In this thesis a known heuristic for decreasing a node's centrality scores while maintaining influence, called ROAM, is compared to a modified version specifically designed to decrease eigenvector centrality. The performances of these heuristics are also tested against the Shapley values of a cooperative game played over the considered network, where the game is such that influential nodes receive higher Shapley values. The modified heuristic performed at least as good as the original ROAM, and in some instances even better (especially when the terrorist network behind the World Trade Center attacks was considered). Both heuristics increased the influence score for a given targeted node when applied consecutively on the WTC network, and consequently the Shapley values increased as well. Therefore the Shapley value of the game considered in this thesis seems to be well suited for discovering individuals that are assumed to actively trying to evade social network analysis.

The aim of this work is to create a functional specification of mobile application newly emerging asocial network for generating meetings with friends. Generating will be divided into two phases. In the first phase, we will use a mathematical model which will generate appointments with those friends that the user meets the least. In the second phase, we will use a mathematical model and multi-criteria decision making process for invitation of maximum number of friends to this meeting. Using graph theory, we will develope an algorithm which will optimize the computing system over all its users. And finally, we will define functionality for meeting friends in partner establishments in the city, which will use the principle of finding the shortest path.

La mémoire épisodique correspond à la reviviscence consciente d'expériences personnelles ancrées dans un contexte spécifique. Ce travail de thèse porte sur l'étude des processus cognitifs et des mécanismes neuronaux du rappel épisodique chez l'Homme. Les souvenirs rappelés par les odeurs sont plus détaillés et plus émotionnels que ceux évoqués par d'autres modalités sensorielles. Ces spécificités expliquent pourquoi nous nous intéressons à l'évocation des souvenirs par des odeurs. Tout d'abord, une tâche comportementale novatrice est développée pour permettre l'étude contrôlée de la mémoire d'épisodes complexes constitués d'odeurs non familières (Quoi), localisées à des emplacements distincts (Où), d'un environnement visuel donné (Quel contexte). A l'aide de cette tâche, nous montrons que, lorsque les dimensions d'un épisode sont étroitement liées, la perception de l'odeur permet le rappel de l'ensemble du souvenir. Le rappel épisodique est essentiellement fondé sur des processus de recollection, la familiarité n'étant pas suffisante pour récupérer l'ensemble du souvenir. De plus, les odeurs associées à une émotion, quelle que soit leur valence, facilitent le rappel épisodique correct. Fonctionnellement, la mémoire épisodique est sous-tendue par un large réseau neuronal, constitué de régions typiquement impliquées dans la mémoire de laboratoire et la mémoire autobiographique. Les souvenirs corrects sont associés à un réseau neuronal différent des souvenirs incorrects, de la perception de l'odeur à la ré-expérience du souvenir. Des analyses de modularité indiquent que les interactions fonctionnelles au sein du réseau de la mémoire épisodique dépendent également de l'exactitude du souvenir. L'ensemble de ces travaux suggère que le rappel épisodique est un processus dynamique complexe, initié dès la perception des odeurs, et interdépendant d'autres systèmes de mémoire tels que les mémoires perceptive et sémantique / Episodic memory is the memory that permits the conscious re-experience of specific personal events and associated with a specific context. This doctoral research aims at investigating the cognitive processes and the neural bases of episodic retrieval in humans. Odor-evoked memories are known to be more detailed and more emotional than memories triggered by other sensorial cues. These specificities explain why we studied odor-evoked memories. First, a novel behavioral task has been designed to study in a controlled way the memory of complex episodes comprising unfamiliar odors (What), localized spatially (Where), within a visual context (Which context). From this approach, we suggest that when the binding between the episodes’ dimensions is strong, the odor perception evokes the whole episodic memory. The episodic retrieval is mainly based on recollection processes, the feeling of knowing being insufficient to induce complete memory recovery. Moreover, emotion carried by odors, whatever its valence, promote accurate episodic retrieval. Functionally, episodic memory is underpinned by a distributed network, constituted of regions typically found in laboratory and autobiographical memory approaches. Accurate memories are associated with a specific neural network, from odor perception to memory re-experience. Modularity analyses show that neural interactions within this network also depend on memory accuracy. Altogether, results of this research suggest that episodic retrieval is a dynamic and complex process, triggered by odors perception, closely linked to other memory systems such as perceptual and semantic memories

