Today, coding skills are among the most required competencies worldwide, often also for non-computer scientists. Because of this trend, community contribution-based, question-and-answer (Q&A) platforms became prominent for finding the proper solution to all programming issues. Stack Overflow has been the most popular platform for technical-related questions for years. Still, recently, some programming-related subreddits of Reddit have become a standing stone for questions and discussions. 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 Q&A 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.
@inproceedings{Antelmi_TheWebConf_2023,author={Antelmi, Alessia and Cordasco, Gennaro and Vinco, Daniele De and Spagnuolo, Carmine},title={The Age of Snippet Programming: Toward Understanding Developer Communities in Stack Overflow and Reddit},year={2023},isbn={9781450394192},publisher={Association for Computing Machinery},address={New York, NY, USA},doi={10.1145/3543873.3587673},html={https://doi.org/10.1145/3543873.3587673},booktitle={Companion Proceedings of the ACM Web Conference 2023},pages={1218–1224},numpages={7},keywords={Developer communities, Hypergraphs, Network Analysis, Q&A social platforms, Reddit, Stack Overflow, User behavior},series={WWW '23 Companion},abbr={TheWebConf},bibtex_show={true},pdf={publications/Antelmi_TheWebConf_2023.pdf},code={https://github.com/ddevin96/DevCommunities}}
ITADATA
The SWH-Analytics Framework
Alessia Antelmi,
Massimo Torquati,
Daniele Gregori,
Francesco Polzella,
Gianmarco Spinatelli,
and Marco Aldinucci
In Companion Proceedings of the ACM Web Conference 2023.
@conference{Antelmi_ITADATA_2023,author={Antelmi, Alessia and Torquati, Massimo and Gregori, Daniele and Polzella, Francesco and Spinatelli, Gianmarco and Aldinucci, Marco},title={The SWH-Analytics Framework},year={2023},journal={CEUR Workshop Proceedings},volume={3606},html={https://ceur-ws.org/Vol-3606/},pdf={publications/Antelmi_ITADATA_2023.pdf},abbr={ITADATA},month=sep,bibtex_show={true},code={https://github.com/alpha-unito/Software-Heritage-Analytics}}
MIS4TEL
Engagement in Open Data Workshops
Alessia Antelmi
In Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference.
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.
@inproceedings{Antelmi_TEL4FC_2023,author={Antelmi, Alessia},title={Engagement in Open Data Workshops},booktitle={Methodologies and Intelligent Systems for Technology Enhanced Learning, Workshops - 13th International Conference},year={2023},publisher={Springer Nature Switzerland},address={Cham},pages={332--342},isbn={978-3-031-42134-1},doi={10.1007/978-3-031-42134-1_33},pdf={publications/Antelmi_TEL4FC_2023.pdf},abbr={MIS4TEL},month=jun,bibtex_show={true},html={https://link.springer.com/chapter/10.1007/978-3-031-42134-1_33}}
CSUR
A Survey on Hypergraph Representation Learning
Alessia Antelmi,
Gennaro Cordasco,
Mirko Polato,
Vittorio Scarano,
Carmine Spagnuolo,
and Dingqi Yang
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.
@article{Antelmi_CSUR_2023,author={Antelmi, Alessia and Cordasco, Gennaro and Polato, Mirko and Scarano, Vittorio and Spagnuolo, Carmine and Yang, Dingqi},title={A Survey on Hypergraph Representation Learning},year={2023},issue_date={January 2024},publisher={Association for Computing Machinery},address={New York, NY, USA},volume={56},number={1},issn={0360-0300},html={https://doi.org/10.1145/3605776},doi={10.1145/3605776},journal={ACM Computing Surveys},month=aug,articleno={24},numpages={38},abbr={CSUR},bibtex_show={true},code={https://github.com/alessant/HEE},pdf={publications/Antelmi_CSUR_2023.pdf},supp={publications/Antelmi_CSUR_2023_supp.pdf}}
Open Data are published to let interested stakeholders exploit data and create value out of them, but limited technical skills are a crucial barrier. Learners are invited to develop data and information literacy according to 21st-century skills and become aware of open data sources and what they can do with the data. They are encouraged to learn how to analyse and exploit data, transform data into information by visualisation, and effectively communicate data insights. This paper presents a systematic literature review of initiatives to let K-12 learners familiarise themselves with Open Data. This review encompasses a total of 21 papers that met the inclusion criteria organising them in taxonomies according to the used data format, the adopted approach, and the expected learning outcome. The discussion compares the included initiative and points out challenges that should be overcome to advance the dialogue around Open Data at school.
