Papers | Parallel Computing
2024
Alessia Antelmi, Massimo Torquati, Giacomo Corridori, Daniele Gregori, Francesco Polzella, Gianmarco Spinatelli, Marco Aldinucci
Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework Journal Article
In: The Journal of Supercomputing, vol. 80, no. 11, pp. 15799-15833, 2024, ISSN: 1573-0484.
Abstract | Links | BibTeX | Tags: analytics, icsc
@article{Antelmi_JSUPE_2024,
title = {Analyzing FOSS license usage in publicly available software at scale via the SWH-analytics framework},
author = {Alessia Antelmi and Massimo Torquati and Giacomo Corridori and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},
url = {https://doi.org/10.1007/s11227-024-06069-x},
doi = {10.1007/s11227-024-06069-x},
issn = {1573-0484},
year = {2024},
date = {2024-07-01},
journal = {The Journal of Supercomputing},
volume = {80},
number = {11},
pages = {15799-15833},
abstract = {The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {article}
}
The Software Heritage (SWH) dataset represents an invaluable source of open-source code as it aims to collect, preserve, and share all publicly available software in source code form ever produced by humankind. Although designed to archive deduplicated small files thanks to the use of a Merkle tree as the underlying data structure, querying the SWH dataset presents challenges due to the nature of these structures, which organize content based on hash values rather than any locality principle. The magnitude of the repository, coupled with the resource-intensive nature of the download process, highlights the need for specialized infrastructure and computational resources to effectively handle and study the extensive dataset housed within SWH. Currently, there is a lack of infrastructures specifically tailored for running analytics on the SWH dataset, leaving users to handle these issues manually. To address these challenges, we implemented the SWH-Analytics (SWHA) framework, a development environment that transparently runs custom analytic applications on publicly available software data preserved over time by SWH. Specifically, this work shows how SWHA can be effectively exploited to study usage patterns of free and open-source software licenses, highlighting the need to improve license literacy among developers.
Alessia Antelmi, Vincenzo Offertucci, Maria Angela Pellegrino
KGSnap! in Practice: a Block-based Programming Environment for Enabling Knowledge Graph Literacy Proceedings Article
In: Proceedings of the 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024), CEUR-WS.org, 2024.
Abstract | Links | BibTeX | Tags: analytics
@inproceedings{Antelmi_ISWCWrks_24,
title = {KGSnap! in Practice: a Block-based Programming Environment for Enabling Knowledge Graph Literacy},
author = {Alessia Antelmi and Vincenzo Offertucci and Maria Angela Pellegrino},
url = {https://ceur-ws.org/Vol-3773/paper6.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 9th International Workshop on the Visualization and Interaction for Ontologies, Linked Data and Knowledge Graphs co-located with the 23rd International Semantic Web Conference (ISWC 2024)},
volume = {3773},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {The growing availability of (linked) open data requires lay users to master how to deal with data effectively, yet SPARQL presents a barrier to leveraging data represented as knowledge graphs. As the block programming paradigm has been successfully used to teach programming skills, we demonstrate how to use KGSnap!, an extension of the block-based programming environment Snap!, to foster knowledge graph literacy among individuals lacking expertise in query languages. This work mainly focuses on the visualization and interaction aspects of KGSnap!, a visual SPARQL query builder, when experienced by users without expertise in the Semantic Web technologies. The reported experience is discussed as a learning-by-doing protocol aimed at facilitating the reproducibility and transparency of the performed evaluation. KGSnap! ease of use has been verified by 14 Snap! experts and 24 high-school learners. The findings indicate that lay users perceived it as a promising approach to acquaint themselves with knowledge graphs.},
keywords = {analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
The growing availability of (linked) open data requires lay users to master how to deal with data effectively, yet SPARQL presents a barrier to leveraging data represented as knowledge graphs. As the block programming paradigm has been successfully used to teach programming skills, we demonstrate how to use KGSnap!, an extension of the block-based programming environment Snap!, to foster knowledge graph literacy among individuals lacking expertise in query languages. This work mainly focuses on the visualization and interaction aspects of KGSnap!, a visual SPARQL query builder, when experienced by users without expertise in the Semantic Web technologies. The reported experience is discussed as a learning-by-doing protocol aimed at facilitating the reproducibility and transparency of the performed evaluation. KGSnap! ease of use has been verified by 14 Snap! experts and 24 high-school learners. The findings indicate that lay users perceived it as a promising approach to acquaint themselves with knowledge graphs.
