65 result(s)
Page Size: 10, 20, 50
Export: bibtex, xml, json, csv
Order by:

CNR Author operator: and / or
more
Typology operator: and / or
Language operator: and / or
Date operator: and / or
Rights operator: and / or
2020 Conference object Open Access OPEN

Eva: Attribute-Aware Network Segmentation
Citraro S., Rossetti G.
Identifying topologically well-defined communities that are also homogeneous w.r.t. attributes carried by the nodes that compose them is a challenging social network analysis task. We address such a problem by introducing Eva, a bottom-up low complexity algorithm designed to identify network hidden mesoscale topologies by optimizing structural and attribute-homophilic clustering criteria. We evaluate the proposed approach on heterogeneous real-world labeled network datasets, such as co-citation, linguistic, and social networks, and compare it with state-of-art community discovery competitors. Experimental results underline that Eva ensures that network nodes are grouped into communities according to their attribute similarity without considerably degrading partition modularity, both in single and multi node-attribute scenarios.Source: International Conference on Complex Networks and their Applications, pp. 141–151, Lisbona, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_12
Project(s): SoBigData via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | Unknown Repository Open Access | link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2020 Conference object Open Access OPEN

Exorcising the Demon: Angel, Efficient Node-Centric Community Discovery
Rossetti G.
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: International Conference on Complex Networks and their Applications, pp. 152–163, Lisbona, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_13
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2020 Conference object Open Access OPEN

Community-Aware Content Diffusion: Embeddednes and Permeability
Milli L., Rossetti G.
Viruses, opinions, ideas are different contents sharing a common trait: they need carriers embedded into a social context to spread. Modeling and approximating diffusive phenomena have always played an essential role in a varied range of applications from outbreak prevention to the analysis of meme and fake news. Classical approaches to such a task assume diffusion processes unfolding in a mean-field context, every actor being able to interact with all its peers. However, during the last decade, such an assumption has been progressively superseded by the availability of data modeling the real social network of individuals, thus producing a more reliable proxy for social interactions as spreading vehicles. In this work, following such a trend, we propose alternative ways of leveraging apriori knowledge on mesoscale network topology to design community-aware diffusion models with the aim of better approximate the spreading of content over complex and clustered social tissues.Source: International Conference on Complex Networks and their Applications, pp. 362–371, 10-12/12/2019
DOI: 10.1007/978-3-030-36687-2_30
Project(s): SoBigData via OpenAIRE

See at: link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2020 Report Open Access OPEN

Mobile phone data analytics against the COVID-19 epidemics in Italy: flow diversity and local job markets during the national lockdown
Bonato P., Cintia P., Fabbri F., Fadda D., Giannotti F., Lopalco P. L., Mazzilli S., Nanni M., Pappalardo L., Pedreschi D., Penone F., Rinzivillo S., Rossetti G., Savarese M., Tavoschi L.
Understanding human mobility patterns is crucial to plan the restart of production and economic activities, which are currently put in "stand-by" to fight the diffusion of the epidemics. A recent analysis shows that, following the national lockdown of March 9th, the mobility fluxes have decreased by 50% or more, everywhere in the country. To this purpose, we use mobile phone data to compute the movements of people between Italian provinces, and we analyze the incoming, outcoming and internal mobility flows before and during the national lockdown (March 9th, 2020) and after the closure of non-necessary productive and economic activities (March 23th, 2020). The population flow across provinces and municipalities enable for the modeling of a risk index tailored for the mobility of each municipality or province. Such an index would be a useful indicator to drive counter-measures in reaction to a sudden reactivation of the epidemics. Mobile phone data, even when aggregated to preserve the privacy of individuals, are a useful data source to track the evolution in time of human mobility, hence allowing for monitoring the effectiveness of control measures such as physical distancing. In this report, we address the following analytical questions: How does the mobility structure of a territory change? Do incoming and outcoming flows become more predictable during the lockdown, and what are the differences between weekdays and weekends? Can we detect proper local job markets based on human mobility flows, to eventually shape the borders of a local outbreak?Source: ISTI Technical Reports 005/2020, 2020, 2020
DOI: 10.32079/ISTI-TR-2020/005
DOI: 10.32079/isti-tr-2020/005

See at: arXiv.org e-Print Archive Open Access | DOI Resolver Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Article Open Access OPEN