@conference{Pellegrino_CSEDU_2023,author={Pellegrino, Maria Angela and Antelmi, Alessia},title={At School of Open Data: A Literature Review},booktitle={Proceedings of the 15th International Conference on Computer Supported Education - Volume 2: CSEDU,},year={2023},pages={172-183},publisher={SciTePress},organization={INSTICC},doi={10.5220/0011747500003470},isbn={978-989-758-641-5},bibtex_show={true},abbr={CSEDU},html={https://www.scitepress.org/Papers/2023/117475/117475.pdf},pdf={publications/Pellegrino_CSEDU_2023.pdf}}
APS
Experimenting with Agent-Based Model Simulation Tools
Alessia Antelmi,
Gennaro Cordasco,
Giuseppe D’Ambrosio,
Daniele De Vinco,
and Carmine Spagnuolo
Agent-based models (ABMs) are one of the most effective and successful methods for analyzing real-world complex systems by investigating how modeling interactions on the individual level (i.e., micro-level) leads to the understanding of emergent phenomena on the system level (i.e., macro-level). ABMs represent an interdisciplinary approach to examining complex systems, and the heterogeneous background of ABM users demands comprehensive, easy-to-use, and efficient environments to develop ABM simulations. Currently, many tools, frameworks, and libraries exist, each with its characteristics and objectives. This article aims to guide newcomers in the jungle of ABM tools toward choosing the right tool for their skills and needs. This work proposes a thorough overview of open-source general-purpose ABM tools and offers a comparison from a two-fold perspective. We first describe an off-the-shelf evaluation by considering each ABM tool’s features, ease of use, and efficiency according to its authors. Then, we provide a hands-on evaluation of some ABM tools by judging the effort required in developing and running four ABM models and the obtained performance.
@article{Dambrosio_APS_2022,author={Antelmi, Alessia and Cordasco, Gennaro and D'Ambrosio, Giuseppe and De Vinco, Daniele and Spagnuolo, Carmine},title={Experimenting with Agent-Based Model Simulation Tools},journal={Applied Sciences},volume={13},year={2023},number={1},article-number={13},url={https://www.mdpi.com/2076-3417/13/1/13},issn={2076-3417},doi={10.3390/app13010013},bibtex_show={true},abbr={APS},html={https://www.mdpi.com/2076-3417/13/1/13},month=jan,pdf={publications/Dambrosio_APS_2022.pdf},code={https://github.com/isislab-unisa/ABM_Comparison}}
2022
IEEE Access
A Volunteer Computing Architecture for Computational Workflows on Decentralized Web
Alessia Antelmi,
Giuseppe D’Ambrosio,
Andrea Petta,
Luigi Serra,
and Carmine Spagnuolo
The amount of accessible computational devices over the Internet offers an enormous but latent computational power. Nonetheless, the complexity of orchestrating and managing such devices requires dedicated architectures and tools and hinders the exploitation of this vast processing capacity. Over the last years, the paradigm of (Browser-based) Volunteer Computing emerged as a unique approach to harnessing such computational capabilities, leveraging the idea of voluntarily offering resources. This article proposes VFuse, a groundbreaking architecture to exploit the Browser-based Volunteer Computing paradigm via a ready-to-access volunteer network. VFuse offers a modern multi-language programming environment for developing scientific workflows using WebAssembly technology without requiring the user any local installation or configuration. We equipped our architecture with a secure and transparent rewarding mechanism based on blockchain technology (Ethereum) and distributed P2P file system (IPFS). Further, the use of Non-Fungible Tokens provides a unique, secure, and transparent methodology for recognizing the users’ participation in the network. We developed a prototype of the proposed architecture and four example applications implemented with our system. All code and examples are publicly available on GitHub.