Daniele De Vinco, Andrea Tranquillo, Alessia Antelmi, Carmine Spagnuolo, Vittorio Scarano
High-Performance Computation on a Rust-based distributed ABM engine Proceedings Article
In: Antelmi, Alessia, Carlini, Emanuele, Dazzi, Patrizio (Ed.): Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024, CEUR-WS.org, 2024.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_BigHPC_24,
title = {High-Performance Computation on a Rust-based distributed ABM engine},
author = {Daniele De Vinco and Andrea Tranquillo and Alessia Antelmi and Carmine Spagnuolo and Vittorio Scarano},
editor = {Alessia Antelmi and Emanuele Carlini and Patrizio Dazzi},
url = {https://ceur-ws.org/Vol-3785/paper124.pdf},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of BigHPC2024: Special Track on Big Data and High-Performance Computing, co-located with the 3textsuperscriptrd Italian Conference on Big Data and Data Science, ITADATA2024},
volume = {3785},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {An agent-based model (ABM) is a computational model for simulating autonomous agents' actions and interactions to understand a system's behavior and what governs its outcomes. When the data or number of agents grow or multiple runs are necessary, agent-based simulations are generally computationally costly. Therefore, adopting different computing paradigms, such as the distributed one, is essential to manage long-running simulations. The main problem with this approach is finding a way to distribute and balance the simulation field so that the agents can move from one machine to another with the least amount of synchronization overhead. Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the sequential version.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
An agent-based model (ABM) is a computational model for simulating autonomous agents' actions and interactions to understand a system's behavior and what governs its outcomes. When the data or number of agents grow or multiple runs are necessary, agent-based simulations are generally computationally costly. Therefore, adopting different computing paradigms, such as the distributed one, is essential to manage long-running simulations. The main problem with this approach is finding a way to distribute and balance the simulation field so that the agents can move from one machine to another with the least amount of synchronization overhead. Based on our experiences, we present a Rust-based ABM engine capable of distributing models on high-performance computing resources, gaining remarkable speedup against the sequential version.
Sunwoo Kim, Soo Yong Lee, Yue Gao, Alessia Antelmi, Mirko Polato, Kijung Shin
A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide Proceedings Article
In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 6534–6544, Association for Computing Machinery, Barcelona, Spain, 2024, ISBN: 9798400704901.
Abstract | Links | BibTeX | Tags: ai, analytics, icsc
@inproceedings{Antelmi_KDD_2024,
title = {A Survey on Hypergraph Neural Networks: An In-Depth and Step-By-Step Guide},
author = {Sunwoo Kim and Soo Yong Lee and Yue Gao and Alessia Antelmi and Mirko Polato and Kijung Shin},
url = {https://doi.org/10.1145/3637528.3671457},
doi = {10.1145/3637528.3671457},
isbn = {9798400704901},
year = {2024},
date = {2024-01-01},
booktitle = {Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining},
pages = {6534–6544},
publisher = {Association for Computing Machinery},
address = {Barcelona, Spain},
series = {KDD '24},
abstract = {Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.},
keywords = {ai, analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Higher-order interactions (HOIs) are ubiquitous in real-world complex systems and applications. Investigation of deep learning for HOIs, thus, has become a valuable agenda for the data mining and machine learning communities. As networks of HOIs are expressed mathematically as hypergraphs, hypergraph neural networks (HNNs) have emerged as a powerful tool for representation learning on hypergraphs. Given the emerging trend, we present the first survey dedicated to HNNs, with an in-depth and step-by-step guide. Broadly, the present survey overviews HNN architectures, training strategies, and applications. First, we break existing HNNs down into four design components: (i) input features, (ii) input structures, (iii) message-passing schemes, and (iv) training strategies. Second, we examine how HNNs address and learn HOIs with each of their components. Third, we overview the recent applications of HNNs in recommendation, bioinformatics and medical science, time series analysis, and computer vision. Lastly, we conclude with a discussion on limitations and future directions.