Orientadores: Pedro Jussieu de Rezende, Alexandre Xavier Falcão / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação / Made available in DSpace on 2018-08-18T13:40:27Z (GMT). No. of bitstreams: 1 CasteloFernandez_CesarChristian_M.pdf: 2721705 bytes, checksum: 0d764319e69f64e1b806f60bbbf54b92 (MD5) Previous issue date: 2011 / Resumo: O Reconhecimento de Padrões pode ser definido como a capacidade de identificar a classe de algum objeto dentre um dado conjunto de classes, baseando-se na informação fornecida por amostras conhecidas (conjunto de treinamento). Nesta dissertação, o foco de estudo é o paradigma de classificação supervisionada, no qual se conhece a classe de todas as amostras utilizadas para o projeto do classificador. Especificamente, estuda-se o Classificador baseado em Floresta de Caminhos Ótimos (Optimum-Path Forest - OPF) e propõem três novos algoritmos de aprendizado, os quais representam melhorias em comparação com o Classificador OPF tradicional. Primeiramente, é desenvolvida uma metodologia simples, porém efetiva, para detecção de outliers no conjunto de treinamento. O método visa uma melhoria na acurácia do Classificador OPF tradicional através da troca desses outliers por novas amostras do conjunto de avaliação e sua exclusão do processo de aprendizagem. Os outliers são detectados computando uma penalidade para cada amostra baseada nos seus acertos e erros na classificação, o qual pode ser medido através do número de falsos positivos/negativos e verdadeiros positivos/negativos obtidos por cada amostra. O método obteve uma melhoria na acurácia em comparação com o OPF tradicional, com apenas um pequeno aumento no tempo de treinamento. Em seguida, é proposto um aprimoramento ao primeiro algoritmo, que permite detectar com maior precisão os outliers presentes na base de dados. Neste caso, utiliza-se a informação de falsos positivos/negativos e verdadeiros positivos/negativos de cada amostra para explorar intrinsecamente as relações de adjacência de cada amostra e determinar se é outlier. Uma inovação do método é que não existe necessidade de se computar explicitamente tal adjacência, como é feito nas técnicas tradicionais, o qual pode ser inviável para grandes bases de dados. O método obteve uma boa taxa de detecção de outliers e um tempo de treinamento muito baixo em vista do tamanho das bases de dados utilizadas. Finalmente, é abordado o problema de se selecionar um úmero tão pequeno quanto possível de amostras de treinamento e se obter a maior acurácia possível sobre o conjunto de teste. Propõe-se uma metodologia que se inicia com um pequeno conjunto de treinamento e, através da classificação de um conjunto bem maior de avaliação, aprende quais amostras são as mais representativas para o conjunto de treinamento. Os resultados mostram que é possível obter uma melhor acurácia que o Classificador OPF tradicional ao custo de um pequeno incremento no tempo de treinamento, mantendo, no entanto, o conjunto de treinamento menor que o conjunto inicial, o que significa um tempo de teste reduzido / Abstract: Pattern recognition can be defined as the capacity of identifying the class of an object among a given set of classes, based on the information provided by known samples (training set). In this dissertation, the focus is on the supervised classification approach, for which we are given the classes of all the samples used in the design of the classifier. Specifically, the Optimum-Path Forest Classifier (OPF) is studied and three new learning algorithms are proposed, which represent improvements to the traditional OPF classifier. First of all, a simple yet effective methodology is developed for the detection of outliers in a training set. This method aims at improving OPF's accuracy through the swapping of outliers for new samples from the evaluating set and their exclusion from the learning process itself. Outliers are detected by computing a penalty for each sample based on its classification-hits and -misses, which can be measured through the number of false positive/negatives and true positives/negatives obtained by each sample. The method achieved an accuracy improvement over the traditional OPF, with just a slight increment in the training time. An improvement to the first algorithm is proposed, allowing for a more precise detection of outliers present in the dataset. In this case, the information on the number of false positive/negatives and true positives/negatives of each sample is used to explore the adjacency relations of each sample and determine whether it is an outlier. The method's merit is that there is no need of explicitly computing an actual vicinity, as the traditional techniques do, which could be infeasible for large datasets. The method achieves a good outlier detection rate and a very low training time, considering the size of the datasets. Finally, the problem of choosing a small number of training samples while achieving a high accuracy in the testing set is addressed. We propose a methodology which starts with a small training set and, through the classification of a much larger evaluating set, it learns which are the most representative samples for the training set. The results show that it is possible to achieve higher accuracy than the traditional OPF's at the cost of a slight increment in the training time, preserving, however, a smaller training set than the original one, leading to a lower testing time / Mestrado / Ciência da Computação / Mestre em Ciência da Computação