@article{Serra_IEEEAccess_2022,author={Antelmi, Alessia and D'Ambrosio, Giuseppe and Petta, Andrea and Serra, Luigi and Spagnuolo, Carmine},journal={IEEE Access},title={A Volunteer Computing Architecture for Computational Workflows on Decentralized Web},year={2022},volume={10},number={},pages={98993-99010},doi={10.1109/ACCESS.2022.3207167},bibtex_show={true},abbr={IEEE Access},html={https://ieeexplore.ieee.org/abstract/document/9893800},month=sep,pdf={publications/Serra_IEEEAccess_2022.pdf},code={https://github.com/luigser/js-vfuse}}
ODAK
Open Data Literacy by Remote: Hiccups and Lessons
Alessia Antelmi,
and Maria Angela Pellegrino
In Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK (ODAK 2022).
Open Data are published to ensure the creation of value and data exploitation, but limited technical skills are a critical barrier. Most users lack the skills required to assess data quality and its fitness to use, awareness of open data sources, and what they can do with the data. To advance the dialogue around methods to increase awareness of Open Data, improve users’ skills to work with them, and deal with the requirement of letting future citizens develop data and information literacy according to 21st-century skills, this article proposes a series of workshops to let Italian high school learners familiarise themselves with effective communication based on Open Data. The article describes an ongoing activity, reporting preliminary results on engagement and learning. We discuss challenges in engaging learners remotely and the promising learning outcomes achieved by overcoming cultural and technical barriers to visualise Open Data.
@inproceedings{Antelmi_ODAK_2022,author={Antelmi, Alessia and Pellegrino, Maria Angela},booktitle={Proceedings of the Symposium on Open Data and Knowledge for a Post-Pandemic Era ODAK22, UK (ODAK 2022)},title={Open Data Literacy by Remote: Hiccups and Lessons},year={2022},volume={},number={},pages={1-5},doi={10.14236/ewic/ODAK22.7},abbr={ODAK},bibtex_show={true},html={https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/ODAK22.7},month=jul,pdf={publications/Antelmi_ODAK_2022.pdf}}
2021
IEEE Access
Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks
Alessia Antelmi,
Gennaro Cordasco,
Vittorio Scarano,
and Carmine Spagnuolo
Non-Pharmaceutical Interventions (NPIs) are essential measures that reduce and control a severe outbreak or a pandemic, especially in the absence of drug treatments. However, estimating and evaluating their impact on society remains challenging, considering the numerous and closely tied aspects to examine. This article proposes a fine-grain modeling methodology for NPIs, based on high-order relationships between people and environments, mimicking direct and indirect contagion pathways over time. After assessing the ability of each intervention in controlling an epidemic propagation, we devise a multi-objective optimization framework, which, based on the epidemiological data, calculates the NPI combination that should be implemented to minimize the spread of an epidemic as well as the damage due to the intervention. Each intervention is thus evaluated through an agent-based simulation, considering not only the reduction in the fraction of infected but also to what extent its application damages the daily life of the population. We run experiments on three data sets, and the results illustrate how the application of NPIs should be tailored to the specific epidemic situation. They further highlight the critical importance of correctly implementing personal protective (e.g., using face masks) and sanitization measures to slow down a pathogen spreading, especially in crowded places.
@article{Antelmi_IEEEAccess_2021,author={Antelmi, Alessia and Cordasco, Gennaro and Scarano, Vittorio and Spagnuolo, Carmine},journal={IEEE Access},title={Modeling and Evaluating Epidemic Control Strategies With High-Order Temporal Networks},year={2021},volume={9},number={},pages={140938-140964},doi={10.1109/ACCESS.2021.3119459},abbr={IEEE Access},bibtex_show={true},html={https://ieeexplore.ieee.org/document/9567682},month=sep,code={https://github.com/alessant/HGEpidemics},pdf={publications/Antelmi_IEEEAccess_2021.pdf}}
IEEE CoG
Comparing the Structures and Characteristics of Different Game Social Networks - The Steam Case
In most games, social connections are an essential part of the gaming experience. Players connect in communities inside or around games and form friendships, which can be translated into other games or even in the real world. Recent research has investigated social phenomena within the player social network of several multiplayer games, yet we still know very little about how these networks are shaped and formed. Specifically, we are unaware of how the game type and its mechanics are related to its community structure and how those structures vary in different games. This paper presents an initial analysis of Steam users and how friendships on Steam are formed around 200 games. We examine the friendship graphs of these 200 games by dividing them into clusters to compare their network properties and their specific characteristics (e.g., genre, game elements, and mechanics). We found how the Steam user-defined tags better characterized the clusters than the game genre, suggesting that how players perceive and use the game also reflects how they connect in the community. Moreover, team-based games are associated with more cohesive and clustered networks than games with a stronger single-player focus, supporting the idea that playing together in teams more likely produces social capital (i.e., Steam friendships).
