18 research outputs found
Hypernode Graphs for Spectral Learning on Binary Relations over Sets
Paper accepted for publication at ECML/PKDD 2014International audienceWe introduce hypernode graphs as weighted binary relations between sets of nodes: a hypernode is a set of nodes, a hyperedge is a pair of hypernodes, and each node in a hypernode of a hyperedge is given a non negative weight that represents the node contribution to the relation. Hypernode graphs model binary relations between sets of individuals while allowing to reason at the level of individuals. We present a spectral theory for hypernode graphs that allows us to introduce an unnormalized Laplacian and a smoothness semi-norm. In this framework, we are able to extend spectral graph learning algorithms to the case of hypernode graphs. We show that hypernode graphs are a proper extension of graphs from the expressive power point of view and from the spectral analysis point of view. Therefore hypernode graphs allow to model higher order relations whereas it is not true for hypergraphs as shown in~\cite{Agarwal2006}. In order to prove the potential of the model, we represent multiple players games with hypernode graphs and introduce a novel method to infer skill ratings from game outcomes. We show that spectral learning algorithms over hypernode graphs obtain competitive results with skill ratings specialized algorithms such as Elo duelling and TrueSkill
Hypernode Graphs for Learning from Binary Relations between Groups in Networks
International audienceThe aim of this paper is to propose methods for learning from interactions between groups in networks. We introduced hypernode graphs in Ricatte et al (2014) a formal model able to represent group interactions and able to infer individual properties as well. Spectral graph learning algorithms were extended to the case of hypern-ode graphs. As a proof-of-concept, we have shown how to model multiple players games with hypernode graphs and that spectral learning algorithms over hyper-node graphs obtain competitive results with skill ratings specialized algorithms. In this paper, we explore theoretical issues for hypernode graphs. We show that hypernode graph kernels strictly generalize over graph kernels and hypergraph kernels. We show that hypernode graphs correspond to signed graphs such that the matrix D â W is positive semidefinite. It should be noted that homophilic relations between groups may lead to non homophilic relations between individ-uals. Moreover, we also present some issues concerning random walks and the resistance distance for hypernode graphs
Skill Rating for Multiplayer Games Introducing Hypernode Graphs and their Spectral Theory
International audienceWe consider the skill rating problem for multiplayer games, that is how to infer player skills from game outcomes in multiplayer games. We formulate the problem as a minimization problem arg min s s T âs where â is a positive semidefinite matrix and s a real-valued function, of which some entries are the skill values to be inferred and other entries are constrained by the game outcomes. We leverage graph-based semi-supervised learning (SSL) algorithms for this problem. We apply our algorithms on several data sets of multiplayer games and obtain very promising results compared to Elo Duelling (see Elo, 1978) and TrueSkill (see Herbrich et al., 2006). As we leverage graph-based SSL algorithms and because games can be seen as relations between sets of players, we then generalize the approach. For this aim, we introduce a new finite model, called hypernode graph, defined to be a set of weighted binary relations between sets of nodes. We define Laplacians of hy-pernode graphs. Then, we show that the skill rating problem for multiplayer games can be formulated as arg min s s T âs where â is the Laplacian of a hypernode graph constructed from a set of games. From a fundamental perspective, we show that hypernode graph Laplacians are symmetric positive semidefinite matrices with constant functions in their null space. We show that problems on hypernode graphs can not be solved with graph constructions and graph kernels. We relate hypernode graphs to signed graphs showing that positive relations between groups can lead to negative relations between individuals
Hypernode Graphs for Learning from Binary Relations between Groups in Networks
The aim of this paper is to propose methods for learning from interactions between groups in networks. We propose a proper extension of graphs, called hypernode graphs as a formal tool able to model group interactions. A hypernode graph is a collection of weighted relations between two disjoint groups of nodes. Weights quantify the individual participation of nodes to a given relation. We define Laplacians and kernels for hypernode graphs and prove that they strictly generalize over graph kernels and hypergraph kernels. We then proceed to prove that hypernode graphs correspond to signed graphs such that the matrix D â W is positive semi-definite. As a consequence, homophilic relations between groups may lead to non homophilic relations between individuals. We also define the notion of connected hypernode graphs and a resistance distance for connected hypernode graphs. Then, we propose spectral learning algorithms on hypernode graphs allowing to infer node ratings or node labelings. As a proof of concept, we model multiple players games with hypernode graphs and we define skill rating algorithms competitive with specialized algorithms
Community detection for correlation matrices
A challenging problem in the study of complex systems is that of resolving,
without prior information, the emergent, mesoscopic organization determined by
groups of units whose dynamical activity is more strongly correlated internally
than with the rest of the system. The existing techniques to filter
correlations are not explicitly oriented towards identifying such modules and
can suffer from an unavoidable information loss. A promising alternative is
that of employing community detection techniques developed in network theory.