Orientador: Pedro José Catuogno / Dissertação (mestrado profissional) - Universidade Estadual de Campinas, Instituto de Matemática, Estatística e Computação Científica / Made available in DSpace on 2018-08-26T10:58:39Z (GMT). No. of bitstreams: 1 Fonte_CarlaCristina_M.pdf: 29679078 bytes, checksum: 0009a52938b1cb16c79bdc47af10d323 (MD5) Previous issue date: 2014 / Resumo: Neste trabalho, exploram-se os conceitos iniciais e aplicações importantes da teoria de grafos. Acentuam-se, nas aplicações, alguns problemas clássicos, como o das sete pontes de Königsberg, o do caixeiro viajante e o problema dos casamentos estáveis. Com o intuito de servir como material de apoio para a introdução de grafos ao ensino médio, expõe-se uma sugestão para plano de aula, cuja exploração sinaliza diversas propriedades matemáticas interessantes, além de estimular o raciocínio e o estudo / Abstract: This work focuses on the initial concepts and important applications of the graph theory. Detailing, in the applications, some classic problems such as the seven bridges of Königsberg problem, the travelling salesman problem and the stable marriage problem. In order to provide a supporting material for the introduction to graphs in high school, it is shown a suggestion to the lesson plan, which exploration indicates various interesting mathematical properties beyond stimulating the reasoning and the deep study in the field / Mestrado / Matemática em Rede Nacional / Mestra em Matemática em Rede Nacional

Orientador: Gabriela Castellano / Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-08-30T20:31:19Z (GMT). No. of bitstreams: 1 TapiaHerrera_LuisCarlos1982-_D.pdf: 17368597 bytes, checksum: b04bfdc96a80f7bba2cdca7390a9d09e (MD5) Previous issue date: 2016 / Resumo: Os neurônios são elementos que no cérebro trabalham em grupo e de forma organizada. A técnica de ressonância magnética funcional (fMRI) permite identificar redes corticais e subcorticais do cérebro quando ele desenvolve atividades cognitivas motoras ou perceptivas. No entanto, redes nomeadas de redes em estado de repouso, estão presentes em ausência de tarefas específicas. Alguns estudos modelaram redes funcionais do cérebro com a ajuda da teoria de grafos. Um dos objetivos deste trabalho foi analisar, utilizando teoria de grafos, dados funcionais do cérebro coletados com a técnica de fMRI, de 10 voluntários saudáveis, que participaram de dois protocolos: uma aquisição em estado de repouso e outra durante uma tarefa de produção de palavras. Outro objetivo do trabalho foi testar duas métricas matemáticas (correlação de Pearson e informação mútua), para determinar quais delas conseguem captar melhor diferenças entre as duas condições mencionadas. Também se objetivou comparar parâmetros termodinâmicos das redes de repouso obtidas por meio dos dados reais com os de redes simuladas computacionalmente via modelo de Ising. Finalmente, um último objetivo foi explorar os dados para ver que informação poderia ser obtida a partir dos mesmos, sem uso prévio de modelos sobre as tarefas realizadas. Utilizando a teoria de grafos, achamos diferenças entre as redes nas condições de repouso e de produção de palavras para os parâmetros grau médio e coeficiente de cluster. Adicionalmente foram comparadas as redes dos hemisférios direito e esquerdo nas redes geradas na condição de produção de palavras, e achamos que o grau médio das redes pode predizer a lateralização (dominância hemisférica para linguagem), também achada com análises padrões de fMRI. Relativo às métricas matemáticas, a correlação de Pearson e a informação mútua foram comparadas para determinar qual destas métricas captura melhor a similaridade ou sincronia entre duas séries temporais que contêm atividade hemodinâmica do cérebro. Concluímos que a correlação linear é uma medida capaz de caracterizar de forma satisfatória a sincronia entre duas séries desse tipo. Simulações computacionais do modelo de Ising foram desenvolvidas para posteriormente criar redes funcionais em três regimes diferentes: crítico, subcrítico e supercrítico. Esta abordagem do estado de repouso foi examinada em trabalhos prévios, e foi concluído que o cérebro como sistema dinâmico possui uma maior semelhança com o sistema simulado no regime crítico. Finalmente, uma metodologia independente de modelo foi implementada para detectar áreas ativas do cérebro em tarefas dirigidas. Esta metodologia foi testada nos dados na condição de produção de palavras, permitindo identificar as áreas envolvidas na execução da tarefa / Abstract: Neuronal elements in the brain are not isolated, they work together and work in an organized way. The functional magnetic resonance imaging (fMRI) technique allows identifying cortical networks when the brain develops a task. However, resting state brain networks are present in the absence of any task. Some studies have modeled the brain networks architecture with aid of graph theory. One of the main aims of this work was the analysis of resting state and language task fMRI data sets, of ten healthy subjects, using graph theory. In order to study the linear and nonlinear relationships