Daniele De Vinco, Alessia Antelmi, Carmine Spagnuolo, Luca Maria Aiello
Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs Proceedings Article
In: Companion Publication of the 16th ACM Web Science Conference, pp. 3–4, Association for Computing Machinery, Stuttgart, Germany, 2024, ISBN: 9798400704536.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_WebSci_2024,
title = {Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs},
author = {Daniele De Vinco and Alessia Antelmi and Carmine Spagnuolo and Luca Maria Aiello},
url = {https://doi.org/10.1145/3630744.3658418},
doi = {10.1145/3630744.3658418},
isbn = {9798400704536},
year = {2024},
date = {2024-01-01},
booktitle = {Companion Publication of the 16th ACM Web Science Conference},
pages = {3–4},
publisher = {Association for Computing Machinery},
address = {Stuttgart, Germany},
series = {Websci Companion '24},
abstract = {Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-the-art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-the-art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.
Alessia Antelmi, Daniele De Vinco, Carmine Spagnuolo
HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection Proceedings Article
In: Dewar, Megan, Kamiński, Bogumił, Kaszyński, Daniel, Kraiński, Łukasz, Prałat, Paweł, Théberge, François, Wrzosek, Małgorzata (Ed.): Modelling and Mining Networks, pp. 159–173, Springer Nature Switzerland, Cham, 2024, ISBN: 978-3-031-59205-8.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_WAW_2024,
title = {HypergraphRepository: A Community-Driven and Interactive Hypernetwork Data Collection},
author = {Alessia Antelmi and Daniele De Vinco and Carmine Spagnuolo},
editor = {Megan Dewar and Bogumił Kamiński and Daniel Kaszyński and Łukasz Kraiński and Paweł Prałat and François Théberge and Małgorzata Wrzosek},
doi = {10.1007/978-3-031-59205-8_11},
isbn = {978-3-031-59205-8},
year = {2024},
date = {2024-01-01},
booktitle = {Modelling and Mining Networks},
pages = {159–173},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {Hypergraph research has been thriving over the past few years, with a growing interest in a plethora of domains. Despite this remarkable surge, the lack of a comprehensive platform for searching and downloading diverse and well-curated datasets poses a significant obstacle to the continued advancement of the field. This absence hinders the ability of researchers and practitioners to validate and benchmark their hypergraph algorithms and models effectively.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Hypergraph research has been thriving over the past few years, with a growing interest in a plethora of domains. Despite this remarkable surge, the lack of a comprehensive platform for searching and downloading diverse and well-curated datasets poses a significant obstacle to the continued advancement of the field. This absence hinders the ability of researchers and practitioners to validate and benchmark their hypergraph algorithms and models effectively.
Alessia Antelmi, Pasquale Caramante, Gennaro Cordasco, Giuseppe D'Ambrosio, Daniele De Vinco, Francesco Foglia, Luca Postiglione, Carmine Spagnuolo
Reliable and Efficient Agent-Based Modeling and Simulation Journal Article
In: Journal of Artificial Societies and Social Simulation, vol. 27, no. 2, pp. 4, 2024, ISSN: 1460-7425.
Abstract | Links | BibTeX | Tags: analytics, icsc
@article{Antelmi_JASSS_2024,
title = {Reliable and Efficient Agent-Based Modeling and Simulation},
author = {Alessia Antelmi and Pasquale Caramante and Gennaro Cordasco and Giuseppe D'Ambrosio and Daniele De Vinco and Francesco Foglia and Luca Postiglione and Carmine Spagnuolo},
url = {http://jasss.soc.surrey.ac.uk/27/2/4.html},
doi = {10.18564/jasss.5300},
issn = {1460-7425},
year = {2024},
date = {2024-01-01},
journal = {Journal of Artificial Societies and Social Simulation},
volume = {27},
number = {2},
pages = {4},
abstract = {Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {article}
}
Agent-based models represent a primary methodology to untangle and study complex systems. Over the last decade, the need for more elaborate computing-demanding models gave rise to many frameworks and tools to run ABM simulations. Current state-of-the-art ABM tools either focus on ease of use, performance, or a trade-off between these two elements. Still, efficiency-oriented solutions (required for both large and small-scale simulations) are vulnerable to memory flaws which could invalidate the experiment results. This work aims to merge efficiency, reliability, and safeness under an innovative ABM software framework based on the Rust programming language. Our framework, krABMaga, is an open-source library that offers a high-level environment by exploiting metaprogramming and expandable visualization features. We equipped our library with a dynamic simulation monitoring system and model exploration and optimization capabilities over parallel, distributed, and cloud architectures. After having presented the overall architecture and functionalities of krABMaga, we discuss a performance comparison of our framework against the mostly adopted ABM software and the scalability potential of our simulation engine on a model calibration experiment running over an AWS EC2 virtual cluster machine. All code and examples models are available on GitHub.
