
shorttitle = {On {{Spectral Clustering}}},
|
||||
booktitle = {Advances in {{Neural Information Processing Systems}}},
|
||||
author = {Ng, Andrew and Jordan, Michael and Weiss, Yair},
|
||||
date = {2001},
|
||||
volume = {14},
|
||||
publisher = {MIT Press},
|
||||
url = {https://papers.nips.cc/paper_files/paper/2001/hash/801272ee79cfde7fa5960571fee36b9b-Abstract.html},
|
||||
urldate = {2025-10-08},
|
||||
abstract = {Despite many empirical successes of spectral clustering methods(cid:173) algorithms that cluster points using eigenvectors of matrices de(cid:173) rived from the data- there are several unresolved issues. First, there are a wide variety of algorithms that use the eigenvectors in slightly different ways. Second, many of these algorithms have no proof that they will actually compute a reasonable clustering. In this paper, we present a simple spectral clustering algorithm that can be implemented using a few lines of Matlab. Using tools from matrix perturbation theory, we analyze the algorithm, and give conditions under which it can be expected to do well. We also show surprisingly good experimental results on a number of challenging clustering problems.},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-10-08T12:47:34.666Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/SEWUWLHD/Ng et al. - 2001 - On Spectral Clustering Analysis and an algorithm.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/XRY4F47W/nips01-spectral.pdf}
|
||||
}
|
||||
|
||||
@book{ottawafield-naturalistsclubCanadianFieldnaturalist1976,
|
||||
title = {The {{Canadian}} Field-Naturalist},
|
||||
author = {Ottawa Field-Naturalists' Club and Club, Ottawa Field-Naturalists'},
|
||||
|
|
@ -958,6 +1177,26 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YP235FFE/Pavlopoulos et al. - 2018 - Bipartite graphs in systems biology and medicine a survey of methods and applications.pdf}
|
||||
}
|
||||
|
||||
@article{pavlovicMultisubjectStochasticBlockmodels2020,
|
||||
title = {Multi-Subject {{Stochastic Blockmodels}} for Adaptive Analysis of Individual Differences in Human Brain Network Cluster Structure},
|
||||
author = {Pavlović, Dragana M. and Guillaume, Bryan R. L. and Towlson, Emma K. and Kuek, Nicole M. Y. and Afyouni, Soroosh and Vértes, Petra E. and Yeo, B. T. Thomas and Bullmore, Edward T. and Nichols, Thomas E.},
|
||||
date = {2020-10-15},
|
||||
journaltitle = {NeuroImage},
|
||||
shortjournal = {Neuroimage},
|
||||
volume = {220},
|
||||
eprint = {32058004},
|
||||
eprinttype = {pubmed},
|
||||
pages = {116611},
|
||||
issn = {1095-9572},
|
||||
doi = {10.1016/j.neuroimage.2020.116611},
|
||||
abstract = {There is considerable interest in elucidating the cluster structure of brain networks in terms of modules, blocks or clusters of similar nodes. However, it is currently challenging to handle data on multiple subjects since most of the existing methods are applicable only on a subject-by-subject basis or for analysis of an average group network. The main limitation of per-subject models is that there is no obvious way to combine the results for group comparisons, and of group-averaged models that they do not reflect the variability between subjects. Here, we propose two new extensions of the classical Stochastic Blockmodel (SBM) that use a mixture model to estimate blocks or clusters of connected nodes, combined with a regression model to capture the effects of subject-level covariates on individual differences in cluster structure. The proposed Multi-Subject Stochastic Blockmodels (MS-SBMs) can flexibly account for between-subject variability in terms of homogeneous or heterogeneous covariate effects on connectivity using subject demographics such as age or diagnostic status. Using synthetic data, representing a range of block sizes and cluster structures, we investigate the accuracy of the estimated MS-SBM parameters as well as the validity of inference procedures based on the Wald, likelihood ratio and permutation tests. We show that the proposed multi-subject SBMs recover the true cluster structure of synthetic networks more accurately and adaptively than standard methods for modular decomposition (i.e. the Fast Louvain and Newman Spectral algorithms). Permutation tests of MS-SBM parameters were more robustly valid for statistical inference and Type I error control than tests based on standard asymptotic assumptions. Applied to analysis of multi-subject resting-state fMRI networks (13 healthy volunteers; 12 people with schizophrenia; n=268 brain regions), we show that Heterogeneous Stochastic Blockmodel (Het-SBM) identifies a range of network topologies simultaneously, including modular and core structures.},
|
||||
langid = {english},
|
||||
