2023
81.
Belkhouja, Taha; Yan, Yan; Doppa, Janardhan Rao
Dynamic Time Warping Based Adversarial Framework for Time-Series Domain Journal Article
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 45, no. 6, pp. 7353–7366, 2023, ISSN: 1939-3539, (Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence).
Abstract | Links | BibTeX | Tags: AI
@article{belkhouja_dynamic_2023,
title = {Dynamic Time Warping Based Adversarial Framework for Time-Series Domain},
author = {Belkhouja, Taha and Yan, Yan and Doppa, Janardhan Rao},
url = {https://ieeexplore.ieee.org/document/9970291},
doi = {10.1109/TPAMI.2022.3224754},
issn = {1939-3539},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {45},
number = {6},
pages = {7353\textendash7366},
abstract = {Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.},
note = {Conference Name: IEEE Transactions on Pattern Analysis and Machine Intelligence},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Despite the rapid progress on research in adversarial robustness of deep neural networks (DNNs), there is little principled work for the time-series domain. Since time-series data arises in diverse applications including mobile health, finance, and smart grid, it is important to verify and improve the robustness of DNNs for the time-series domain. In this paper, we propose a novel framework for the time-series domain referred as Dynamic Time Warping for Adversarial Robustness (DTW-AR) using the dynamic time warping measure. Theoretical and empirical evidence is provided to demonstrate the effectiveness of DTW over the standard euclidean distance metric employed in prior methods for the image domain. We develop a principled algorithm justified by theoretical analysis to efficiently create diverse adversarial examples using random alignment paths. Experiments on diverse real-world benchmarks show the effectiveness of DTW-AR to fool DNNs for time-series data and to improve their robustness using adversarial training.
82.
Mishra, Ritwick; Heavey, Jack; Kaur, Gursharn; Adiga, Abhijin; Vullikanti, Anil
Reconstructing an Epidemic Outbreak Using Steiner Connectivity Journal Article
In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, no. 10, pp. 11613–11620, 2023, ISSN: 2374-3468, (Number: 10).
Abstract | Links | BibTeX | Tags: AI
@article{mishra_reconstructing_2023,
title = {Reconstructing an Epidemic Outbreak Using Steiner Connectivity},
author = {Mishra, Ritwick and Heavey, Jack and Kaur, Gursharn and Adiga, Abhijin and Vullikanti, Anil},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26372},
doi = {10.1609/aaai.v37i10.26372},
issn = {2374-3468},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
volume = {37},
number = {10},
pages = {11613\textendash11620},
abstract = {Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.},
note = {Number: 10},
keywords = {AI},
pubstate = {published},
tppubtype = {article}
}
Only a subset of infections is actually observed in an outbreak, due to multiple reasons such as asymptomatic cases and under-reporting. Therefore, reconstructing an epidemic cascade given some observed cases is an important step in responding to such an outbreak. A maximum likelihood solution to this problem ( referred to as CascadeMLE ) can be shown to be a variation of the classical Steiner subgraph problem, which connects a subset of observed infections. In contrast to prior works on epidemic reconstruction, which consider the standard Steiner tree objective, we show that a solution to CascadeMLE, based on the actual MLE objective, has a very different structure. We design a logarithmic approximation algorithm for CascadeMLE, and evaluate it on multiple synthetic and social contact networks, including a contact network constructed for a hospital. Our algorithm has significantly better performance compared to a prior baseline.
83.
Ghosh, Subhankar; Shi, Yuanjie; Belkhouja, Taha; Yan, Yan; Doppa, Jana; Jones, Brian
Probabilistically Robust Conformal Prediction Proceedings Article
In: 2023.