between time series of cortical areas of the brain, two metrics were compared the Pearson correlation and the mutual information. Also, graphs parameters built from resting state data and graph parameters built using simulations of the Ising model were compared. Finally, we developed a methodology to study the time series of differents regions of the brain in order to obtain information of the task without using predefined models of the brain activity. We found differences in the mean degree and the cluster coefficient of the network between the two conditions. In addition, we compared the networks corresponding to the left and right hemispheres during the language task, and found that the mean degree of these networks can predict the language lateralization found with standard fMRI analysis in most cases. The mean degree of the network and the cluster coefficient shows differences for the two conditions. Relative to the comparison between the Pearson correlation and the mutual information, we conclude that the linear correlation is an efficient metric to characterize the synchrony between the haemodynamic time series of the brain. Computational simulations of the Ising model for three different phases were developed: in critical, subcritical and supercritical phases. This comparison was presented in a previous work, and it was concluded that the brain as a dynamical system has remarkable similarities with the computational model in the critical phase. Relatively to the model independent methodology developed, it was possible to identify brain areas engaged with the word production task / Doutorado / Física / Doutor em Ciências / 157356/2011-6 / CNPQ

Orientador: Carlos Lenz César / Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Física Gleb Wataghin / Made available in DSpace on 2018-09-01T14:53:04Z (GMT). No. of bitstreams: 1 Camacho_LudwingFerneyMarenco_M.pdf: 18964058 bytes, checksum: fa65aaa210a9f9f4dbd93261b64da143 (MD5) Previous issue date: 2017 / Resumo: Apresenta-se um novo método efetivo para analisar um sistema de Deputados usando o formalismo da teoria de redes. Construiu-se uma matriz com os resultados anuais da votação nominal da Câmara dos Deputados do Brasil desde 2007 até 2015. Através da medida do coeficiente de correlação entre os conjuntos anuais de votação nominal, calculou-se uma rede de Deputados. Encontrando a Árvore Geradora Mínima da rede de Deputados características generais do sistema podem visualiza-se. Especificamente, expõe-se a postura de concordância - oposição, as conexões individuais entre os Deputados, a fidelidade partidária e uma nova maneira de observar os projetos de lei aprovados ou rejeitados, assim como sua evolução no tempo. Devido ao bom comportamento de correlação observado entre os Deputados, prova-se que cinco ou seis partidos políticos são suficientes para capturar toda a diversidade política existente na Câmara dos Deputados do Brasil. Além disso, propõe-se que a distribuição de probabilidade dos valores de correlação da Câmara dos Deputados do Brasil é uma combinação de distribuições logísticas. Enuncia-se também, um novo método de ordenar matrizes de correlação baseado no resultado da Árvore Geradora Mínima / Abstract: A new effective method for analysing a Representatives¿ system from the network formalism is presented. A matrix with the annual results of roll - call vote of the Brazilian House of Representatives from 2007 to 2015 was constructed. By measuring the correlation coefficient between each pair of annual roll - call vote sets a Representatives¿ network was computed. For extracting the Minimal Spanning Tree of the Representatives' network general features of this system arises. Specifically, the concordance - opposition stance, the individual connections among Representatives, the partisan fidelity and a new way to identify the approved and disapproved draft bills, as well as, its time evolution are disclosed. A well-define correlation behaviour among Representatives is observed, in fact, we prove that five or six political parties are sufficient to encapsulate all political diversity in the Brazilian House of Representatives. In addition, we propose that the probability distribution of correlation values in the Brazilian House of Representatives is a combination of logistic distributions. Besides that, a new method for re-ordering correlation matrices based on the result of the Minimal Spanning Tree is enunciated / Mestrado / Física / Mestre em Física / 1490097/2015 / CAPES

Uma representação retangular de um grafo plano G é uma representação de G, onde cada vértice é desenhado como um retângulo de modo que dois retângulos devem compartilhar algum segmento de seus lados se e somente se existe uma aresta em G entre os vértices correspondentes aos retângulos. Ainda, a representação de G deve formar um retângulo e não deve existir buracos, ou seja, toda região interna deve corresponder a algum vértice de G. Um desenho retangular de um grafo plano H é um desenho de H, onde todas as arestas são desenhadas como segmentos horizontais ou verticais. Ainda, todas as faces internas são retângulos e as arestas que incidem na face externa também formam um retângulo. Nesta dissertação, apresentamos os principais trabalhos existentes na literatura para problemas associados à