Social Network Analysis: From Graph Theory to Applications with Python

Social network analysis is the process of investigating social structures through the use of networks and graph theory. It combines a variety of techniques for analyzing the structure of social networks as well as theories that aim at explaining the underlying dynamics and patterns observed in these structures. It is an inherently interdisciplinary field which originally emerged from the fields of social psychology, statistics and graph theory. This talk will covers the theory of social network analysis, with a short introduction to graph theory and information spread. Then we will deep dive into Python code with NetworkX to get a better understanding of the network components, followed-up by constructing and implying social networks from real Pandas and textual datasets. Finally we will go over code examples of practical use-cases such as visualization with matplotlib, social-centrality analysis and influence maximization for information spread.Comment: Presented at PyCon'19 - Israeli Python Conference 201

Using global diversity and local topology features to identify influential network spreaders

AbstractIdentifying the most influential individuals spreading ideas, information, or infectious diseases is a topic receiving significant attention from network researchers, since such identification can assist or hinder information dissemination, product exposure, and contagious disease detection. Hub nodes, high betweenness nodes, high closeness nodes, and high k-shell nodes have been identified as good initial spreaders. However, few efforts have been made to use node diversity within network structures to measure spreading ability. The two-step framework described in this paper uses a robust and reliable measure that combines global diversity and local features to identify the most influential network nodes. Results from a series of Susceptible–Infected–Recovered (SIR) epidemic simulations indicate that our proposed method performs well and stably in single initial spreader scenarios associated with various complex network datasets

Αποτύπωση της συμπεριφοράς που σχετίζεται με τη χρήση ναρκωτικών και διερεύνηση των σχετιζόμενων δικτύων στους συμμετέχοντες του προγράμματος "Αριστοτέλης"

Το Πρόγραμμα ΑΡΙΣΤΟΤΕΛΗΣ αποτελεί ένα Πρόγραμμα Δευτερογενούς Πρόληψης, για την αντιμετώπιση της εξάπλωσης του ιού HIV στους Χρήστες Ενδοφλέβιων Ναρκωτικών (ΧΕΝ) της Αθήνας. Το πρόγραμμα χρησιμοποίησε μια μη-πιθανοθεωρητική μέθοδο αλυσιδωτής δειγματοληψίας, την «Καθοδηγούμενη από τους Αποκρινόμενους Δειγματοληψία» (Respondent-Driven Sampling, RDS), ώστε να διεισδύσει στο δύσκολα προσεγγίσιμο πληθυσμό των ΧΕΝ και να εξάγει αξιόπιστα αποτελέσματα. Σκοπός της συγκεκριμένης διπλωματικής εργασίας είναι η ανασκόπηση της RDS μεθοδολογίας και η εφαρμογή της για την αξιολόγηση της συμπεριφοράς που σχετίζεται με την ενδοφλέβια χρήση ναρκωτικών και τη διερεύνηση των χαρακτηριστικών των κοινωνικών δικτύων των συμμετεχόντων του προγράμματος ΑΡΙΣΤΟΤΕΛΗΣ. Ο πρώτος γύρος του ΑΡΟΣΤΟΤΕΛΗ πραγματοποιήθηκε το 2012 και συγκέντρωσε 1404 ΧΕΝ. Η παρούσα ανάλυση υπέδειξε ότι οι ΧΕΝ στην Αθήνα, είναι στενά συνδεδεμένοι και σχηματίζουν εκτενή δίκτυα. Τα δίκτυα αυτά χαρακτηρίζονται από ποικιλομορφία αναφορικά με συμπεριφορές που σχετίζονται με την κοινή χρήση συριγγών αλλά εμφανίζουν τάση προς το σχηματισμό εσω-ομαδικών δεσμών (in-group affiliation) αναφορικά με την ενδοφλέβια χρήση κατά τον τελευταίο μήνα, την κύρια ουσία χρήσης, τη συχνότητα και τη διάρκεια χρήσης. Η διερεύνηση του τρόπου με τον οποίο συνδέονται οι ΧΕΝ, με βάση συγκεκριμένα χαρακτηριστικά ενδοφλέβιας χρήσης, αποτελεί σημαντικό εργαλείο για την κατανόηση των οδών και των μοτίβων μετάδοσης του HIV και την εφαρμογή αποτελεσματικών παρεμβάσεων.Background: Hidden populations, such as Injecting Drug Users (IDUs), comprise a special part of the population. Due to their sensitive nature and risky behaviors, they frequently hold a key role concerning the spread of infectious diseases. However, these populations are hard to reach with the usual sampling methods. Respondent Driven Sampling (RDS) is a non-probability method that uses the social network of participants to penetrate the hidden population and make reliable inferences. Aim: ARISTOTLE was a large ‘seek, test, treat and retain’ intervention which employed RDS and was implemented as a response to the HIV outbreak among IDUs of Athens, Greece. The first round of ARISTOTLE was conducted in 2012 and recruited 1404 IDUs. This thesis aims to review the RDS methodology and apply it to assess injection risk behavior and network characteristics of IDUs recruited by the ARISTOTLE program. Methods: In RDS the sampling begins with a handful of seeds from the target population, which are offered incentives to recruit usually up to three participants, drawing from their social networks. The process is repeated for the new recruits until, after a few recruitment waves, the sample is independent of the choice of initial seeds (equilibrium). With appropriate methodology the sample can be corrected for over-representation of certain groups and for indices such as homophily and network size. Results: The analysis revealed that IDUs in Athens form large social networks which are diverse concerning syringe sharing behaviors but showed a tendency towards in-group affiliation with respect to injection within the last month, main substance of use, frequency and duration of injection drug use. Users who had injected within the past month and IDUs who reported injecting more than once a day had the highest homophily indices (H=0.434 and 0.304 respectively). Also, IDUs who reported injecting drugs divided with a used syringe most of the times/always in the past year, as well as at their last injection, had increased adjusted average network sizes (22.1 and 23.1 people respectively). Conclusions: Measuring how well IDUs are connected and whether they form network ties based on specific injection traits may be helpful for understanding routes and patterns of HIV transmission and, as a result, for designing effective interventions