Abstract | Links | BibTeX | Tags: AI
@inproceedings{ghosh_probabilistically_2023,
title = {Probabilistically Robust Conformal Prediction},
author = {Ghosh, Subhankar and Shi, Yuanjie and Belkhouja, Taha and Yan, Yan and Doppa, Jana and Jones, Brian},
url = {https://openreview.net/forum?id=4xI4oaqIs2},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
abstract = {Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
Conformal prediction (CP) is a framework to quantify uncertainty of machine learning classifiers including deep neural networks. Given a testing example and a trained classifier, CP produces a prediction set of candidate labels with a user-specified coverage (i.e., true class label is contained with high probability). Almost all the existing work on CP assumes clean testing data and there is not much known about the robustness of CP algorithms w.r.t natural/adversarial perturbations to testing examples. This paper studies the problem of probabilistically robust conformal prediction (PRCP) which ensures robustness to most perturbations around clean input examples. PRCP generalizes the standard CP (cannot handle perturbations) and adversarially robust CP (ensures robustness w.r.t worst-case perturbations) to achieve better trade-offs between nominal performance and robustness. We propose a novel adaptive PRCP (aPRCP) algorithm to achieve probabilistically robust coverage. The key idea behind aPRCP is to determine two parallel thresholds, one for data samples and another one for the perturbations on data (aka "quantile-of-quantile'' design). We provide theoretical analysis to show that aPRCP algorithm achieves robust coverage. Our experiments on CIFAR-10, CIFAR-100, and ImageNet datasets using deep neural networks demonstrate that aPRCP achieves better trade-offs than state-of-the-art CP and adversarially robust CP algorithms.
84.
He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic
Bidirectional alignment for domain adaptive detection with transformers Proceedings Article
In: Proceedings of International Conference on Computer Vision, 2023.
Abstract | Links | BibTeX | Tags: Pruning
@inproceedings{noauthor_bidirectional_nodate,
title = {Bidirectional alignment for domain adaptive detection with transformers},
author = {He, Liqiang; Wei Wang, Albert Chen; Min Sun; Cheng-hao Kuo; Sinisa Todorovic},
url = {https://www.amazon.science/publications/bidirectional-alignment-for-domain-adaptive-detection-with-transformers},
year = {2023},
date = {2023-03-08},
urldate = {2023-03-08},
journal = {Amazon Science},
publisher = {Proceedings of International Conference on Computer Vision},
abstract = {We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…},
keywords = {Pruning},
pubstate = {published},
tppubtype = {inproceedings}
}
We propose a Bidirectional Alignment for domain adaptive Detection with Transformers (BiADT) to improve cross domain object detection performance. Existing adversarial learning based methods use gradient reverse layer (GRL) to reduce the domain gap between the source and target domains in feature…
85.
Aseem Saxena; Paola Pesantez-Cabrera; Rohan Ballapragada; Markus Keller; Alan Fern
Multi-Task Learning for Budbreak Prediction Workshop
2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS), arXiv, 2023, (arXiv:2301.01815 [cs]).
Abstract | Links | BibTeX | Tags: Cold Hardiness, Computer Science
@workshop{saxena_multi-task_2023,
title = {Multi-Task Learning for Budbreak Prediction},
author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Markus Keller and Alan Fern},
url = {https://openreview.net/pdf?id=kvGm8DJ-cM},
doi = {10.48550/arXiv.2301.01815},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)},
publisher = {arXiv},
abstract = {Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.},
note = {arXiv:2301.01815 [cs]},
keywords = {Cold Hardiness, Computer Science },
pubstate = {published},
tppubtype = {workshop}
}
Grapevine budbreak is a key phenological stage of seasonal development, which serves as a signal for the onset of active growth. This is also when grape plants are most vulnerable to damage from freezing temperatures. Hence, it is important for winegrowers to anticipate the day of budbreak occurrence to protect their vineyards from late spring frost events. This work investigates deep learning for budbreak prediction using data collected for multiple grape cultivars. While some cultivars have over 30 seasons of data others have as little as 4 seasons, which can adversely impact prediction accuracy. To address this issue, we investigate multi-task learning, which combines data across all cultivars to make predictions for individual cultivars. Our main result shows that several variants of multi-task learning are all able to significantly improve prediction accuracy compared to learning for each cultivar independently.
86.
Welankar Sejal; Paola Pesantez-Cabrera; Bala Krishnamoorthy; Ananth Kalyanaraman
Persistent Homology to Study Cold Hardiness of Grape Cultivars Workshop
2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS), arXiv, 2023.
Links | BibTeX | Tags: Cold Hardiness, Computer Science , Topological Data Analysis
@workshop{welankar_grape_2023,
title = {Persistent Homology to Study Cold Hardiness of Grape Cultivars},
author = {Welankar Sejal and Paola Pesantez-Cabrera and Bala Krishnamoorthy and Ananth Kalyanaraman},
url = {https://openreview.net/pdf?id=PPoe26Ys-j},
doi = {https://doi.org/10.48550/arXiv.2302.05600},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {2nd AAAI Workshop on AI for Agriculture and Food Systems (AIAFS)},
publisher = {arXiv},
keywords = {Cold Hardiness, Computer Science , Topological Data Analysis},
pubstate = {published},
tppubtype = {workshop}
}
87.