ANGEL: efficient, and effective, node-centric community discovery in static and dynamic networks
Rossetti G.
Community discovery is one of the most challenging tasks in social network analysis. During the last decades, several algorithms have been proposed with the aim of identifying communities in complex networks, each one searching for mesoscale topologies having different and peculiar characteristics. Among such vast literature, an interesting family of Community Discovery algorithms, designed for the analysis of social network data, is represented by overlapping, node-centric approaches. In this work, following such line of research, we propose Angel, an algorithm that aims to lower the computational complexity of previous solutions while ensuring the identification of high-quality overlapping partitions. We compare Angel, both on synthetic and real-world datasets, against state of the art community discovery algorithms designed for the same community definition. Our experiments underline the effectiveness and efficiency of the proposed methodology, confirmed by its ability to constantly outperform the identified competitors.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00270-6
DOI: 10.1007/s41109-020-00270-6

See at: Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

The relationship between human mobility and viral transmissibility during the COVID-19 epidemics in Italy
Cintia P., Fadda D., Giannotti F., Pappalardo L., Rossetti G., Pedreschi D., Rinzivillo S., Bonato P., Fabbri F., Penone F., Bavarese M., Checchi D., Chiaromonte F., Vineis P., Gazzetta G., Riccardo F., Marziano V., Poletti P., Trentini F., Bella A., Xanthi A., Del Manso M., Fabiani M., Bellino S., Boros S., Urdiales A. M., Vescia M. F., Brusaferro S., Rezza G., Pezzotti P., Ajelli M., Merler S.
We describe in this report our studies to understand the relationship between human mobility and the spreading of COVID-19, as an aid to manage the restart of the social and economic activities after the lockdown and monitor the epidemics in the coming weeks and months. We compare the evolution (from January to May 2020) of the daily mobility flows in Italy, measured by means of nation-wide mobile phone data, and the evolution of transmissibility, measured by the net reproduction number, i.e., the mean number of secondary infections generated by one primary infector in the presence of control interventions and human behavioural adaptations. We find a striking relationship between the negative variation of mobility flows and the net reproduction number, in all Italian regions, between March 11th and March 18th, when the country entered the lockdown. This observation allows us to quantify the time needed to "switch off" the country mobility (one week) and the time required to bring the net reproduction number below 1 (one week). A reasonably simple regression model provides evidence that the net reproduction number is correlated with a region's incoming, outgoing and internal mobility. We also find a strong relationship between the number of days above the epidemic threshold before the mobility flows reduce significantly as an effect of lockdowns, and the total number of confirmed SARS-CoV-2 infections per 100k inhabitants, thus indirectly showing the effectiveness of the lockdown and the other non-pharmaceutical interventions in the containment of the contagion. Our study demonstrates the value of "big" mobility data to the monitoring of key epidemic indicators to inform choices as the epidemics unfolds in the coming months.Project(s): SoBigData via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Master thesis Unknown

Modeling Human Mobility considering Spatial, Temporal and Social Dimensions
Cornacchia G.
The analysis of human mobility is crucial in several areas, from urban planning to epidemic modeling, estimation of migratory flows and traffic forecasting.However, mobility data (e.g., Call Detail Records and GPS traces from vehicles or smartphones) are sensitive since it is possible to infer personal information even from anonymized datasets.A solution to dealing with this privacy issue is to use synthetic and realistic trajectories generated by proper generative models.Existing mechanistic generative models usually consider the spatial and temporal dimensions only. In this thesis, we select as a baseline model GeoSim, which considers the social dimension together with spatial and temporal dimensions during the generation of the synthetic trajectories.Our contribution in the field of the human mobility consists of including, incrementally, three mobility mechanisms, specifically the introduction of the distance and the use of a gravity-model in the location selection phase, finally, we include a diary generator, an algorithm capable to capture the tendency of humans to follow or break their routine, improving the modeling capability of the GeoSim model.
We show that the three implemented models, obtained from GeoSim with the introduction of the mobility mechanisms, can reproduce the statistical proprieties of real trajectories, in all the three dimensions, more accurately than GeoSim.
Project(s): SoBigData via OpenAIRE

