Giulio Rossetti

Italian National Research Council | CNR · Institute of Information Science and Technology "Alessandro Faedo" ISTI

Community discovery has emerged during the last decade as one of the most challenging problems in social network analysis. Many algorithms have been proposed to find communities on static networks, i.e. networks which do not change in time. However, social networks are dynamic realities (e.g. call graphs, online social networks): in such scenarios...

Community discovery in complex networks is the task of organizing a network's structure by grouping together nodes related to each other. Traditional approaches are based on the assumption that there is a global-level organization in the network. However, in many scenarios, each node is the bearer of complex information and cannot be classified in...

Due to the growing availability of Internet services in the last decade, the interactions between people became more and more easy to establish. For example, we can have an intercontinental job interview, or we can send real-time multimedia content to any friend of us just owning a smartphone. All this kind of human activities generates digital foo...

Evaluating a community detection algorithm is a complex task due to the lack of a shared and universally accepted definition of community. In literature, one of the most common way to assess the performances of a community detection algorithm is to compare its output with given ground truth communities by using computationally expensive metrics (i....

Every year football clubs trade players in order to build competitive rosters able to compete for success, increase the number of their supporters and amplify sponsors and media attention. In the complex system described by the football transfer market can we identify the strategies pursued by successful teams? Where do they search for new talents?...

People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users’ shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the...

Conceptual associations influence how human memory is structured: Cognitive research indicates that similar concepts tend to be recalled one after another. Semantic network accounts provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory reca...

Social Network sites are fertile ground for several polluting phenomena affecting online and offline spaces. Among these phenomena are included echo chambers, closed systems in which the opinions expressed by the people inside are exacerbated for the effect of the repetition, while opposite views are actively excluded. This paper offers a framework...

Large Language Models (LLMs) are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and...

Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates...

Network structure is often considered one of the most important features of a network, and various models exist to generate graphs having one of the most studied types of structures, such as blocks/communities or spatial structures. In this article, we introduce a framework for the generation of random graphs with a controlled size -- number of nod...

Large language models are becoming increasingly integrated into our lives. Hence, it is important to understand the biases present in their outputs in order to avoid perpetuating harmful stereotypes, which originate in our own flawed ways of thinking. This challenge requires developing new benchmarks and methods for quantifying affective and semant...

Given the gaps and limitations of traditional data for migration research, social big data have been proposed to fill and complement them. Amongst various types of social media data, user-generated content from Twitter is considered a valuable resource in migration studies. As recent works have shown, Twitter can indeed be used to study various mig...

Semantic networks provide a useful tool to understand how related concepts are retrieved from memory. However, most current network approaches use pairwise links to represent memory recall patterns. Pairwise connections neglect higher-order associations, i.e. relationships between more than two concepts at a time. These higher-order interactions mi...

Recent advances in network science have resulted in two distinct research directions aimed at augmenting and enhancing representations for complex networks. The first direction, that of high-order modeling, aims to focus on connectivity between sets of nodes rather than pairs, whereas the second one, that of feature-rich augmentation, incorporates...

People increasingly shape their opinions by accessing and discussing content shared on social networking websites. These platforms contain a mixture of other users' shared opinions and content from mainstream media sources. While online social networks have fostered information access and diffusion, they also represent optimal environments for the...

Interest in social media has only increased with time. Social media today represent the main channel to communicate and share personal information. Social media analysis usually combines content-based and network-based analysis. [...]

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms—fragmented across linguistics, psychology and computer science—by introducing FEature-Rich MUltiplex LEXic...

Echo chambers can be described as situations in which individuals encounter and interact only with viewpoints that confirm their own, thus moving, as a group, to more polarized and extreme positions. Recent literature mainly focuses on characterizing such entities via static observations, thus disregarding their temporal dimension. In this work, di...