Aseem Saxena; Paola Pesantez-Cabrera; Rohan Ballapragada; Kin-Ho Lam; Markus Keller; Alan Fern
Grape Cold Hardiness Prediction via Multi-Task Learning Conference
Association for the Advancement of Artificial Intelligence (AAAI) 2023, 2023.
Abstract | Links | BibTeX | Tags: Cold Hardiness, Computer and Information Sciences, Machine Learning
@conference{saxena_aaai2023,
title = {Grape Cold Hardiness Prediction via Multi-Task Learning},
author = {Aseem Saxena and Paola Pesantez-Cabrera and Rohan Ballapragada and Kin-Ho Lam and Markus Keller and Alan Fern},
url = {https://ojs.aaai.org/index.php/AAAI/article/view/26865},
doi = { https://doi.org/10.1609/aaai.v37i13.26865},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Association for the Advancement of Artificial Intelligence (AAAI) 2023},
abstract = {Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures such as sprinklers, heaters, and wind machines when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning
approaches and show that the highest-performing approach is able to significantly improve overlearning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.},
keywords = {Cold Hardiness, Computer and Information Sciences, Machine Learning},
pubstate = {published},
tppubtype = {conference}
}
Cold temperatures during fall and spring have the potential to cause frost damage to grapevines and other fruit plants, which can significantly decrease harvest yields. To help prevent these losses, farmers deploy expensive frost mitigation measures such as sprinklers, heaters, and wind machines when they judge that damage may occur. This judgment, however, is challenging because the cold hardiness of plants changes throughout the dormancy period and it is difficult to directly measure. This has led scientists to develop cold hardiness prediction models that can be tuned to different grape cultivars based on laborious field measurement data. In this paper, we study whether deep-learning models can improve cold hardiness prediction for grapes based on data that has been collected over a 30-year time period. A key challenge is that the amount of data per cultivar is highly variable, with some cultivars having only a small amount. For this purpose, we investigate the use of multi-task learning to leverage data across cultivars in order to improve prediction performance for individual cultivars. We evaluate a number of multi-task learning
approaches and show that the highest-performing approach is able to significantly improve overlearning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.
approaches and show that the highest-performing approach is able to significantly improve overlearning for single cultivars and outperforms the current state-of-the-art scientific model for most cultivars.
88.
Abatzoglou, John T.; McEvoy, Daniel J.; Nauslar, Nicholas J.; Hegewisch, Katherine C.; Huntington, Justin L.
Downscaled subseasonal fire danger forecast skill across the contiguous United States Journal Article
In: Atmospheric Science Letters, vol. 24, no. 8, pp. e1165, 2023, ISSN: 1530-261X, (_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165).
Abstract | Links | BibTeX | Tags: Fallow, Water
@article{abatzoglou_downscaled_2023,
title = {Downscaled subseasonal fire danger forecast skill across the contiguous United States},
author = {Abatzoglou, John T. and McEvoy, Daniel J. and Nauslar, Nicholas J. and Hegewisch, Katherine C. and Huntington, Justin L.},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/asl.1165},
doi = {10.1002/asl.1165},
issn = {1530-261X},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {Atmospheric Science Letters},
volume = {24},
number = {8},
pages = {e1165},
abstract = {The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.},
note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/asl.1165},
keywords = {Fallow, Water},
pubstate = {published},
tppubtype = {article}
}
The increasing complexity and impacts of fire seasons in the United States have prompted efforts to improve early warning systems for wildland fire management. Outlooks of potential fire activity at lead-times of several weeks can help in wildland fire resource allocation as well as complement short-term meteorological forecasts for ongoing fire events. Here, we describe an experimental system for developing downscaled ensemble-based subseasonal forecasts for the contiguous US using NCEP's operational Climate Forecast System version 2 model. These forecasts are used to calculate forecasted fire danger indices from the United States (US) National Fire Danger Rating System in addition to forecasts of evaporative demand. We further illustrate the skill of subseasonal forecasts on weekly timescales using hindcasts from 2011 to 2021. Results show that while forecast skill degrades with time, statistically significant week 3 correlative skill was found for 76% and 30% of the contiguous US for Energy Release Component and evaporative demand, respectively. These results highlight the potential value of experimental subseasonal forecasts in complementing existing information streams in weekly-to-monthly fire business decision making for suppression-based decisions and geographic reallocation of resources during the fire season, as well for proactive fire management actions outside of the core fire season.