See at: etd.adm.unipi.it | CNR ExploRA


2020 Report Restricted

UTLDR: an agent-based framework for modeling infectious diseases and public interventions
Rossetti G., Milli L., Citraro S., Morini V.
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 descriptions. To ease the access to diffusion modelling, several programming libraries and tools have been proposed during the last decade: however, to the best of our knowledge, none of them is explicitly designed to allow its users to integrate public interventions in their model. In this work, we introduce UTLDR, a framework that can simulate the effects of several public interventions (and their combinations) on the unfolding of epidemic processes. UTLDR enables the design of compartmental models incrementally and to simulate them over complex interaction network topologies. Moreover, it allows integrating external information on the analyzed population (e.g., age, gender, geographical allocation, and mobility patterns. . . ) and to use it to stratify and refine the designed model. After introducing the framework, we provide a few case studies to underline its flexibility and expressive power.Source: ISTI Working Papers, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: CNR ExploRA Restricted


2020 Report Open Access OPEN

Conformity: A Path-Aware Homophily Measure for Node-Attributed Networks
Rossetti G., Citraro S., Milli L.
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 of the reality. Moving from such literature, in this work, we propose a novel measure, namely Conformity, designed to overcome such limitation by providing a node-centric quantification of assortative mixing patterns. Differently from the measures proposed so far, Conformity is designed to be path-aware, thus allowing for a more detailed evaluation of the impact that nodes at different degrees of separations have on the homophilic embeddedness of a target. Experimental analysis on synthetic and real data allowed us to observe that Conformity can unveil valuable insights from node-attributed graphs.Source: ISTI Working Papers, 2020, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: arxiv.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

Predicting seasonal influenza using supermarket retail records
Miliou I., Xiong X., Rinzivillo S., Zhang Q., Rossetti G., Giannotti F., Pedreschi D., Vespignani A.
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. Specifically, we consider supermarket retail data as a proxy signal for influenza, through the identification of sentinel baskets, i.e., products bought together by a population of selected customers. We develop a nowcasting and forecasting framework that provides estimates for influenza incidence in Italy up to 4 weeks ahead. We make use of the Support Vector Regression (SVR) model to produce the predictions of seasonal flu incidence. Our predictions outperform both a baseline autoregressive model and a second baseline based on product purchases. The results show quantitatively the value of incorporating retail market data in forecasting models, acting as a proxy that can be used for the real-time analysis of epidemics.Source: ISTI Technical Reports 2020/012, 2020, 2020
DOI: 10.32079/ISTI-TR-2020/012
DOI: 10.32079/isti-tr-2020/012
Project(s): Development of an Open-Source and Data-Driven Modeling Platform to Monitor and Forecast Disease Activity via OpenAIRE, SoBigData via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | DOI Resolver Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Report Open Access OPEN

Evaluating Community Detection Algorithms for Progressively Evolving Graphs
Cazabet R., Boudebza S., Rossetti G.
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 benchmarks, it is able to specify any desired evolving community structure through a descriptive language, and then to generate the corresponding progressively evolving network. We empirically evaluate six existing algorithms for dynamic community detection in terms of instantaneous and longitudinal similarity with the planted ground truth, smoothness of dynamic partitions, and scalability. We notably observe different types of weaknesses depending on their approach to ensure smoothness, namely Glitches, Oversimplification, and Identity loss. Although no method arises as a clear winner, we observe clear differences between methods, and we identified the fastest, those yielding the most smoothed or the most accurate solutions at each step.Source: ISTI Technical Reports 2020/013, 2020, 2020
DOI: 10.32079/ISTI-TR-2020/013
DOI: 10.32079/isti-tr-2020/013
Project(s): BITUNAM via OpenAIRE, SoBigData-PlusPlus via OpenAIRE

See at: arXiv.org e-Print Archive Open Access | DOI Resolver Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Article Open Access OPEN

Identifying and exploiting homogeneous communities in labeled networks
Citraro S., Rossetti G.
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 evaluate Eva on several real-world labeled networks carrying both nominal and ordinal information, and we compare our approach to other classic and attribute-aware algorithms. Our results suggest that Eva is the only method, among the compared ones, able to discover homogeneous clusters without considerably degrading partition modularity.We also investigate two well-defined applicative scenarios to characterize better Eva: i) the clustering of a mental lexicon, i.e., a linguistic network modeling human semantic memory, and (ii) the node label prediction task, namely the problem of inferring the missing label of a node.Source: Applied network science 5 (2020). doi:10.1007/s41109-020-00302-1
DOI: 10.1007/s41109-020-00302-1
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Conference object Open Access OPEN