representação retangular. Também apresentamos resultados para problemas associados ao desenho retangular. Por fim, apresentamos o algoritmo que desenvolvemos para determinar as coordenadas dos vértices de um desenho retangular quando a orientação das arestas já foram determinadas. / A rectangular representation of a plane graph G is a representation of G, where each vertex is drawn as a rectangle, such as two rectangles have to share some boundary if and only if exist an edge in G between the corresponding vertices. Also, the representation of G must form a rectangle and does not contain any holes, in other words, every point inside the formed rectangle must correspond to some vertex of G. A rectangular drawing of a plane graph H is a drawing of H, where all edges are drawn either in vertical or in horizontal. Also, every internal face is a rectangle and the edges which are incident in the external face define a rectangle. In this dissertation, we present the main studies in the literature for problems associated with the rectangular representation. We also present results for problems associated with rectangular drawing. Finally, we present the algorithm we developed to determine the coordinates of the vertices of a rectangular drawing when the orientation of the edges have been determined.

La maladie de Huntington est une maladie neurodégénérative héréditaire qui est devenue un modèle d'étude pour comprendre la physiopathologie des maladies du cerveau associées à la production de protéines mal conformées et à la neurodégénérescence. Bien que plusieurs mécanismes aient été mis en avant pour cette maladie, dont plusieurs seraient aussi impliqués dans des pathologies plus fréquentes comme la maladie d’Alzheimer ou la maladie de Parkinson, nous ne savons toujours pas quels sont les mécanismes ou les profils moléculaires qui déterminent fondamentalement la dynamique des processus de dysfonction et de dégénérescence neuronale dans cette maladie. De même, nous ne savons toujours pas comment le cerveau peut résister aussi longtemps à la production de protéines mal conformées, ce qui suggère en fait que ces protéines ne présentent qu’une toxicité modérée ou que le cerveau dispose d'une capacité de compensation et de résilience considérable. L'hypothèse de mon travail de thèse est que l'intégration de données génomiques et transcriptomiques au travers des modèles qui récapitulent différentes phases biologiques de la maladie de Huntington peut permettre de répondre à ces questions. Dans cette optique, l'utilisation des réseaux de gènes et la mise en application de concepts issus de la théorie des graphes sont particulièrement bien adaptés à l'intégration de données hétérogènes, au travers des modèles et au travers des espèces. Les résultats de mon travail suggèrent que l'altération précoce (avant les symptômes, avant la mort cellulaire) et éventuellement dès le développement cérébral) des grandes voies de développement et de maintenance neuronale, puis la persistance voire l'aggravation de ces effets, sont à la base des processus physiopathologiques qui conduisent à la dysfonction puis à la mort neuronale. Ces résultats permettent aussi de prioriser des gènes et de générer des hypothèses fortes sur les cibles thérapeutiques les plus intéressantes à étudier d'un point de vue expérimental. En conclusion, mes recherches ont un impact à la fois fondamental et translationnel sur l'étude de la maladie de Huntington, permettant de dégager des méthodes d'analyse et des hypothèses qui pourraient avoir valeur thérapeutique pour les maladies neurodégénératives en général. / Huntington’s disease is a hereditary neurodegenerative disease that has become a model to understand physiopathological mechanisms associated to misfolded proteins that ocurs in brain diseases. Despite exciting findings that have uncover pathological mechanisms occurring in this disease and that might also be relevant to Alzheimer’s disease and Parkinson’s disease, we still do not know yet which are the mechanisms and molecular profiles that rule the dynamic of neurodegenerative processes in Huntington’s disease. Also, we do not understand clearly how the brain resist over such a long time to misfolded proteins, which suggest that the toxicity of these proteins is mild, and that the brain have exceptional compensation capacities. My work is based on the hypothesis that integration of ‘omics’ data from models that depicts various stages of the disease might be able to give us clues to answer these questions. Within this framework, the use of network biology and graph theory concepts seems particularly well suited to help us integrate heterogeneous data across models and species. So far, the outcome of my work suggest that early, pre-symptomatic alterations of signaling pathways and cellular maintenance processes, and persistency and worthening of these phenomenon are at the basis of physiopathological processes that lead to neuronal dysfunction and death. These results might allow to prioritize targets and formulate new hypotheses that are interesting to further study and test experimentally. To conclude, this work shall have a fundamental and translational impact to the field of Huntington’s disease, by pinpointing methods and hypotheses that could be valuable in a therapeutic perspective.