Unfortunately, this approach has focused predominantly on replacing network
data with correlation matrices, a procedure that tends to be intrinsically
biased due to its inconsistency with the null hypotheses underlying the
existing algorithms. Here we introduce, via a consistent redefinition of null
models based on random matrix theory, the appropriate correlation-based
counterparts of the most popular community detection techniques. Our methods
can filter out both unit-specific noise and system-wide dependencies, and the
resulting communities are internally correlated and mutually anti-correlated.
We also implement multiresolution and multifrequency approaches revealing
hierarchically nested sub-communities with `hard' cores and `soft' peripheries.
We apply our techniques to several financial time series and identify
mesoscopic groups of stocks which are irreducible to a standard, sectorial
taxonomy, detect `soft stocks' that alternate between communities, and discuss
implications for portfolio optimization and risk management.Comment: Final version, accepted for publication on PR
Biclustering fMRI time series
Tese de mestrado, CiĂȘncia de Dados, Universidade de Lisboa, Faculdade de CiĂȘncias, 2020Biclustering Ă© um mĂ©todo de anĂĄlise que procura gerar clusters tendo em conta simultaneamente as linhas e as colunas de uma matriz de dados. Este mĂ©todo tem sido vastamente explorado em anĂĄlise de dados genĂ©ticos. Apesar de diversos estudos reconhecerem as capacidades deste mĂ©todo de anĂĄlise em outras ĂĄreas de investigação, as Ășltimas duas dĂ©cadas tem sido marcadas por um nĂșmero elevado de estudos aplicados em dados genĂ©ticos e pela ausĂȘncia de uma linha de investigação que explore as capacidades de biclustering fora desta ĂĄrea tradicional Esta tese segue pistas que sugerem potencial no uso de biclustering em dados de natureza espaço-temporal. Considerando o contexto particular das neurociĂȘncias, esta tese explora as capacidades dos algoritmos de biclustering em extrair conhecimento das sĂ©ries temporais geradas por tĂ©cnicas de imagem por ressonĂąncia magnĂ©tica funcional (fMRI). Eta tese propĂ”e uma metodologia para avaliar a capacidade de algoritmos de biclustering em estudar dados fMRI, considerando tanto dados sintĂ©ticos como dados reais. Para avaliar estes algoritmos, usamos mĂ©tricas de avaliação interna. Os nossos resultados discutem o uso de diversas estratĂ©gias de busca, revelando a superioridade de estratĂ©gias exaustivos para obter os biclusters mais homogĂ©neos. No entanto, o elevado custo computacional de estratĂ©gias exaustivas ainda sĂŁo um desafio e Ă© necessĂĄrio pesquisa adicional para a busca eficiente de biclusters no contexto de anĂĄlise de dados fMRI. Propomos adicionalmente uma nova metodologia de anĂĄlise de biclusters baseada em algoritmos de descoberta de padrĂ”es para determinar os padrĂ”es mais frequentes presentes nas soluçÔes de biclustering geradas. Um bicluster nĂŁo Ă© mais que um hipervĂ©rtice num hipergrafo . Extrair padrĂ”es frequentes numa solução de biclustering implica extrair os hipervĂ©rtices mais significativos. Numa primeira abordagem, isto permite entender relaçÔes entre regiĂ”es do cĂ©rebro e traçar perfis temporais que mĂ©todos tradicionais de estudos de correlação nĂŁo sĂŁo capazes de detetar. Adicionalmente, o processo de gerar os biclusters permite filtrar ligaçÔes pouco interessantes, permitindo potencialmente gerar hipergrafos de forma eficiente. A questĂŁo final Ă© o que podemos fazer com este conhecimento. Conhecer a relação entre regiĂ”es do cĂ©rebro Ă© o objetivo central das neurociĂȘncias. Entender as ligaçÔes entre regiĂ”es do cĂ©rebro para vĂĄrios sujeitos permitem traçar perfis. Nesse caso, propomos uma metodologia para extrapolar biclusters para dados tridimensionais e efetuar triclustering. Adicionalmente, entender a ligação entre zonas cerebrais permite identificar doenças como a esquizofrenia, demĂȘncia ou o Alzheimer. Este trabalho aponta caminhos para o uso de biclustering na anĂĄlise