Redes sociais: modelação e previsão de interações sociais

Mestrado em Engenharia e Gestão IndustrialAs redes sociais são como canais de disseminação de informação, de partilha de conhecimentos, decisões, comportamentos, riscos e crenças. A análise de redes é uma ferramenta com elevado potencial no mundo organizacional, permitindo representar casos reais e práticos de relações. Esta análise tem vindo a ser efetuada nas mais diversas matérias, como metodologia auxiliar nos processos de gestão, económicos e nas ciências sociais. Este trabalho apresenta em primeiro lugar uma revisão dos conceitos utilizados na análise de grafos, como se formam as redes e como estas se podem representar; de seguida, é descrita a aplicação das redes em diferentes áreas e por último são estudados dois casos de estudo. Através da análise de dois casos de estudo, “Os 50 Mais Poderosos da Economia Portuguesa, de 2012” e a “Rede de Ligação de Alunos, nas Distintas Áreas de Formação da Universidade de Aveiro”, explora-se os conceitos utilizados na análise de redes. A rede “Os 50 Mais Poderosos da Economia Portuguesa, de 2012” comprova que os indivíduos mais magnatas são aqueles que se manifestam mais influentes com um maior número de ligações a convergirem para eles. O caso de estudo “Rede de Ligação de Alunos, nas Distintas Áreas de Formação da Universidade de Aveiro” permite compreender o peso do uso de cada departamento no campus universitário de Aveiro.Social networks are channels that allow the dissemination of information, knowledge sharing, decisions, behaviours, risks and beliefs. Network analysis is a tool with great potential in the organizational world, enabling represent real cases and practical relations. This analysis is being use in several areas, such as additional methodology in management processes, economics and social sciences. First this work presents a review of the concepts used in the analysis of graphs, how networks are formed and how we can represent them, then, describes the implementation of networks in different areas and finally are studied two cases of study. Through the analysis of two case studies, "The 50 Most Powerful of the Portuguese Economy, in 2012" and "Network connection between production modules of courses" explores the concepts used in network analysis. The network "The 50 Most Powerful of the Portuguese economy, in 2012" proves that the tycoon individuals are the more influents and with more edges converging to them. The case study "Network Connection Students in Different Areas of Education, University of Aveiro" allows us to understand the weight of the usage of each department on campus of Aveiro

As estruturas globais e regionais do campo de pesquisa, desenvolvimento e inovação das doenças negligenciadas leishmaniose e tuberculose sob a ótica das redes complexas