keywords = {/unread,Brain,Community detection,Computer Simulation,Connectome,Default Mode Network,Firth estimation,Humans,Individuality,Integrated classification likelihood criterion,Likelihood ratio,Magnetic Resonance Imaging,Mixture models,Models Neurological,Models Statistical,Modularity,Multi-subject network analysis,Nerve Net,Network analysis,Permutation test,Schizophrenia,Stochastic block model,Stochastic blockmodel,Variational approximation,Wald test},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T14:04:30.797Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ENZLGULT/Pavlović et al. - 2020 - Multi-subject Stochastic Blockmodels for adaptive analysis of individual differences in human brain.pdf}
|
||||
}
|
||||
|
||||
@online{peixotoBayesianStochasticBlockmodeling2023,
|
||||
title = {Bayesian Stochastic Blockmodeling},
|
||||
author = {Peixoto, Tiago P.},
|
||||
|
|
@ -993,6 +1232,26 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9C6TE4FS/Peixoto - 2014 - Efficient Monte Carlo and greedy heuristic for the inference of stochastic block models.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/Q4LRS4GA/PhysRevE.89.html}
|
||||
}
|
||||
|
||||
@article{peixotoHierarchicalBlockStructures2014,
|
||||
title = {Hierarchical {{Block Structures}} and {{High-Resolution Model Selection}} in {{Large Networks}}},
|
||||
author = {Peixoto, Tiago P.},
|
||||
date = {2014-03-24},
|
||||
journaltitle = {Physical Review X},
|
||||
shortjournal = {Phys. Rev. X},
|
||||
volume = {4},
|
||||
number = {1},
|
||||
pages = {011047},
|
||||
issn = {2160-3308},
|
||||
doi = {10.1103/PhysRevX.4.011047},
|
||||
url = {https://link.aps.org/doi/10.1103/PhysRevX.4.011047},
|
||||
urldate = {2025-09-26},
|
||||
langid = {english},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-26T08:27:38.586Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/R58ZQK8J/Peixoto - 2014 - Hierarchical Block Structures and High-Resolution Model Selection in Large Networks.pdf}
|
||||
}
|
||||
|
||||
@article{pichonTellingMutualisticAntagonistic2024,
|
||||
title = {Telling Mutualistic and Antagonistic Ecological Networks Apart by Learning Their Multiscale Structure},
|
||||
author = {Pichon, Benoît and Le Goff, Rémy and Morlon, Hélène and Perez-Lamarque, Benoît},
|
||||
|
|
@ -1060,6 +1319,26 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YKP9NU4L/1990 - References.pdf}
|
||||
}
|
||||
|
||||
@article{rivera-hutinelEffectsSamplingCompleteness2012,
|
||||
title = {Effects of Sampling Completeness on the Structure of Plant–Pollinator Networks},
|
||||
author = {Rivera-Hutinel, A. and Bustamante, R. O. and Marín, V. H. and Medel, R.},
|
||||
date = {2012},
|
||||
journaltitle = {Ecology},
|
||||
volume = {93},
|
||||
number = {7},
|
||||
pages = {1593--1603},
|
||||
issn = {1939-9170},
|
||||
doi = {10.1890/11-1803.1},
|
||||
url = {https://onlinelibrary.wiley.com/doi/abs/10.1890/11-1803.1},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Plant–animal interaction networks provide important information on community organization. One of the most critical assumptions of network analysis is that the observed interaction patterns constitute an adequate sample of the set of interactions present in plant–animal communities. In spite of its importance, few studies have evaluated this assumption, and in consequence, there is no consensus on the sensitivity of network metrics to sampling methodological shortcomings. In this study we examined how variation in sampling completeness influences the estimation of six network metrics frequently used in the literature (connectance, nestedness, modularity, robustness to species loss, path length, and centralization). We analyzed data of 186 flowering plants and 336 pollinator species in 10 networks from a forest-fragmented system in central Chile. Using species-based accumulation curves, we estimated the deviation of network metrics in undersampled communities with respect to exhaustively sampled communities and the effect of network size and sampling evenness on network metrics. Our results indicate that: (1) most metrics were affected by sampling completeness but differed in their sensitivity to sampling effort; (2) nestedness, modularity, and robustness to species loss were less influenced by insufficient sampling than connectance, path length, and centralization; (3) robustness was mildly influenced by sampling evenness. These results caution studies that summarize information from databases with high, or unknown, heterogeneity in sampling effort per species and should stimulate researchers to report sampling intensity to standardize its effects in the search for broad patterns in plant–pollinator networks.},
|
||||
langid = {english},
|
||||