Capturing Political Polarization of Reddit Submissions in the Trump Era
Morini V., Pollacci L., Rossetti G.
The American political situation of the last years, combined with the incredible growth of Social Networks, led to the diffusion of political polarization's phenomenon online. Our work presents a model that attempts to measure the political polarization of Reddit submissions during the first half of Donald Trump's presidency. To do so, we design a text classification task: Political polarization of submissions is assessed by quantifying those who align themselves with pro-Trump ideologies and vice versa. We build our ground truth by picking submissions from subreddits known to be strongly polarized. Then, for model selection, we use a Neural Network with word embeddings and Long Short Time Memory layer and, finally, we analyze how model performances change trying different hyper-parameters and types of embeddings.Source: SEBD 2020 28th SYMPOSIUM ON ADVANCED DATABASE SYSTEMS, pp. 80–87, Villasimius, Sardinia, Italy, June 21-24, 2020
Project(s): SoBigData-PlusPlus via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2020 Part of book or chapter of book Open Access OPEN

"Know thyself" how personal music tastes shape the last.fm online social network
Guidotti R., Rossetti G.
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 applied our model to 30k users of Last.Fm for which we collected both friendship ties and multiple listening. Starting from such rich data we performed an analysis whose final aim was twofold: (i) capture, and characterize, the individual dimension of music consumption in order to identify clusters of like-minded Last.Fm users; (ii) analyze if, and how, such clusters relate to the social structure expressed by the users in the service. Do there exist individuals having similar Personal Listening Data Models? If so, are they directly connected in the social graph or belong to the same community?.Source: Formal Methods. FM 2019 International Workshops Porto, Portugal, October 7-11, 2019, Revised Selected Papers, Part I, edited by Sekerinski E. et al., pp. 146–161, 2020
DOI: 10.1007/978-3-030-54994-7_11
Project(s): Track and Know via OpenAIRE, SoBigData via OpenAIRE

See at: ISTI Repository Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | link.springer.com Restricted | Unknown Repository Restricted | CNR ExploRA Restricted


2020 Conference object Open Access OPEN

CDlib: A python library to extract, compare and evaluate communities from complex networks
Rossetti G., Milli L., Cazabet R.
In the last decades, the analysis of complex networks has received increasing attention from several, heterogeneous fields of research. One of the hottest topics in network science is Community Discovery (henceforth CD), the task of clustering network entities belonging to topological dense regions of a graph. Although many methods and algorithms have been proposed to cope with this problem, and related issues such as their evaluation and comparison, few of them are integrated into a common software framework, making hard and time-consuming to use, study and compare them. Only a handful of the most famous methods are available in generic libraries such as NetworkX and Igraph. To cope with this issue, we introduce a novel library designed to easily select/apply community discovery methods on network datasets, evaluate/compare the obtained clustering and visualize the results.Source: The 11th Conference on Network Modeling and Analysis, October 14 - 15, 2020

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Part of book or chapter of book Open Access OPEN

Challenges in community discovery on temporal networks
Cazabet R., Rossetti G.
Community discovery is one of the most studied problems in network science. In recent years, many works have focused on discovering communities in temporal networks, thus identifying dynamic communities. Interestingly, dynamic communities are not mere sequences of static ones; new challenges arise from their dynamic nature. Despite the large number of algorithms introduced in the literature, some of these challenges have been overlooked or little studied until recently. In this chapter, we will discuss some of these challenges and recent propositions to tackle them. We will, among other topics, discuss of community events in gradually evolving networks, on the notion of identity through change and the ship of Theseus paradox, on dynamic communities in different types of networks including link streams, on the smoothness of dynamic communities, and on the different types of complexity of algorithms for their discovery. We will also list available tools and libraries adapted to work with this problem.Source: Temporal Network Theory, edited by Holme P.; Saramäki J., pp. 181–197, 2019
DOI: 10.1007/978-3-030-23495-9_10

See at: arXiv.org e-Print Archive Open Access | Unknown Repository Open Access | link.springer.com Open Access | ISTI Repository Open Access | CNR ExploRA Open Access | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted | Unknown Repository Restricted