Complex networks are solid models to describe human behavior. However, most analyses employing them are bounded to observations made on dyadic connectivity, whereas complex human dynamics involve higher-order relations as well. In the last few years, hypergraph models are rising as promising tools to better understand the behavior of social groups....

One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals’ personalities and relate it to emerging phenomena like polarization, radicalization, a...

Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types o...

A wealth of tourism-related data is available on the Internet, particularly on social networking sites (SNSs) like Facebook and Instagram. Big data analytics (BDA) allows this large quantity of data to be processed, supported by machine learning and artificial intelligence, and gain an in-depth understanding of traveller preferences and behaviours....

Multi-scale strategies to estimate mixing patterns are meant to capture heterogeneous behaviors among node homophily, but they ignore an important addendum often available in real-world networks: the time when edges are present and the time-varying paths that edges form accordingly. In this work, we go beyond the assumption of a static network topo...

The cultural integration of immigrants conditions their overall socio-economic integration as well as natives’ attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration—or assimilation—can be detrimental in that it implies forfeiting one’s ties to the origin country and eventually translates i...

Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between concepts in texts as co-occurrences enriched with affective data....

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Decades of psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and...

One of the main dimensions characterizing the unfolding of opinion formation processes in social debates is the degree of open-mindedness of the involved population. Opinion dynamic modeling studies have tried to capture such a peculiar expression of individuals' personalities and relate it to emerging phenomena like polarization, radicalization, a...

Every day, people inform themselves and create their opinions on social networks. Although these platforms have promoted the access and dissemination of information, they may expose readers to manipulative, biased, and disinformative content—co-causes of polarization/radicalization. Moreover, recommendation algorithms, intended initially to enhance...

In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms’ and investors’ relations. Focusing on deal-level data for the health sector, we first create a bipartite network amo...

Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarizati...

Understanding the careers and movements of highly skilled people plays an ever-increasing role in today's global knowledge-based economy. Researchers and academics are sources of innovation and development for governments and institutions. Our study uses scientific-related data to track careers evolution and Researchers' movements over time. To thi...

In this paper we characterize the performance of venture capital-backed firms based on their ability to attract investment. The aim of the study is to identify relevant predictors of success built from the network structure of firms' and investors' relations. Focusing on deal-level data for the health sector, we first create a bipartite network amo...

Knowledge in the human mind exhibits a dualistic vector/network nature. Modelling words as vectors is key to natural language processing, whereas networks of word associations can map the nature of semantic memory. We reconcile these paradigms - fragmented across linguistics, psychology and computer science - by introducing FEature-Rich MUltiplex L...

Nowadays, we live in a society where people often form their opinion by accessing and discussing contents shared on social networking websites. While these platforms have fostered information access and diffusion, they represent optimal environments for the proliferation of polluted contents, which is argued to be one of the co-causes of polarizati...

Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types o...

“Success” of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize “success” by introducing progressively more refined definitions, and we find a positive a...

Grouping well-connected nodes that also result in label-homogeneous clusters is a task often known as attribute-aware community discovery. While approaching node-enriched graph clustering methods, rigorous tools need to be developed for evaluating the quality of the resulting partitions. In this work, we present X-Mark , a model that generates synt...

Heterogeneity is a key aspect of complex networks, often emerging by looking at the distribution of node properties, from the milestone observations on the degree to the recent developments in mixing pattern estimation. Mixing patterns, in particular, refer to nodes' connectivity preferences with respect to an attribute label. Social networks are m...

"Success" of firms in venture capital markets is hard to define, and its determinants are still poorly understood. We build a bipartite network of investors and firms in the healthcare sector, describing its structure and its communities. Then, we characterize "success" introducing progressively more refined definitions, and we find a positive asso...

Writing messages is key to expressing feelings. This study adopts cognitive network science to reconstruct how individuals report their feelings in clinical narratives like suicide notes or mental health posts. We achieve this by reconstructing syntactic/semantic associations between conceptsin texts as co-occurrences enriched with affective data....