keywords = {/unread,accumulation curves,Chile,Clench model,ecological networks,Los Ruiles National Reserve,network size,plant–pollinator network metrics,sampling completeness,sampling effort,sampling evenness},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T15:47:50.369Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/PJ7NWTIV/Rivera-Hutinel et al. - 2012 - Effects of sampling completeness on the structure of plant–pollinator networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/37GFBFZH/11-1803.html}
|
||||
}
|
||||
|
||||
@article{sanderWhatCanInteraction2015,
|
||||
title = {What {{Can Interaction Webs Tell Us About Species Roles}}?},
|
||||
author = {Sander, Elizabeth L. and Wootton, J. Timothy and Allesina, Stefano},
|
||||
|
|
@ -1096,6 +1375,28 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/L8CXITBN/Schwarz - 1978 - Estimating the Dimension of a Model.pdf}
|
||||
}
|
||||
|
||||
@article{sheykhaliRobustnessExtinctionPlasticity2020,
|
||||
title = {Robustness to Extinction and Plasticity Derived from Mutualistic Bipartite Ecological Networks},
|
||||
author = {Sheykhali, Somaye and Fernández-Gracia, Juan and Traveset, Anna and Ziegler, Maren and Voolstra, Christian R. and Duarte, Carlos M. and Eguíluz, Víctor M.},
|
||||
date = {2020-06-17},
|
||||
journaltitle = {Scientific Reports},
|
||||
shortjournal = {Sci Rep},
|
||||
volume = {10},
|
||||
number = {1},
|
||||
pages = {9783},
|
||||
publisher = {Nature Publishing Group},
|
||||
issn = {2045-2322},
|
||||
doi = {10.1038/s41598-020-66131-5},
|
||||
url = {https://www.nature.com/articles/s41598-020-66131-5},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Understanding the response of ecological networks to perturbations and disruptive events is needed to anticipate the biodiversity loss and extinction cascades. Here, we study how network plasticity reshapes the topology of mutualistic networks in response to species loss. We analyze more than one hundred empirical mutualistic networks and considered random and targeted removal as mechanisms of species extinction. Network plasticity is modeled as either random rewiring, as the most parsimonious approach, or resource affinity-driven rewiring, as a proxy for encoding the phylogenetic similarity and functional redundancy among species. This redundancy should be positively correlated with the robustness of an ecosystem, as functions can be taken by other species once one of them is extinct. We show that effective modularity, i.e. the ability of an ecosystem to adapt or restructure, increases with increasing numbers of extinctions, and with decreasing the replacement probability. Importantly, modularity is mostly affected by the extinction rather than by rewiring mechanisms. These changes in community structure are reflected in the robustness and stability due to their positive correlation with modularity. Resource affinity-driven rewiring offers an increase of modularity, robustness, and stability which could be an evolutionary favored mechanism to prevent a cascade of co-extinctions.},
|
||||
langid = {english},
|
||||
keywords = {/unread,Biodiversity,Complex networks,Ecological networks},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T14:43:11.755Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/YVCBAJYI/Sheykhali et al. - 2020 - Robustness to extinction and plasticity derived from mutualistic bipartite ecological networks.pdf}
|
||||
}
|
||||
|
||||
@article{simmonsMotifsBipartiteEcological2019,
|
||||
title = {Motifs in Bipartite Ecological Networks: Uncovering Indirect Interactions},
|
||||
shorttitle = {Motifs in Bipartite Ecological Networks},
|
||||
|
|
@ -1242,14 +1543,59 @@ Read\_Status\_Date: 2025-05-26T11:42:27.939Z},
|
|||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/P6MW22VI/Trøjelsgaard et Olesen - 2013 - Macroecology of pollination networks.pdf;/home/louis/snap/zotero-snap/common/Zotero/storage/JKFY35H2/j.1466-8238.2012.00777.html}
|
||||
}
|
||||
|
||||
@online{WebLifeEcological,
|
||||
@online{WebLifeEcological2022,
|
||||
title = {Web of {{Life}}: Ecological Networks Database},
|
||||
date = {2022-07},
|
||||
url = {https://www.web-of-life.es/map.php},
|
||||
urldate = {2023-06-17},
|
||||
keywords = {networks,site},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/9WZE8QLQ/map.html}
|
||||
}
|
||||
|
||||
@article{willsMetricsGraphComparison2020a,
|
||||
title = {Metrics for Graph Comparison: {{A}} Practitioner’s Guide},
|
||||
shorttitle = {Metrics for Graph Comparison},
|
||||
author = {Wills, Peter and Meyer, François G.},
|
||||
date = {2020-02-12},
|
||||
journaltitle = {PLOS ONE},
|
||||
shortjournal = {PLOS ONE},
|
||||
volume = {15},
|
||||
number = {2},
|
||||
pages = {e0228728},
|
||||
publisher = {Public Library of Science},