Le sujet abordé dans cette thèse concerne la contrôlabilité d’une classe de systèmes spatio-temporels, entièrement discrets de type automates cellulaires (AC). Le but de cette étude est de mettre en lumière de nouvelles pistes pour prouver la contrôlabilité des systèmes complexes. Plus spécifiquement, cette thèse se focalise sur la contrôlabilité régionale qui consiste à se restreindre à une région du domaine où le système devra atteindre un objectif donné à travers des actions ciblées. Le cas d’AC Booléens a été particulièrement examiné tout au long de cette thèse. La première partie est consacrée à l’étude du problème de la contrôlabilité régionale des AC déterministes lorsque les actions sont exercées sur la frontière de la régioncontrôlée. Une première démarche que nous avons utilisée s’appuie sur les chaines de Markov et la contrôlabilité est caractérisée en établissant une matrice similaire à leur matrice de transition en utilisant les définitions d’une chaîne ergodique et régulière. Cette étude a été étendue au cas des AC probabilistes qui sont largement utilisés pour modéliser de nombreux phénomènes réels. Le même problème a été appréhendé en utilisant des outils de la théorie des graphes. Nous proposons des conditions nécessaires et suffisantes pour la contrôlabilité régionale des AC déterministes en utilisant les notions de circuit hamiltonien et de composante fortement connexe. Le contrôle qui assure la contrôlabilité régionale est défini à travers un algorithme préimages. La deuxième partie est dédiée au problème de la contrôlabilité régionale frontière des AC Booléens qui consiste à agir sur la frontière du domaine pour atteindre un objectif sur une région cible. Nous considérons d’abord des AC linéaires pour lesquels nousdonnons un résultat de caractérisation grâce à la condition de Kalman. Nous proposons un algorithme pour déterminer le contrôle qui permet de forcer l’apparition d’une configuration désirée dans la région d’étude. Le cas des AC non linéaires a été également considéré en utilisant un algorithme de recherche des préimages. / The issue addressed in this thesis concerns the controllability of a class of discrete spatio-temporal systems named cellular automata (CA). The purpose of this study is to highlight new ways to prove the controllability of complex systems. Morespecifically, this thesis focuses on regional controllability which consists in restricting the study to a subregion of the domain where the system will have to achieve a given objective through targeted actions. The case of Boolean CA have been particularly examined throughout this thesis. The first part is devoted to the study of the problem of the regional controllability of deterministic CAs when the actions are exerted on the boundaries of the controlled region. A first approach that we used relies on Markov chains and controllability is characterized by establishing a matrix similar to their transition matrix using the definitions of a regular and ergodic chain. This study has been extended to the case of probabilistic CAs that are widely used tomodel many real phenomena. The same problem has been apprehended using tools of graph theory. We proposenecessary and sufficient conditions for the regional controllability of deterministic CAs using the notions of Hamiltonian circuit and strongly connected component. The control that ensures regional controllability is defined through a preimage algorithm.The second part is devoted to the problem of the boundary regional controllability of Boolean CAs, which consists of acting on the boundary of the domain in order to reach a desired goal in a target region. We first consider linear CAs for which we givea characterization result using the Kalman condition. We propose an algorithm to determine the control that allows to force the appearance of a desired configuration in the study area. The case of nonlinear CAs was also considered using a preimagesearch algorithm.