2023
Alessia Antelmi, Gennaro Cordasco, Mirko Polato, Vittorio Scarano, Carmine Spagnuolo, Dingqi Yang
A Survey on Hypergraph Representation Learning Journal Article
In: ACM Comput. Surv., 2023, ISSN: 0360-0300.
Abstract | Links | BibTeX | Tags: analytics
@article{Antelmi_CSUR_23,
title = {A Survey on Hypergraph Representation Learning},
author = {Alessia Antelmi and Gennaro Cordasco and Mirko Polato and Vittorio Scarano and Carmine Spagnuolo and Dingqi Yang},
url = {https://doi.org/10.1145/3605776},
doi = {10.1145/3605776},
issn = {0360-0300},
year = {2023},
date = {2023-06-01},
journal = {ACM Comput. Surv.},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
abstract = {Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most commonly nodes - of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.},
keywords = {analytics},
pubstate = {published},
tppubtype = {article}
}
Hypergraphs have attracted increasing attention in recent years thanks to their flexibility in naturally modeling a broad range of systems where high-order relationships exist among their interacting parts. This survey reviews the newly born hypergraph representation learning problem, whose goal is to learn a function to project objects - most commonly nodes - of an input hyper-network into a latent space such that both the structural and relational properties of the network can be encoded and preserved. We provide a thorough overview of existing literature and offer a new taxonomy of hypergraph embedding methods by identifying three main families of techniques, i.e., spectral, proximity-preserving, and (deep) neural networks. For each family, we describe its characteristics and our insights in a single yet flexible framework and then discuss the peculiarities of individual methods, as well as their pros and cons. We then review the main tasks, datasets, and settings in which hypergraph embeddings are typically used. We finally identify and discuss open challenges that would inspire further research in this field.
Alessia Antelmi, Luca La Cava, Arianna Pera
Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques Proceedings Article
In: The 12th International Conference on Complex Networks and their Applications-Book of Abstracts, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_CNA1_2023,
title = {Tell Me Who You Are and I Will Predict Your Vulnerability to Political Persuasion Techniques},
author = {Alessia Antelmi and Luca La Cava and Arianna Pera},
url = {https://iris.unito.it/bitstream/2318/1949370/1/_CNA__23__Personality_vs_propaganda.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
abstract = {Given the evolving role of social media in political communication and the strategic use of these platforms by politicians to shape public opinion, research has commonly focused on investigating computational propaganda as a means for automated information diffusion. Focusing on a less explored yet promising line, we aim to assess political persuasion in digital contexts by introducing a computational framework that combines Natural Language Processing and Network Science methods to investigate the linkage between persuasion techniques on social media and personality traits of online political audiences. Our final goal is to enhance public awareness of political tactics and encourage critical thinking in response to the online spread of political information.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
Given the evolving role of social media in political communication and the strategic use of these platforms by politicians to shape public opinion, research has commonly focused on investigating computational propaganda as a means for automated information diffusion. Focusing on a less explored yet promising line, we aim to assess political persuasion in digital contexts by introducing a computational framework that combines Natural Language Processing and Network Science methods to investigate the linkage between persuasion techniques on social media and personality traits of online political audiences. Our final goal is to enhance public awareness of political tactics and encourage critical thinking in response to the online spread of political information.