@inproceedings{Loria_Cog_2021,author={Loria, Enrica and Antelmi, Alessia and Pirker, Johanna},booktitle={2021 IEEE Conference on Games (CoG)},title={Comparing the Structures and Characteristics of Different Game Social Networks - The Steam Case},year={2021},volume={},number={},pages={1-8},doi={10.1109/CoG52621.2021.9619130},abbr={IEEE CoG},bibtex_show={true},html={https://ieeexplore.ieee.org/abstract/document/9619130},month=aug,pdf={publications/Loria_CoG_2021.pdf}}
Entropy
Social Influence Maximization in Hypergraphs
Alessia Antelmi,
Gennaro Cordasco,
Carmine Spagnuolo,
and Przemyslaw Szufel
This work deals with a generalization of the minimum Target Set Selection (TSS) problem, a key algorithmic question in information diffusion research due to its potential commercial value. Firstly proposed by Kempe et al., the TSS problem is based on a linear threshold diffusion model defined on an input graph with node thresholds, quantifying the hardness to influence each node. The goal is to find the smaller set of items that can influence the whole network according to the diffusion model defined. This study generalizes the TSS problem on networks characterized by many-to-many relationships modeled via hypergraphs. Specifically, we introduce a linear threshold diffusion process on such structures, which evolves as follows. Let H=(V,E) be a hypergraph. At the beginning of the process, the nodes in a given set S⊆V are influenced. Then, at each iteration, (i) the influenced hyperedges set is augmented by all edges having a sufficiently large number of influenced nodes; (ii) consequently, the set of influenced nodes is enlarged by all the nodes having a sufficiently large number of already influenced hyperedges. The process ends when no new nodes can be influenced. Exploiting this diffusion model, we define the minimum Target Set Selection problem on hypergraphs (TSSH). Being the problem NP-hard (as it generalizes the TSS problem), we introduce four heuristics and provide an extensive evaluation on real-world networks.
@article{Antelmi_Entropy_2021,author={Antelmi, Alessia and Cordasco, Gennaro and Spagnuolo, Carmine and Szufel, Przemyslaw},title={Social Influence Maximization in Hypergraphs},journal={Entropy},volume={23},year={2021},number={7},article-number={796},doi={10.3390/e23070796},abbr={Entropy},bibtex_show={true},html={https://www.mdpi.com/1099-4300/23/7/796?type=check_update&version=1},month=mar,code={https://github.com/pszufe/LTMSim.jl},pdf={publications/Antelmi_Entropy_2021.pdf}}
2020
WAW
Information Diffusion in Complex Networks: A Model Based on Hypergraphs and Its Analysis
Alessia Antelmi,
Gennaro Cordasco,
Carmine Spagnuolo,
and Przemyslaw Szufel
In Algorithms and Models for the Web Graph, WAW’20.
This work introduces the problem of social influence diffusion in complex networks, where vertices are linked not only through simple pairwise relationships to other nodes but with groups of nodes of arbitrary size. A challenging problem that arises in this domain is to determine a small subset of nodes S (a target-set) able to spread their influence in the whole network. This problem has been formalized and studied in different ways, and many viable solutions have been found for graphs. These have been applied to study several phenomena in research fields such as social, economic, biological, and physical sciences.
@inproceedings{Antelmi_WAW_2020,author={Antelmi, Alessia and Cordasco, Gennaro and Spagnuolo, Carmine and Szufel, Przemyslaw},title={Information Diffusion in Complex Networks: A Model Based on Hypergraphs and Its Analysis},booktitle={Algorithms and Models for the Web Graph, WAW'20},year={2020},publisher={Springer International Publishing},address={Cham},series={Lecture Notes in Computer Science},pages={36--51},abbr={WAW},bibtex_show={true},html={https://link.springer.com/chapter/10.1007/978-3-030-48478-1_3},month=jul,code={https://github.com/pszufe/LTMSim.jl},pdf={publications/Antelmi_WAW_2020.pdf}}
AAMAS
A Design-Methodology for Epidemic Dynamics via Time-Varying Hypergraphs
Alessia Antelmi,
Gennaro Cordasco,
C. Spagnuolo,
and V. Scarano
In Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems.