de dados espaço-temporais, em particular em neurociĂȘncias. A metodologia de avaliação proposta mostra evidĂȘncias da eficĂĄcia do biclustering para encontrar padrĂ”es locais em dados de fMRI, embora mais trabalhos sejam necessĂĄrios em relação Ă escalabilidade para promover a aplicação em cenĂĄrios reais.The effectiveness of biclustering, simultaneous clustering of both rows and columns in a data matrix, has been primarily shown in gene expression data analysis. Furthermore, several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades witnessed many biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Following hints that suggest potentialities for biclustering on Spatiotemporal data, particularly in neurosciences, this thesis explores biclusteringâs capacity to extract knowledge from fMRI time series. This thesis proposes a methodology to evaluate biclustering algorithmsâ feasibility to study the fMRI signal, considering both synthetic and realworld fMRI datasets. In the absence of ground truth to compare bicluster solutions with a reference one, we used internal valuation metrics. Results discussing the use of different search strategies showed the superiority of exhaustive approaches, obtaining the most homogeneous biclusters. However, their high computational cost is still a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. We propose a new methodology for analyzing biclusters based on performing pattern mining algorithms to determine the most frequent patterns present in the generated biclustering solutions. A bicluster is nothing more than a hyperlink in a hypergraph. Extracting frequent patterns in a biclustering solution implies extracting the most significant hyperlinks. In a first approach, this allows to understand relationships between regions of the brain and draw temporal profiles that traditional methods of correlation studies cannot detect. Additionally, the process of generating biclusters allows filtering uninteresting links, potentially allowing to generate hypergraphs efficiently. The final question is, what can we do with this knowledge. Knowing the relationship between brain regions is the central objective of neurosciences. Understanding the connections between regions of the brain for various subjects allows one to draw profiles. In this case, we propose a methodology to extrapolate biclusters to threedimensional data and perform triclustering. Additionally, understanding the link between brain zones allows identifying diseases like schizophrenia, dementia, or Alzheimerâs. This work pinpoints avenues for the use of biclustering in Spatiotemporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of biclusteringâs effectiveness in finding local fMRI data patterns, although further work is needed regarding scalability to promote the application in real scenarios
IdentificaciĂłn de mĂșltiples intenciones y sus dependencias subsumidas en mĂșltiples utterances para el desarrollo de Chatbots
Los chatbots son sistemas de procesamiento de lenguaje natural con los que se puede
interactuar mediante una interfaz de texto o voz, y han sido adoptados en muchas
industrias para responder las preguntas y solicitudes de los usuarios a través de
interfaces de chat. Por ende, los chatbots tienen un valor comercial como asistentes
virtuales.
Tanto es asĂ que se estĂĄ trabajando en que los chatbots puedan comunicarse con los
usuarios de manera similar a la comunicaciĂłn que hay entre dos humanos; en otras
palabras, un usuario debe experimentar la sensaciĂłn de comunicarse con una
persona. A su vez, dado que los chatbots eliminan los factores humanos y estĂĄn
disponibles las 24 horas del dĂa, hay un incremento en la demanda de las capacidades
de inteligencia artificial para interactuar con los clientes. En este aspecto, la sensaciĂłn
de comunicarse con una persona puede ser lograda mediante la inclusión de técnicas
de comprensiĂłn del lenguaje natural, procesamiento del lenguaje natural, generaciĂłn
del lenguaje natural y aprendizaje automĂĄtico.