|
||||
issn = {1932-6203},
|
||||
doi = {10.1371/journal.pone.0228728},
|
||||
url = {https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0228728},
|
||||
urldate = {2025-09-18},
|
||||
abstract = {Comparison of graph structure is a ubiquitous task in data analysis and machine learning, with diverse applications in fields such as neuroscience, cyber security, social network analysis, and bioinformatics, among others. Discovery and comparison of structures such as modular communities, rich clubs, hubs, and trees yield insight into the generative mechanisms and functional properties of the graph. Often, two graphs are compared via a pairwise distance measure, with a small distance indicating structural similarity and vice versa. Common choices include spectral distances and distances based on node affinities. However, there has of yet been no comparative study of the efficacy of these distance measures in discerning between common graph topologies at different structural scales. In this work, we compare commonly used graph metrics and distance measures, and demonstrate their ability to discern between common topological features found in both random graph models and real world networks. We put forward a multi-scale picture of graph structure wherein we study the effect of global and local structures on changes in distance measures. We make recommendations on the applicability of different distance measures to the analysis of empirical graph data based on this multi-scale view. Finally, we introduce the Python library NetComp that implements the graph distances used in this work.},
|
||||
langid = {english},
|
||||
keywords = {/unread,Community structure,Connectomics,Distance measurement,Eigenvalues,Mathematical models,Neural networks,Random graphs,Schools},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-18T14:47:55.289Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/ZBX75DTN/Wills et Meyer - 2020 - Metrics for graph comparison A practitioner’s guide.pdf}
|
||||
}
|
||||
|
||||
@article{xuUnderstandingGraphEmbedding2021,
|
||||
title = {Understanding {{Graph Embedding Methods}} and {{Their Applications}}},
|
||||
author = {Xu, Mengjia},
|
||||
date = {2021-01},
|
||||
journaltitle = {SIAM Review},
|
||||
shortjournal = {SIAM Rev.},
|
||||
volume = {63},
|
||||
number = {4},
|
||||
pages = {825--853},
|
||||
issn = {0036-1445, 1095-7200},
|
||||
doi = {10.1137/20M1386062},
|
||||
url = {https://epubs.siam.org/doi/10.1137/20M1386062},
|
||||
urldate = {2025-09-19},
|
||||
abstract = {Graph analytics can lead to better quantitative understanding and control of complex networks, but traditional methods suffer from the high computational cost and excessive memory requirements associated with the high-dimensionality and heterogeneous characteristics of industrial size networks. Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense, and continuous vector spaces, preserving maximally the graph structure properties. Another type of emerging graph embedding employs Gaussian distribution--based graph embedding with important uncertainty estimation. The main goal of graph embedding methods is to pack every node's properties into a vector with a smaller dimension; hence, node similarity in the original complex irregular spaces can be easily quantified in the embedded vector spaces using standard metrics. The nonlinear and highly informative graph embeddings generated in the latent space can be conveniently used to address different downstream graph analytics tasks (e.g., node classification, link prediction, community detection, visualization, etc.). In this review, we present some fundamental concepts in graph analytics and graph embedding methods, focusing in particular on random walk--based and neural network--based methods. We also discuss the emerging deep learning--based dynamic graph embedding methods. We highlight the distinct advantages of graph embedding methods in four diverse applications, and we present implementation details and references to open-source software as well as available databases in the supplementary material to help interested readers start their exploration into graph analytics.},
|
||||
langid = {english},
|
||||
keywords = {/unread},
|
||||
annotation = {Read\_Status: New\\
|
||||
Read\_Status\_Date: 2025-09-19T12:36:55.370Z},
|
||||
file = {/home/louis/snap/zotero-snap/common/Zotero/storage/WLYYXUGK/Xu - 2021 - Understanding Graph Embedding Methods and Their Applications.pdf}
|
||||
}
|
||||
|
||||
@online{yumpu.comInsectPollinatorsMer,
|
||||
title = {Insect Pollinators of the {{Mer Bleue}} Peat Bog of {{Ottawa}} - {{Biodiversity}} ...},
|
||||
author = {Yumpu.com},
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue