Current unsupervised approaches for keyphrase extraction compute a single importance score for each candidate word by considering the number and quality of its associated words in the graph and they are not flexible enough to incorporate multiple types of information. For instance, nodes in a network may exhibit diverse connectivity patterns which are not captured by the graph-based ranking methods. To address this, we present a new approach to keyphrase extraction that represents the document as a word graph and exploits its structure in order to reveal underlying explanatory factors hidden in the data that may distinguish keyphrases from non-keyphrases. Experimental results show that our model, which uses phrase graph representations in a supervised probabilistic framework, obtains remarkable improvements in performance over previous supervised and unsupervised keyphrase extraction systems.

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Internet services, such as online shopping and chat apps, have been spreading significantly in recent years, generating substantial amounts of data. These data are precious for machine learning and consist of connections between different entities, such as users and items. These connections contain important information essential for ML models to exploit, and the need to extract this information from graphs gives rise to Graph Representation Learning. By training on these data using Graph Representation Learning methods, hidden information can be obtained, and services can be improved. Initially, the models used for Graph Representation Learning were unsupervised, such as the Deepwalk and Node2vec. These models originated from the field of Natural Language Processing. These models are easy to apply, but their performance is not satisfactory. On the other hand, while supervised models like GNN and GCN have better performance than unsupervised models, they require a huge effort to label the data and finetune the model. Nowadays, the datasets have become larger and more complex, which makes the burden heavier for applying these supervised models. A recent breakthrough in the field of Natural Language Processing may solve the problem. In the paper ‘Attention is all you need’, the authors introduce the Transformer model, which shows excellent performance in NLP. Considering that the field of NLP has many things in common with the GRL and the first supervised models all originated from NLP, it is reasonable to guess whether we can take advantage of the Transformer in improving the performance of the unsupervised model in GRL. Generating embedding for nodes in the graph is one of the significant tasks of GRL. In this thesis, the performance of the Transformer model on generating embedding is tested. Three popular datasets (Cora, Citeseer, Pubmed) are used in training, and the embedding quality is measured through node classification with a linear classification algorithm. Another part of the thesis is to finetune the model to determine the effect of model parameters on embedding accuracy. In this part, comparison experiments are conducted on the dimensions, the number of layers, the sample size, and other parameters. The experiments show that the Transformer model performs better in generating embedding than the original methods, such as the Deepwalk. Compared to supervised methods, it requires less finetuning and less training time. The characteristic of the Transformer model revealed from the experiments shows that it is a good alternative to the baseline model for embedding generation. Improvement may be made on the prepossessing and loss function of the model to get higher performance. / Internettjänster, som onlineshopping och chattappar, har spridits avsevärt de senaste åren och genererat betydande mängder data. Dessa data är värdefulla för maskininlärning och består av kopplingar mellan olika enheter, såsom användare och objekt. Dessa kopplingar innehåller viktig information som är väsentlig för ML-modeller att utnyttja, och behovet av att extrahera denna information från grafer ger upphov till Graph Representation Learning. Genom att träna på dessa data med hjälp av Graph Representation Learning-metoder kan dold information erhållas och tjänster kan förbättras. Till en början var modellerna som användes för Graph Representation Learning oövervakade, såsom Deepwalk och Node2vec. Dessa modeller härstammar från området Natural Language Processing. Dessa modeller är lätta att applicera, men deras prestanda är inte tillfredsställande. Å andra sidan, medan övervakade modeller som GNN och GCN har bättre prestanda än oövervakade modeller, kräver de en enorm ansträngning för att märka data och finjustera modellen. Numera har datamängderna blivit större och mer komplexa, vilket gör bördan tyngre för att tillämpa dessa övervakade modeller. Ett nyligen genomfört genombrott inom området Natural Language Processing kan lösa problemet. I tidningen ‘Attention is all you need’ introducerar författarna Transformer-modellen, som visar utmärkta prestanda i NLP. Med tanke på att området NLP har många saker gemensamt med GRL och att de första övervakade modellerna alla härstammar från NLP, är det rimligt att gissa om vi kan dra fördel av Transformatorn för att förbättra prestandan för den oövervakade modellen i GRL. Att generera inbäddning för noder i grafen är en av GRL:s viktiga uppgifter. I detta examensarbete testas transformatormodellens prestanda för att generera inbäddning. Tre populära datamängder (Cora, Citeseer, Pubmed) används i utbildningen, och inbäddningskvaliteten mäts genom nodklassificering med en linjär klassificeringsalgoritm. En annan del av avhandlingen är att finjustera modellen för att bestämma effekten av modellparametrar på inbäddningsnoggrannheten. I den här delen utförs jämförelseexperiment på dimensionerna, antalet lager, provstorleken och andra parametrar. Experimenten visar att Transformer-modellen presterar bättre när det gäller att generera inbäddning än de ursprungliga metoderna, såsom Deep-walk. Jämfört med övervakade metoder kräver det mindre finjustering och mindre träningstid. Den egenskap hos transformatormodellen som avslöjades från experimenten visar att den är ett bra alternativ till baslinjemodellen för inbäddningsgenerering. Förbättringar kan göras av modellens preposseing- och förlustfunktion för att få högre prestanda.

With increasing data and technology, multiple types of data from the same set of nodes have been generated. Since each data modality contains a unique aspect of the underlying mechanisms, multiple datatypes are integrated. In addition to multiple datatypes, networks are important to store information representing associations between entities such as genes of a protein-protein interaction network and authors of a citation network. Recently, some advanced approaches to graph-structured data leverage node associations and features simultaneously, called Graph Neural Network (GNN), but they have limitations for integrative approaches. The overall aim of this dissertation is to integrate multiple data modalities on graph-structured data to infer some context-specific gene regulation and predict outcomes of interest. To this end, first, we introduce a computational tool named CRINET to infer genome-wide competing endogenous RNA (ceRNA) networks. By integrating multiple data properly, we had a better understanding of gene regulatory circuitry addressing important drawbacks pertaining to ceRNA regulation. We tested CRINET on breast cancer data and found that ceRNA interactions and groups were significantly enriched in the cancer-related genes and processes. CRINET-inferred ceRNA groups supported the studies claiming the relation between immunotherapy and cancer. Second, we present SUPREME, a node classification framework, by comprehensively analyzing multiple data and associations between nodes with graph convolutions on multiple networks. Our results on survival analysis suggested that SUPREME could demystify the characteristics of classes with proper utilization of multiple data and networks. Finally, we introduce an attention-aware fusion approach, called GRAF, which fuses multiple networks and utilizes attention mechanisms on graph-structured data. Utilization of learned node- and association-level attention with network fusion allowed us to prioritize the edges properly, leading to improvement in the prediction results. Given the findings of all three tools and their outperformance over state-of-the-art methods, the proposed dissertation shows the importance of integrating multiple types of data and the exploitation of multiple graph structured data.

There are different phases in any design activity, one of them being concept generation. Research in automating the conceptual design process in planar mechanisms is always challenging due to the existence of many different elements and their endless combinations. There may be instances where designers arrive at a concept without considering all the alternatives. Computational synthesis aims to arrive at a design by considering the entire space of valid designs. Different researchers have adopted various methods to automate the design process that includes existence of similar graph grammar approaches. But few methods replicate the way humans’ design. An attempt is being made in the thesis in this direction and as a first step, we focus on representing and evaluating planar mechanisms designed using graph grammars. Graph grammars have been used to represent planar mechanisms but there are disadvantages in the methods currently available. This is due to the lack of information in understanding the details of a mechanism represented by the graph since the graphs do not include information about the type of joints and components such as revolute links, prismatic blocks, gears and cams. In order to overcome drawbacks in the existing methods, a novel representation scheme has been developed. In this method, labels and x, y position information in the nodes are used to represent the different mechanism types. A set of sixteen grammar rules that construct different mechanisms from the basic seed is developed, which implicitly represents a tree of candidate solutions. The scheme is tested to determine its capability in capturing the entire set of feasible planar mechanisms of one degree of freedom including Stephenson and double butterfly linkages. In addition to the representation, another important consideration is the need for an accurate and generalized evaluator for kinematic analysis of mechanisms which, given the lack of information, may not be possible with current design automation schemes. The approach employed for analysis is purely kinematic and hence the instantaneous center of rotation method is employed in this research. The velocities of pivots and links are obtained using the instant center method. Once velocities are determined, the vector polygon approach is used to obtain accelerations and geometrical intersection to determine positions of pivots. The graph grammar based analysis module is implemented in an existing object-oriented grammar framework and the results have found this to be superior to or equivalent to existing commercial packages such as Working Model and SAM for topologies consisting of four-bar loop chain with single degree of freedom. / text

Enterprises use live video streaming as a mean of communication. Streaming high-quality video to thousands of devices in a corporate network is not an easy task; the bandwidth requirements often exceed the network capacity. For that matter, Peer-To-Peer (P2P) networks have been proven beneficial, as peers can exchange content efficiently by utilizing the topology of the corporate network. However, such networks are dynamic and their topology might not always be known. In this project we propose ABD, a new dynamic graph representation learning approach, which aims to estimate the bandwidth capacity between peers in a corporate network. The architecture of ABDis adapted to the properties of corporate networks. The model is composed of an attention mechanism and a decoder. The attention mechanism produces node embeddings, while the decoder converts those embeddings into bandwidth predictions. The model aims to capture both the dynamicity and the structure of the dynamic network, using an advanced training process. The performance of ABD is tested with two dynamic graphs which were produced by real corporate networks. Our results show that ABD achieves better results when compared to existing state-of-the-art dynamic graph representation learning models. / Företag använder live video streaming för både intern och extern kommunikation. Strömmning av hög kvalitet video till tusentals tittare i ett företagsnätverk är inte enkelt eftersom bandbreddskraven ofta överstiger kapaciteten på nätverket. För att minska lasten på nätverket har Peer-to-Peer (P2P) nätverk visat sig vara en lösning. Här anpassar sig P2P nätverket efter företagsnätverkets struktur och kan därigenom utbyta video data på ett effektivt sätt. Anpassning till ett företagsnätverk är ett utmanande problem eftersom dom är dynamiska med förändring över tid och kännedom över topologin är inte alltid tillgänglig. I det här projektet föreslår vi en ny lösning, ABD, en dynamisk approach baserat på inlärning av grafrepresentationer. Vi försöker estimera den bandbreddskapacitet som finns mellan två peers eller tittare. Architekturen av ABD anpassar sig till egenskaperna av företagsnätverket. Själva modellen bakom ABD använder en koncentrationsmekanism och en avkodare. Attention mekanismen producerar node embeddings, medan avkodaren konverterar embeddings till estimeringar av bandbredden. Modellen fångar upp dynamiken och strukturen av nätverket med hjälp av en avancerad träningsprocess. Effektiviteten av ABD är testad på två dynamiska nätverksgrafer baserat på data från riktiga företagsnätverk. Enligt våra experiment har ABD bättre resultat när man jämför med andra state-of the-art modeller för inlärning av dynamisk grafrepresentation.

The last decade has brought with it a wave of innovative technology, shifting the channels through which creative content is created, consumed, and categorized. And, as our interactions with creative multimedia content shift towards online platforms, the sheer quantity of content on these platforms has necessitated the integration of algorithmic guidance in the discovery of these spaces. In this way, the recommendation algorithms that guide users' interactions with various art forms have been cast into the role of gatekeepers and begun to play an increasingly influential role in shaping the creation of artistic content. The work laid out in the following chapters fuses three major areas of research: graph representation learning, music information retrieval, and fairness as applied to the task of music recommendation. In recent years, graph neural networks (GNNs), a powerful new architecture which enables deep learning approaches to be applied to graph or network structures, have proven incredibly influential in the music recommendation domain. In tandem with the striking performance gains that GNNs are able to achieve, many of these systems, have been shown to be strongly influenced by the degree, or number of outgoing edges, of individual nodes. More concretely, recent works have uncovered disparities in the qualities of representations learned by state of the art GNNs between nodes which are strongly and weakly connected. Translating these findings to the sphere of recommender systems, where nodes and edges are used to represent the interactions between users and various items, these disparities in representation that are contingent upon a node's connectivity can be seen as a form of popularity bias. And, indeed, within the broader recommendation community, popularity bias has long been considered an open problem, in which recommender systems begin to favor mainstream content over, potentially more relevant, but niche or novel items. If left unchecked these algorithmic nudged towards previously popular content can create, intensify, and enforce negative cycles that perpetuate disparities in representation on both the user and the creator ends of the content consumption