In epidemiology science, the importance to explore innovative modeling tools for acutely analyzing epidemic diffusion is turning into a big challenge considering the myriad of real-world aspects to capture. Typically, equation-based models, such as SIS and SIR, are used to study the propagation of diseases over a population. Improved approaches also include human-mobility patterns as network information to describe contacts among individuals. However, there still is the need to incorporate in these models information about different types of contagion, geographical information, humans habits, and environmental properties. In this paper, we propose a novel approach that takes into account: 1. direct and indirect epidemic contagion pathways to explore the dynamics of the epidemic, 2. the times of possible contagions, and 3. human-mobility patterns. We combine these three features exploiting time-varying hypergraphs, and we embed this model into a design-methodology for agent-based models (ABMs), able to improve the correctness in the epidemic estimations of classical contact-network approaches. We further describe a diffusion algorithm suitable for our design-methodology and adaptable to the peculiarities of any disease spreading policies and/or models. Finally, we tested our methodology by developing an ABM, realizing the SIS epidemic compartmental model, for simulating an epidemic propagation over a population of individuals. We experimented the model using real user-mobility data from the location-based social networkFoursquare, and we demonstrated the high-impact of temporal direct and indirect contagion pathways.
@inproceedings{Antelmi_AAMAS_2020,author={Antelmi, Alessia and Cordasco, Gennaro and Spagnuolo, C. and Scarano, V.},title={A Design-Methodology for Epidemic Dynamics via Time-Varying Hypergraphs},year={2020},publisher={International Foundation for Autonomous Agents and Multiagent Systems},booktitle={Proceedings of the 19th International Conference on Autonomous Agents and MultiAgent Systems},pages={61–69},numpages={9},series={AAMAS '20},doi={10.5555/3398761.3398774},abbr={AAMAS},bibtex_show={true},html={https://dl.acm.org/doi/10.5555/3398761.3398774},month=may,code={https://github.com/alessant/HGEpidemics},pdf={publications/Antelmi_AAMAS_2020.pdf}}
Internet Math.
Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl
Alessia Antelmi,
Gennaro Cordasco,
Bogumił Kamiński,
Paweł Prałat,
Vittorio Scarano,
Carmine Spagnuolo,
and Przemyslaw Szufel
Real-world complex networks are usually being modeled as graphs. The concept of graphs assumes that the relations within the network are binary (for instance, between pairs of nodes); however, this is not always true for many real-life scenarios, such as peer-to-peer communication schemes, paper co-authorship, or social network interactions. For such scenarios, it is often the case that the underlying network is better and more naturally modeled by hypergraphs. A hypergraph is a generalization of a graph in which a single (hyper)edge can connect any number of vertices. Hypergraphs allow modelers to have a complete representation of multi-relational (many-to-many) networks; hence, they are extremely suitable for analyzing and discovering more subtle dependencies in such data structures. Working with hypergraphs requires new software libraries that make it possible to perform operations on them, from basic algorithms (such as searching or traversing the network) to computing significant hypergraph measures, to including more challenging algorithms (such as community detection). In this paper, we present a new software library, SimpleHypergraphs.jl, written in the Julia language and designed for high-performance computing on hypergraphs and propose two new algorithms for analyzing their properties: s-betweenness and modified label propagation. We also present various approaches for hypergraph visualization integrated into our tool. In order to demonstrate how to exploit the library in practice, we discuss two case studies based on the 2019 Yelp Challenge dataset and the collaboration network built upon the Game of Thrones TV series. The results are promising and they confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.
@article{Antelmi_InternetMathematics_2020,title={Analyzing, Exploring, and Visualizing Complex Networks via Hypergraphs using SimpleHypergraphs.jl},issn={1944-9488},doi={10.24166/im.01.2020},journal={Internet Mathematics},publisher={Internet Mathematics},author={Antelmi, Alessia and Cordasco, Gennaro and Kami{\'{n}}ski, Bogumi{\l} and Pra{\l}at, Pawe{\l} and Scarano, Vittorio and Spagnuolo, Carmine and Szufel, Przemyslaw},year={2020},abbr={Internet Math.},bibtex_show={true},html={https://www.internetmathematicsjournal.com/article/12464-analyzing-exploring-and-visualizing-complex-networks-via-hypergraphs-using-simplehypergraphs-jl},month=mar,code={https://github.com/pszufe/SimpleHypergraphs.jl},pdf={publications/Antelmi_InternetMathematics_2020.pdf}}
2019
AsiaSim
On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulations
Alessia Antelmi,
Gennaro Cordasco,
Matteo D’Auria,
Daniele De Vinco,
Alberto Negro,
and Carmine Spagnuolo
In Methods and Applications for Modeling and Simulation of Complex Systems.