89.
Bertucci, Donald; Hamid, Md Montaser; Anand, Yashwanthi; Ruangrotsakun, Anita; Tabatabai, Delyar; Perez, Melissa; Kahng, Minsuk
DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps Journal Article
In: IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, pp. 320–330, 2023, ISSN: 1941-0506, (Conference Name: IEEE Transactions on Visualization and Computer Graphics).
Abstract | Links | BibTeX | Tags: AI, Humans
@article{bertucci_dendromap_2023,
title = {DendroMap: Visual Exploration of Large-Scale Image Datasets for Machine Learning with Treemaps},
author = {Bertucci, Donald and Hamid, Md Montaser and Anand, Yashwanthi and Ruangrotsakun, Anita and Tabatabai, Delyar and Perez, Melissa and Kahng, Minsuk},
url = {https://ieeexplore.ieee.org/document/9904448},
doi = {10.1109/TVCG.2022.3209425},
issn = {1941-0506},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
journal = {IEEE Transactions on Visualization and Computer Graphics},
volume = {29},
number = {1},
pages = {320\textendash330},
abstract = {In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.},
note = {Conference Name: IEEE Transactions on Visualization and Computer Graphics},
keywords = {AI, Humans},
pubstate = {published},
tppubtype = {article}
}
In this paper, we present DendroMap, a novel approach to interactively exploring large-scale image datasets for machine learning (ML). ML practitioners often explore image datasets by generating a grid of images or projecting high-dimensional representations of images into 2-D using dimensionality reduction techniques (e.g., t-SNE). However, neither approach effectively scales to large datasets because images are ineffectively organized and interactions are insufficiently supported. To address these challenges, we develop DendroMap by adapting Treemaps, a well-known visualization technique. DendroMap effectively organizes images by extracting hierarchical cluster structures from high-dimensional representations of images. It enables users to make sense of the overall distributions of datasets and interactively zoom into specific areas of interests at multiple levels of abstraction. Our case studies with widely-used image datasets for deep learning demonstrate that users can discover insights about datasets and trained models by examining the diversity of images, identifying underperforming subgroups, and analyzing classification errors. We conducted a user study that evaluates the effectiveness of DendroMap in grouping and searching tasks by comparing it with a gridified version of t-SNE and found that participants preferred DendroMap. DendroMap is available at https://div-lab.github.io/dendromap/.
90.
Mishra, Ritwick; Eubank, Stephen; Nath, Madhurima; Amundsen, Manu; Adiga, Abhijin
Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks Proceedings Article
In: Cherifi, Hocine; Mantegna, Rosario Nunzio; Rocha, Luis M.; Cherifi, Chantal; Micciche, Salvatore (Ed.): Complex Networks and Their Applications XI, pp. 271–282, Springer International Publishing, Cham, 2023, ISBN: 978-3-031-21131-7.
Abstract | Links | BibTeX | Tags: AI
@inproceedings{mishra_community_2023,
title = {Community Detection Using Moore-Shannon Network Reliability: Application to Food Networks},
author = {Mishra, Ritwick and Eubank, Stephen and Nath, Madhurima and Amundsen, Manu and Adiga, Abhijin},
editor = {Cherifi, Hocine and Mantegna, Rosario Nunzio and Rocha, Luis M. and Cherifi, Chantal and Micciche, Salvatore},
url = {https://link.springer.com/chapter/10.1007/978-3-031-21131-7_21},
doi = {10.1007/978-3-031-21131-7_21},
isbn = {978-3-031-21131-7},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Complex Networks and Their Applications XI},
pages = {271\textendash282},
publisher = {Springer International Publishing},
address = {Cham},
series = {Studies in Computational Intelligence},
abstract = {Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.},
keywords = {AI},
pubstate = {published},
tppubtype = {inproceedings}
}
Community detection in networks is extensively studied from a structural perspective, but very few works characterize communities with respect to dynamics on networks. We propose a generic framework based on Moore-Shannon network reliability for defining and discovering communities with respect to a variety of dynamical processes. This approach extracts communities in directed edge-weighted networks which satisfy strong connectivity properties as well as strong mutual influence between pairs of nodes through the dynamical process. We apply this framework to food networks. We compare our results with modularity-based approach, and analyze community structure across commodities, evolution over time, and with regard to dynamical system properties.