2019 Article Open Access OPEN

The italian music superdiversity. Geography, emotion and language: one resource to find them, one resource to rule them all
Pollacci L., Guidotti R., Rossetti G., Giannotti F., Pedreschi D.
Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs' melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.Source: Multimedia tools and applications (Dordrecht. Online) 78 (2019): 3297–3319. doi:10.1007/s11042-018-6511-6
DOI: 10.1007/s11042-018-6511-6
Project(s): SoBigData via OpenAIRE

See at: Archivio della Ricerca - Università di Pisa Open Access | ISTI Repository Open Access | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | link.springer.com Restricted | Multimedia Tools and Applications Restricted | Multimedia Tools and Applications Restricted | CNR ExploRA Restricted


2019 Report Open Access OPEN

ISTI Young Researcher Award "Matteo Dellepiane" - Edition 2019
Barsocchi P., Candela L., Crivello A., Esuli A., Ferrari A., Girardi M., Guidotti R., Lonetti F., Malomo L., Moroni D., Nardini F. M., Pappalardo L., Rinzivillo S., Rossetti G., Robol L.
The ISTI Young Researcher Award (YRA) selects yearly the best young staff members working at Institute of Information Science and Technologies (ISTI). This award focuses on quality and quantity of the scientific production. In particular, the award is granted to the best young staff members (less than 35 years old) by assessing their scientific production in the year preceding the award. This report documents the selection procedure and the results of the 2019 YRA edition. From the 2019 edition on the award is named as "Matteo Dellepiane", being dedicated to a bright ISTI researcher who prematurely left us and who contributed a lot to the YRA initiative from its early start.Source: ISTI Technical reports, 2019

See at: ISTI Repository Open Access | CNR ExploRA Open Access


2019 Article Open Access OPEN

CDLIB: a python library to extract, compare and evaluate communities from complex networks
Rossetti G., Milli L., Cazabet R.
Community Discovery is among the most studied problems in complex network analysis. During the last decade, many algorithms have been proposed to address such task; however, only a few of them have been integrated into a common framework, making it hard to use and compare different solutions. To support developers, researchers and practitioners, in this paper we introduce a python library - namely CDlib - designed to serve this need. The aim of CDlib is to allow easy and standardized access to a wide variety of network clustering algorithms, to evaluate and compare the results they provide, and to visualize them. It notably provides the largest available collection of community detection implementations, with a total of 39 algorithms.Source: Applied network science 4 (2019). doi:10.1007/s41109-019-0165-9
DOI: 10.1007/s41109-019-0165-9
Project(s): SoBigData via OpenAIRE

See at: Applied Network Science Open Access | Applied Network Science Open Access | appliednetsci.springeropen.com Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Hyper Article en Ligne Open Access | Hyper Article en Ligne Open Access | Hyper Article en Ligne Open Access | Applied Network Science Open Access | Applied Network Science Open Access | Applied Network Science Open Access | ISTI Repository Open Access | CNR ExploRA Open Access


2019 Conference object Open Access OPEN

A complex network approach to semantic spaces: How meaning organizes itself
Citraro S., Rossetti G.
We propose a complex network approach to the emergence of word meaning through the analysis of semantic spaces: NLP techniques able to capture an aspect of meaning based on distributional semantic theories, so that words are linked to each other if they can be substituted in the same linguistic contexts, forming clusters representing semantic fields. This approach can be used to model a mental lexicon of word similarities: a graph G = (N, L) where N are words connected by some type of semantic or associative property L. Networks extracted from a baseline neural language model are analyzed in terms of global properties: they are small world and the probability of degree distribution follows a truncated power law. Moreover, they throw in a strong degree assortativity, a peculiarity that introduces us to the problem of semantic field identification. We support the idea that semantic fields can be identified exploiting the topological information of networks. Several community discovery methods have been tested, identifying from time to time strict semantic fields as crisp communities, linguistic contexts as overlapping communities or meaning conveyed by single words as communities produced starting from a seed-set expansion.Source: Italian Symposium on Advanced Database Systems, Castiglione Della Pescaia (GR), 19-19/6/2019
Project(s): SoBigData via OpenAIRE

See at: ceur-ws.org Open Access | ISTI Repository Open Access | CNR ExploRA Open Access