Alessia Antelmi, Luca La Cava, Arianna Pera
Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse Proceedings Article
In: The 12th International Conference on Complex Networks and their Applications-Book of Abstracts, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_CNA_2023,
title = {Finding Hidden Swingers in the 2022 Italian Elections Twitter Discourse},
author = {Alessia Antelmi and Luca La Cava and Arianna Pera},
url = {https://iris.unito.it/bitstream/2318/1949354/1/_CNA__23__TweetYourMind.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {The 12th International Conference on Complex Networks and their Applications-Book of Abstracts},
abstract = {The volume of the Italian online political discourse on social media has recently increased, but the coverage level does not compare with other Countries such as the US. Nonetheless, researchers focused on studying polarization and homophily with respect to political debates or investigating the role of populism in online engagement. In this research landscape, the analysis of political preference shifts through social media remains to be explored. We aim to bridge this gap by examining the Twitter discourse during the 2022 Italian general elections, with a specific emphasis on political "swingers". In particular, our findings indicate a stable political discourse in Italy, yet they also uncover a growing presence of political swingers willing to shift their support to significantly different factions.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
The volume of the Italian online political discourse on social media has recently increased, but the coverage level does not compare with other Countries such as the US. Nonetheless, researchers focused on studying polarization and homophily with respect to political debates or investigating the role of populism in online engagement. In this research landscape, the analysis of political preference shifts through social media remains to be explored. We aim to bridge this gap by examining the Twitter discourse during the 2022 Italian general elections, with a specific emphasis on political "swingers". In particular, our findings indicate a stable political discourse in Italy, yet they also uncover a growing presence of political swingers willing to shift their support to significantly different factions.
Alessia Antelmi, Massimo Torquati, Daniele Gregori, Francesco Polzella, Gianmarco Spinatelli, Marco Aldinucci
The SWH-Analytics Framework Proceedings Article
In: Bena, Nicola, Martino, Beniamino Di, Maratea, Antonio, Sperduti, Alessandro, Nardo, Emanuel Di, Ciaramella, Angelo, Montella, Raffaele, Ardagna, Claudio A. (Ed.): Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023, CEUR-WS.org, 2023.
Abstract | Links | BibTeX | Tags: admire, analytics, icsc
@inproceedings{Antelmi_ITADATA_2023,
title = {The SWH-Analytics Framework},
author = {Alessia Antelmi and Massimo Torquati and Daniele Gregori and Francesco Polzella and Gianmarco Spinatelli and Marco Aldinucci},
editor = {Nicola Bena and Beniamino Di Martino and Antonio Maratea and Alessandro Sperduti and Emanuel Di Nardo and Angelo Ciaramella and Raffaele Montella and Claudio A. Ardagna},
url = {https://ceur-ws.org/Vol-3606/paper76.pdf},
year = {2023},
date = {2023-01-01},
booktitle = {Proceedings of the 2nd Italian Conference on Big Data and Data Science (ITADATA 2023), Naples, Italy, September 11-13, 2023},
volume = {3606},
publisher = {CEUR-WS.org},
series = {CEUR Workshop Proceedings},
abstract = {The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.},
keywords = {admire, analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
The Software Heritage (SWH) dataset serves as a vast repository for open-source code, with the ambitious goal of preserving all publicly available open-source projects. Despite being designed to effectively archive project files, its size of nearly 1 petabyte presents challenges in efficiently supporting Big Data MapReduce or AI systems. To address this disparity and enable seamless custom analytics on the SWH dataset, we present the SWH-Analytics (SWHA) architecture. This development environment quickly and transparently runs custom analytic applications on open-source software data preserved over time by SWH.
Alessia Antelmi, Daniele De Vinco, Gennaro Cordasco, Carmine Spagnuolo
Towards Unraveling Developers Communities in Stack Overflow and Reddit Proceedings Article
In: International Conference on Computational Social Science 2023, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_IC2S2_2023,
title = {Towards Unraveling Developers Communities in Stack Overflow and Reddit},
author = {Alessia Antelmi and Daniele De Vinco and Gennaro Cordasco and Carmine Spagnuolo},
url = {https://openreview.net/forum?id=WP5ZaAFP19},
year = {2023},
date = {2023-01-01},
booktitle = {International Conference on Computational Social Science 2023},
abstract = {This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these QA platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
This work investigates the developers' behavior and community formation around the twenty most popular programming languages. We examined two consecutive years of programming-related questions from Stack Overflow and Reddit, performing a longitudinal study on users' posting activity and their high-order interaction patterns abstracted via hypergraphs. Our analysis highlighted crucial differences in how these QA platforms are utilized by their users. In line with previous literature, it emphasized the constant decline of Stack Overflow in favor of more community-friendly platforms, such as Reddit, which has been growing rapidly lately.