The analysis of real systems and the development of predictive models to describe the evolution of real phenomena are challenging tasks that can improve the design of methodologies in many research fields. In this context, Agent-Based Model (ABM) can be seen as an innovative tool for modelling real-world complex simulations. This paper presents Rust-AB, an open-source library for developing ABM simulation on sequential and/or parallel computing platforms, exploiting Rust as programming language. The Rust-AB architecture as well as an investigation on the ability of Rust to develop ABM simulations are discussed. An ABM simulation written in Rust-AB, and a performance comparison against the well-adopted Java ABM toolkit MASON is also presented.
@inproceedings{Spagnuolo_AsiaSim_2019,author={Antelmi, Alessia and Cordasco, Gennaro and D'Auria, Matteo and De Vinco, Daniele and Negro, Alberto and Spagnuolo, Carmine},title={On Evaluating Rust as a Programming Language for the Future of Massive Agent-Based Simulations},booktitle={Methods and Applications for Modeling and Simulation of Complex Systems},year={2019},publisher={Springer Singapore},address={Singapore},pages={15--28},isbn={978-981-15-1078-6},doi={10.1007/978-981-15-1078-6_2},pdf={publications/Spagnuolo_AsiaSim_2019.pdf},bibtex_show={true},html={https://link.springer.com/chapter/10.1007/978-981-15-1078-6_2},code={https://github.com/krABMaga/krABMaga},abbr={AsiaSim},note={Best Paper Nominee}}
WAW
SimpleHypergraphs.jl—Novel Software Framework for Modelling and Analysis of Hypergraphs
Alessia Antelmi,
Gennaro Cordasco,
Bogumił Kamiński,
Paweł Prałat,
Vittorio Scarano,
Carmine Spagnuolo,
and Przemyslaw Szufel
In Algorithms and Models for the Web Graph, WAW’19.
Hypergraphs are natural generalization of graphs in which a single (hyper)edge can connect any number of vertices. As a result, hypergraphs are suitable and useful to model many important networks and processes. Typical applications are related to social data analysis and include situations such as exchanging emails with several recipients, reviewing products on social platforms, or analyzing security vulnerabilities of information networks. In many situations, using hypergraphs instead of classical graphs allows us to better capture and analyze dependencies within the network. In this paper, we propose a new library, named SimpleHypergraphs.jl, designed for efficient hypegraph analysis. The library exploits the Julia language flexibility and direct support for distributed computing in order to bring a new quality for simulating and analyzing processes represented as hypergraphs. In order to show how the library can be used we study two case studies based on the Yelp dataset. Results are promising and confirm the ability of hypergraphs to provide more insight than standard graph-based approaches.
@inproceedings{Antelmi_WAW_2019,author={Antelmi, Alessia and Cordasco, Gennaro and Kami{\'{n}}ski, Bogumi{\l} and Pra{\l}at, Pawe{\l} and Scarano, Vittorio and Spagnuolo, Carmine and Szufel, Przemyslaw},title={SimpleHypergraphs.jl---Novel Software Framework for Modelling and Analysis of Hypergraphs},booktitle={Algorithms and Models for the Web Graph, WAW'19},year={2019},publisher={Springer International Publishing},address={Cham},pages={115--129},isbn={978-3-030-25070-6},series={WAW'19},abbr={WAW},bibtex_show={true},html={https://link.springer.com/chapter/10.1007/978-3-030-25070-6_9},month=jul,code={https://github.com/pszufe/SimpleHypergraphs.jl},pdf={publications/Antelmi_WAW_2019.pdf}}
UMAP
Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling
Alessia Antelmi
In Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization.