Tese (doutorado)—Universidade de Brasília, Faculdade de Ciência da Informação, Programa de Pós-Graduação em Ciência da Informação, 2015.Na era da informação, do conhecimento e das mídias digitais, de que forma os padrões de relacionamento interpessoais registrados geram informações sobre a estrutura global e regional da pesquisa em doenças negligenciadas? Este estudo investiga as redes de coautoria de cientistas que trabalham em leishmaniose e em tuberculose, duas das principais doenças negligenciadas no contexto brasileiro e mundial, em busca de revelar de que forma vêm ocorrendo mudanças no universo de participação das pesquisas. Foi identificada a ascensão de pesquisadores de países como Brasil, Índia, China e África do Sul, que se tornaram não apenas relevantes, mas em certas áreas chegam a dominar a estrutura de colaboração em rede e a produtividade no campo observado. Os resultados apresentados possibilitam às agências de financiamento avaliar seu papel em relação aos objetivos de construção e desenvolvimento da capacidade científica, bem como a consistência de parcerias científicas em todo o mundo. Eles também permitem a avaliação dos mecanismos que apoiam e incentivam a investigação em países endêmicos. O estudo também realiza uma análise exploratória acerca dos resultados da aplicação de métodos de análise de redes complexas no universo analisado. Nesse sentido, a pesquisa avalia a criação de instrumentos que apoiem a tomada de decisão em saúde pública, buscando demonstrar como a utilização desses métodos, amparados pelo uso apropriado de tecnologias para tratamento de dados, apresenta alternativas promissoras na avaliação da ciência e das dinâmicas de redes de ciência e tecnologia. O estudo utilizou diferentes bases para a recuperação dos dados, a saber: o PubMed, a Web of Science e o SciELO. Foi analisada a dinâmica das redes de coautoria em publicações científicas além das palavras-chave, das revistas científicas e outros elementos que compõem os chamados metadados das publicações científicas. O resultado final é um conjunto de métodos que podem apoiar o estudo das comunidades científicas e dos grupos de pesquisa com base em seu comportamento específico quanto à comunicação e ao relacionamento entre seus pares.In the age of information, knowledge and digital media, how the registered interpersonal relationship patterns generate information on global and regional structure of the research in neglected diseases? This study investigates the networks of co-authorship of scientists working on leishmaniasis and tuberculosis, two of the major neglected diseases in the Brazilian and global context, seeking to reveal how changes are taking place in the universe of shared research. It has been identified the rise of researchers from countries such as Brazil, India, China and South Africa, which became not only relevant, but in certain areas come to dominate the networked collaborative structure and productivity in the observed field. The presented results allow the funding agencies to assess its role in relation to construction and development goals of scientific capacity as well as the consistency of scientific partnerships worldwide. They also allow the evaluation of the mechanisms that support and encourage research in endemic countries. The study also conducts an exploratory analysis of the results of the application analysis of complex networks methods in the universe analyzed. In this sense, the research evaluates the development of tools to support public health decisions demonstrating how the use of these methods, supported by appropriate use of technologies for data processing, offers promising alternatives in the evaluation of science and network dynamics on science and technology. The study used different databases for data retrieval, namely: PubMed, Web of Science and SciELO. The dynamics of co-authorship networks in scientific publications was analyzed in addition to keywords, scientific journals and other elements that make up the so-called metadata of scientific publications. The end result is a set of methods that can support the study of scientific communities and research groups based on their specific behavior as communication and the relationship among their peers

Influence of Local Information on Social Simulations in Small-World Network Models

As part of Watts and Strogatz's small-world model of complex networks, local information mechanisms such as landscape properties are used to approximate real-world conditions in social simulations. The authors investigated the influence of local information on social simulations based on the small-world network model, using a cellular automata variation with added shortcuts as a test platform for simulating the spread of an epidemic disease or cultural values/ideas. Results from experimental simulations show that the percentage of weak individuals should be considered significant local information, but vertex degree influences and the distribution patterns of weak individuals should not. When exploring contagion problems, the results encourage a future emphasis on setting and the proportions of specific values of local information related to infection strength or resistance, and a reduced emphasis on the detailed topological structure of small-world network models and the distribution patterns of specific values of local information