De este modo, los chatbots son capaces de interpretar una o varias intenciones
comunicativas en cada âutteranceâ de un usuario, siendo que un âutteranceâ es todo lo
que el usuario o chatbot mencionan mientras es su turno de hablar o escribir. AsĂ
mismo, los chatbots pueden asociar una o varias intenciones comunicativas a un
identificador de âutterancesâ que contiene varios âutterancesâ. Por ende, a partir del
âutteranceâ de un usuario, un chatbot es capaz de interpretar una o varias intenciones
comunicativas asociadas a un identificador de âutterancesâ, a travĂ©s del cual usa los
âutterancesâ contenidos para escoger o generar un âutteranceâ como respuesta al
usuario. No obstante, si bien un chatbot puede identificar mĂșltiples intenciones
comunicativas en un enunciado, de un usuario, con un âutteranceâ, no puede identificar
mĂșltiples intenciones comunicativas en un enunciado, de un usuario, que contenga
mĂșltiples âutterancesâ. En consecuencia, tampoco se ha investigado como encontrar
los âutterancesâ de respuesta del chatbot cuando se tiene mĂșltiples âutterancesâ.
Por lo descrito previamente, en este proyecto se propone la implementaciĂłn de una
herramienta para: identificar mĂșltiples intenciones comunicativas en mĂșltiples
âutterancesâ, identificar las dependencias entre intenciones, agrupar las intenciones a
partir de sus dependencias, identificar las dependencias entre los grupos de
intenciones respecto de los identificadores de âutterancesâ y los identificadores de
âutterancesâ respecto de los âutterancesâ. AdemĂĄs, para facilitar el uso de la
herramienta, se elabora una interfaz de programaciĂłn de aplicaciones que recibe
mĂșltiples âutterancesâ en forma de texto, y devuelve los âutterancesâ segmentados, las
intenciones identificadas, los grupos entre intenciones y los âutterancesâ de respuesta
del chatbot para cada grupo de intenciones.
Los resultados obtenidos evidencian que los enfoques utilizados son exitosos. Por
Ășltimo, se espera mejorar los resultados con tĂ©cnicas de inteligencia artificial y
computaciĂłn lingĂŒĂstica
Learning with Graphs using Kernels from Propagated Information
Traditional machine learning approaches are designed to learn from independent vector-valued data points. The assumption that instances are independent, however, is not always true. On the contrary, there are numerous domains where data points are cross-linked, for example social networks, where persons are linked by friendship relations. These relations among data points make traditional machine learning diffcult and often insuffcient. Furthermore, data points themselves can have complex structure, for example molecules or proteins constructed from various bindings of different atoms. Networked and structured data are naturally represented by graphs, and for learning we aimto exploit their structure to improve upon non-graph-based methods. However, graphs encountered in real-world applications often come with rich additional information. This naturally implies many challenges for representation and learning: node information is likely to be incomplete leading to partially labeled graphs, information can be aggregated from multiple sources and can therefore be uncertain, or additional information on nodes and edges can be derived from complex sensor measurements, thus being naturally continuous. Although learning with graphs is an active research area, learning with structured data, substantially modeling structural similarities of graphs, mostly assumes fully labeled graphs of reasonable sizes with discrete and certain node and edge information, and learning with networked data, naturally dealing with missing information and huge graphs, mostly assumes homophily and forgets about structural similarity. To close these gaps, we present a novel paradigm for learning with graphs, that exploits the intermediate results of iterative information propagation schemes on graphs. Originally developed for within-network relational and semi-supervised learning, these propagation schemes have two desirable properties: they capture structural information and they can naturally adapt to the aforementioned issues of real-world graph data. Additionally, information propagation can be efficiently realized by random walks leading to fast, flexible, and scalable feature and kernel computations. Further, by considering intermediate random walk distributions, we can model structural similarity for learning with structured and networked data. We develop several approaches based on this paradigm. In particular, we introduce propagation kernels for learning on the graph level and coinciding walk kernels and Markov logic sets for learning on the node level. Finally, we present two application domains where kernels from propagated information successfully tackle real-world problems
Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems
This PhD thesis thoroughly examines the utilization of deep learning
techniques as a means to advance the algorithms employed in the monitoring and
optimization of electric power systems. The first major contribution of this
thesis involves the application of graph neural networks to enhance power
system state estimation. The second key aspect of this thesis focuses on
utilizing reinforcement learning for dynamic distribution network
reconfiguration. The effectiveness of the proposed methods is affirmed through
extensive experimentation and simulations.Comment: PhD thesi