Since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs user behaviour. Based on the current challenges and future directions proposed in literature, we aim to investigate how to comprehensively model OSNs user behaviours, by exploiting and combining user data of different nature. We propose to use hypergraphs as a model to easily analyse and combine structural, semantic, and activity-related user information, and to study their evolution over time. This novel user behaviour modelling technique will converge in open, efficient, and scalable libraries, which will be integrated into a modular framework able to handle the data crawling process from several OSNs.
@inproceedings{Antelmi_UMAP_2019,author={Antelmi, Alessia},title={Towards an Exhaustive Framework for Online Social Networks User Behaviour Modelling},year={2019},isbn={9781450360210},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3320435.3323466},booktitle={Proceedings of the 27th ACM Conference on User Modeling, Adaptation and Personalization},pages={349–352},numpages={4},abbr={UMAP},bibtex_show={true},html={https://dl.acm.org/doi/10.1145/3320435.3323466},month=jun,pdf={publications/Antelmi_UMAP_2019.pdf}}
TheWebConf
Characterizing the Behavioral Evolution of Twitter Users and The Truth Behind the 90-9-1 Rule
Alessia Antelmi,
Delfina Malandrino,
and Vittorio Scarano
In Companion Proceedings of The 2019 World Wide Web Conference.
Online Social Networks (OSNs) represent a fertile field to collect real user data and to explore OSNs user behavior. Recently, two topics are drawing the attention of researchers: the evolution of online social roles and the question of participation inequality. In this work, we bring these two fields together to study and characterize the behavioral evolution of OSNs users according to the quantity and the typology of their social interactions. We found that online participation on the microblogging platform can be categorized into four different activity levels. Furthermore, we empirically verified that the 90-9-1 rule of thumb about participation inequality is not an accurate representation of reality. Findings from our analysis reveal that lurkers are less than expected: they are not 9 out of 10 as suggested by Nielsen, but 3 out of 4. This represents a significant result that can give new insights on how users relate with social media and how their use is evolving towards a more active interaction with the new generation of consumers.
@inproceedings{Antelmi_WWW_2019,author={Antelmi, Alessia and Malandrino, Delfina and Scarano, Vittorio},title={Characterizing the Behavioral Evolution of Twitter Users and The Truth Behind the 90-9-1 Rule},year={2019},isbn={9781450366755},publisher={Association for Computing Machinery},address={New York, NY, USA},url={https://doi.org/10.1145/3308560.3316705},doi={10.1145/3308560.3316705},booktitle={Companion Proceedings of The 2019 World Wide Web Conference},pages={1035–1038},numpages={4},location={San Francisco, USA},series={WWW ’19},abbr={TheWebConf},bibtex_show={true},html={https://dl.acm.org/doi/10.1145/3308560.3316705},month=may,pdf={publications/Antelmi_TheWebConf_2019.pdf}}
2018
IEEE BigData
Characterizing Twitter Users: What do Samantha Cristoforetti, Barack Obama and Britney Spears Have in Common?
Alessia Antelmi,
Delfina Malandrino,
and Vittorio Scarano
In 2018 IEEE International Conference on Big Data (Big Data).
The exponential growth in the use of digital devices and the ubiquitous online access produce a huge amount of structured and unstructured data that can be mined and analyzed to gather insights into several domains. In particular, since the advent of Web 2.0, Online Social Networks (OSNs) represent a rich opportunity for researchers to collect real user data and to explore OSNs users behavior. This study represents a first attempt to characterize and classify OSNs users according to their level of activity through the use of user profile attributes. We analyzed four case studies from the Twitter platform for a final total of around 721 thousand users, divided into four sub-datasets and examined over a period of at least six months in 2017. Following a data-driven methodology, we found that static, profile-based information - based on the entire lifetime of the users - can help to recognize users influence in Twitter online communities. On the other hand, these profile attributes are not enough to characterize user activity on the microblogging platform.