Due to the SARS-CoV-2 pandemic, epidemic modeling is now experiencing a constantly growing interest from researchers of heterogeneous study fields. Indeed, due to such an increased attention, several software libraries and scientific tools have been developed to ease the access to epidemic modeling. However, only a handful of such resources were de...

The analysis of dynamics in networks represents a great deal in the social network analysis research area. To support students, teachers, developers, and researchers in this work, the authors introduce a novel R package, namely DynComm. It is designed to be a multi-language package used for community detection and analysis on dynamic networks. The...

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifical...

In a digital environment, the term echo chamber refers to an alarming phenomenon in which beliefs are amplified or reinforced by communication repetition inside a closed system and insulated from rebuttal. Up to date, a formal definition, as well as a platform-independent approach for its detection, is still lacking. This paper proposes a general f...

In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users...

In Online Social Networks (OSN) numerous are the cases in which users create multiple accounts that publicly seem to belong to different people but are actually fake identities of the same person. These fictitious characters can be exploited to carry out abusive behaviors such as manipulating opinions, spreading fake news and disturbing other users...

Many algorithms have been proposed in the last 10 years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed bench...

Today, many users are actively using Twitter to express their opinions and to share information. Thanks to the availability of the data, researchers have studied behaviours and social networks of these users. International migration studies have also benefited from this social media platform to improve migration statistics. Although diverse types o...

The cultural integration of immigrants conditions their overall socio-economic integration as well as natives' attitudes towards globalisation in general and immigration in particular. At the same time, excessive integration -- or acculturation -- can be detrimental in that it implies forfeiting one's ties to the home country and eventually transla...

Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture...

Fake news diffusion represents one of the most pressing issues of our online society. In recent years, fake news has been analyzed from several points of view, primarily to improve our ability to separate them from the legit ones as well as identify their sources. Among such vast literature, a rarely discussed theme is likely to play uttermost impo...

Unveil the homophilic/heterophilic behaviors that characterize the wiring patterns of complex networks is an important task in social network analysis, often approached studying the assortative mixing of node attributes. Recent works underlined that a global measure to quantify node homophily necessarily provides a partial, often deceiving, picture...

Increased availability of epidemiological data, novel digital data streams, and the rise of powerful machine learning approaches have generated a surge of research activity on real-time epidemic forecast systems. In this paper, we propose the use of a novel data source, namely retail market data to improve seasonal influenza forecasting. Specifical...

Nowadays, due to the SARS-CoV-2 pandemic, epidemic modelling is experiencing a constantly growing interest from researchers of heterogeneous fields of study. Indeed, the vast literature on computational epidemiology offers solid grounds for analytical studies and the definition of novel models aimed at both predictive and prescriptive scenario desc...

Abstract Attribute-aware community discovery aims to find well-connected communities that are also homogeneous w.r.t. the labels carried by the nodes. In this work, we address such a challenging task presenting Eva, an algorithmic approach designed to maximize a quality function tailoring both structural and homophilic clustering criteria. We evalu...

As Nietzsche once wrote “Without music, life would be a mistake” (Twilight of the Idols, 1889.). The music we listen to reflects our personality, our way to approach life. In order to enforce self-awareness, we devised a Personal Listening Data Model that allows for capturing individual music preferences and patterns of music consumption. We applie...

Many algorithms have been proposed in the last ten years for the discovery of dynamic communities. However, these methods are seldom compared between themselves. In this article, we propose a generator of dynamic graphs with planted evolving community structure, as a benchmark to compare and evaluate such algorithms. Unlike previously proposed benc...

Modelling human mobility is crucial in several areas, from urban planning to epidemic modeling, traffic forecasting, and what-if analysis. On the one hand, existing models focus mainly on reproducing the spatial and temporal dimensions of human mobility, while the social aspect, though it influences human movements significantly, is often neglected...