Alessia Antelmi
Engagement in Open Data Workshops: The dark side of remote settings Proceedings Article
In: Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference, Springer International Publishing, Cham, 2023.
Abstract | Links | BibTeX | Tags: analytics, icsc
@inproceedings{Antelmi_TEL4FC_2023,
title = {Engagement in Open Data Workshops: The dark side of remote settings},
author = {Alessia Antelmi},
url = {https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33},
year = {2023},
date = {2023-01-01},
booktitle = {Methodologies and Intelligent Systems for Technology Enhanced Learning, 12th International Conference},
publisher = {Springer International Publishing},
address = {Cham},
abstract = {The increasing availability of Open Data gives birth to a fertile field for interested stakeholders to create value out of them; however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open Data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open Data and improve users' skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open Data. This article describes an ongoing activity and details its organization, reports preliminary results on learners' engagement, and discusses both challenges of the remote setting as well as promising learning outcomes.},
keywords = {analytics, icsc},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing availability of Open Data gives birth to a fertile field for interested stakeholders to create value out of them; however, limited technical expertise and poor awareness are crucial barriers to their exploitation. Because of these reasons, there is an urge for learners to acquire data and information literacy competencies, which are essential for 21st-century skills, and become familiar with available Open Data sources and their potential uses. To promote the dialogue around activities to boost recognition of Open Data and improve users' skills to work with them, we proposed a series of workshops to introduce Italian high school learners to searching for, authoring, and building effective communication based on Open Data. This article describes an ongoing activity and details its organization, reports preliminary results on learners' engagement, and discusses both challenges of the remote setting as well as promising learning outcomes.
2022
Yujin Zhu, Zilong Zhao, Robert Birke, Lydia Y. Chen
Permutation-Invariant Tabular Data Synthesis Proceedings Article
In: Tsumoto, Shusaku, Ohsawa, Yukio, Chen, Lei, Poel, Dirk Van, Hu, Xiaohua, Motomura, Yoichi, Takagi, Takuya, Wu, Lingfei, Xie, Ying, Abe, Akihiro, Raghavan, Vijay (Ed.): IEEE International Conference on Big Data (Big Data), pp. 5855–5864, IEEE, 2022.
Abstract | Links | BibTeX | Tags: analytics
@inproceedings{bigdata-zhu22,
title = {Permutation-Invariant Tabular Data Synthesis},
author = {Yujin Zhu and Zilong Zhao and Robert Birke and Lydia Y. Chen},
editor = {Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and Dirk Van Poel and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan},
url = {https://datacloud.di.unito.it/index.php/s/b6z3moNLxnNiCxz},
doi = {10.1109/BigData55660.2022.10020639},
year = {2022},
date = {2022-12-01},
booktitle = {IEEE International Conference on Big Data (Big Data)},
pages = {5855–5864},
publisher = {IEEE},
abstract = {Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first c onduct a n e xtensive e mpirical s tudy to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find t he s uitable c olumn o rder o f i nput d ata f or CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.},
keywords = {analytics},
pubstate = {published},
tppubtype = {inproceedings}
}
Tabular data synthesis is an emerging approach to circumvent strict regulations on data privacy while discovering knowledge through big data. Although state-of-the-art AI-based tabular data synthesizers, e.g., table-GAN, CTGAN, TVAE, and CTAB-GAN, are effective at generating synthetic tabular data, their training is sensitive to column permutations of input data. In this paper, we first c onduct a n e xtensive e mpirical s tudy to disclose such a property of permutation invariance and an in-depth analysis of the existing synthesizers. We show that changing the input column order worsens the statistical difference between real and synthetic data by up to 38.67% due to the encoding of tabular data and the network architectures. To fully unleash the potential of big synthetic tabular data, we propose two solutions: (i) AE-GAN, a synthesizer that uses an autoencoder network to represent the tabular data and GAN networks to synthesize the latent representation, and (ii) a feature sorting algorithm to find t he s uitable c olumn o rder o f i nput d ata f or CNN-based synthesizers. We evaluate the proposed solutions on five datasets in terms of the sensitivity to the column permutation, the quality of synthetic data, and the utility in downstream analyses. Our results show that we enhance the property of permutation-invariance when training synthesizers and further improve the quality and utility of synthetic data, up to 22%, compared to the existing synthesizers.