@inproceedings{Antelmi_BigData_2018,author={{Antelmi}, Alessia and {Malandrino}, Delfina and {Scarano}, Vittorio},booktitle={2018 IEEE International Conference on Big Data (Big Data)},title={{Characterizing Twitter Users: What do Samantha Cristoforetti, Barack Obama and Britney Spears Have in Common?}},year={2018},volume={},number={},pages={3622-3627},month=dec,doi={10.1109/BigData.2018.8622045},bibtex_show={true},html={https://ieeexplore.ieee.org/document/8622045},abbr={IEEE BigData},pdf={publications/Antelmi_IEEEBigData_2018.pdf}}
AICS
Towards a more systematic analysis of twitter data: A framework for the analysis of twitter communities
Alessia Antelmi,
Josephine Griffith,
and Karen Young
@inproceedings{Antelmi_AICS_2018,author={Antelmi, Alessia and Griffith, Josephine and Young, Karen},title={Towards a more systematic analysis of twitter data: A framework for the analysis of twitter communities},year={2018},month=dec,booktitle={CEUR Workshop Proceedings},volume={2259},pages={303 – 314},html={https://ceur-ws.org/Vol-2259/aics_28.pdf},publisher={CEUR-WS},abbr={AICS},bibtex_show={true},pdf={publications/Antelmi_AICS_2018.pdf}}
IEEE GEM
Understanding User Engagement with Entertainment Media: A Case Study of the Twitter Behaviour of Game of Thrones (GoT) Fans
Alessia Antelmi,
Joh Breslin,
and Karen Young
In 2018 IEEE Games, Entertainment, Media Conference.
User Engagement in digital experiences is a fundamental concern for technology developers, educators, businesses, TV networks and marketing agencies. However, engagement remains a confusing concept with cognitive, emotional and behavioural dimensions, that depends on a large number of technical and human interrelated factors. This study contributes to our understanding of User Engagement at a behavioural level, through an analysis of a HBO Game of Thrones (GoT) fans’ Twitter dataset, collected over a six-month period. This analysis across the GoT universe: literary, screen, and media extensions, found users are most engaged in discussions around the TV show, and predictably most particularly the season premiere and finale, on the day after it is aired using mobile devices to post their status updates. Additionally, an initial hash tag analysis reveals users are engaged in both user generated conversations and in corporate TV network campaigns, while initial semantic analysis of tweets reveals TV locations and GOT characters to be the most tweeted about topics. This characterisation of user behaviours presents opportunities to content developers to maximise and capitalise on this active social interaction in a timely manner, keeping the brand alive and also to mine this user feedback in real time to inform the development of forthcomina content.
@inproceedings{Antelmi_IEEEGEM_2018,author={{Antelmi}, Alessia and {Breslin}, Joh and {Young}, Karen},booktitle={2018 IEEE Games, Entertainment, Media Conference},series={GEM'18},title={Understanding User Engagement with Entertainment Media: A Case Study of the Twitter Behaviour of Game of Thrones (GoT) Fans},year={2018},doi={10.1109/GEM.2018.8516505},volume={},number={},pages={1-9},bibtex_show={true},html={https://ieeexplore.ieee.org/document/8516505},abbr={IEEE GEM},month=aug,pdf={/publications/Antelmi_GEM_2018.pdf}}
2015
Euro-Par
On Evaluating Graph Partitioning Algorithms for Distributed Agent Based Models on Networks
Alessia Antelmi,
Gennaro Cordasco,
Carmine Spagnuolo,
and Luca Vicidomini
Graph Partitioning is a key challenge problem with application in many scientific and technological fields. The problem is very well studied with a rich literature and is known to be NP-hard. Several heuristic solutions, which follow diverse approaches, have been proposed, they are based on different initial assumptions that make them difficult to compare. An analytical comparison was performed based on an Implementation Challenge [3], however being a multi-objective problem (two opposing goals are for instance load balancing and edge-cut size), the results are difficult to compare and it is hard to foresee what can be the impact of one solution, instead of another, in a real scenario. In this paper we analyze the problem in a real context: the development of a distributed agent-based simulation model on a network field (which for instance can model social interactions).
@inproceedings{Spagnuolo_EuroPar_2015,author={Antelmi, Alessia and Cordasco, Gennaro and Spagnuolo, Carmine and Vicidomini, Luca},title={On Evaluating Graph Partitioning Algorithms for Distributed Agent Based Models on Networks},booktitle={Euro-Par 2015: Parallel Processing Workshops},year={2015},publisher={Springer International Publishing},address={Cham},pages={367--378},isbn={978-3-319-27308-2},bibtex_show={true},html={https://link.springer.com/chapter/10.1007/978-3-319-27308-2_30},abbr={Euro-Par},month=aug,pdf={publications/Spagnuolo_EuroPar_2015.pdf}}