pipeline. Particularly in the recommendation of creative (e.g. musical) content, the downstream effects in these disparities of visibility can have genuine economic consequences for artists from under-represented communities. Thus, the problem of popularity bias is something that must be addressed from both a technical and societal perspective. And, as the influence of recommender systems continues to spread, the effects of this phenomenon only become more spurious, as they begin to have critical downstream effects that shape the larger ecosystems in which art is created. Thus, the broad focus of thesis is the mitigation of popularity bias in music recommendation. In order to tailor our exploration of this issue to the graph domain, we begin by formalizing the relationship between degree fairness and popularity bias. In doing so, we concretely define the notion of popularity, grounding it in the structural principles of an interaction network, and enabling us to design objectives that can mitigate the effects of popularity on representation learning. In our first work, we focus on understanding the effects of sampling on degree fairness in uni-partite graphs. The purpose of this work is to lay the foundation for the graph neural network model which will underlie our music recommender system. We then build off this first work by extending the initial fairness framework to be compatible with bi-partite graphs and applying it to the music domain. The motivation of this work is rooted in the notion of discovery, or the idea that users engage with algorithmic curation in order to find content that is both novel and relevant to their artistic tastes. We present the intrinsic relationship between discovery objectives and the presence of popularity bias, explaining that the presence of popularity bias can blind a system to the musical qualities that underpin the underlying needs of music listening. As we will explain in later sections, one of the key elements of this work is our ability to ground our fairness notion in the musical domain. Thus, we propose a domain-aware, individual fairness-based approach which addresses popularity bias in graph neural network (GNNs) based recommender systems. In order to facilitate this domain awareness, we perform extensive dataset augmentation, taking two state of the art music recommendation datasets and augmenting them with rich multi-modal node-level features. Finally, we ground our evaluation in the cold start setting, showing the importance of inductive methodologies in the music space. / La dernière décennie a apporté avec elle une vague de technologies innovantes, modifiant la manière dont le contenu créatif est créé, consommé et catégorisé. Et, à mesure que nos interactions avec les contenus multimédias créatifs se déplacent vers les plateformes en ligne, la quantité de contenu sur ces plateformes a nécessité l’intégration d’un guidage algorithmique dans la découverte de ces espaces. De cette façon, les algorithmes de recommandation qui guident les interactions des utilisateurs avec diverses formes d’art ont été jetés dans le rôle de gardiens et ont commencé à jouer un rôle de plus en plus influent dans l’élaboration de la création de contenu artistique. Le travail présenté dans les chapitres suivants fusionne trois grands domaines de recherche : l’apprentissage de la représentation graphique, la recherche d’informations musicales et l’équité appliquée à la tâche de recommandation musicale. Alors que l’influence des systèmes de recommandation continue de s’étendre et de s’intensifier, il est crucial de prendre en compte les effets en aval que les choix de conception peuvent avoir sur l’écosystème plus large de la création artistique. Ces dernières années, l’intégration des réseaux sociaux dans la tâche de recommandation musicale a donné naissance aux réseaux neuronaux de graphes (GNN), une nouvelle architecture capable de faire des prédictions sur les structures de graphes. Parallèlement aux gains miraculeux que les GNN sont capables de réaliser, bon nombre de ces systèmes peuvent également être la proie de biais de popularité, les forçant à privilégier le contenu grand public par rapport à des éléments potentiellement plus pertinents, mais de niche ou nouveaux. S’il n’est pas maîtrisé, ce cycle négatif peut perpétuer les disparités de représentation entre la musique d’artistes, de genres ou de populations minoritaires. Et, ce faisant, les disparités dans la visibilité des éléments peuvent entraîner des problèmes à la fois du point de vue des performances et de la société. L’objectif de la thèse est l’atténuation du biais de popularité. Premièrement, le travail formalise les liens entre l’équité individuelle et la présence d’un biais de popularité parmi les contenus créatifs. Ensuite, nous étendons un cadre d’équité individuelle, en l’appliquant au domaine de la recommandation musicale. Le coeur de cette thèse s’articule autour de la proposition d’une approche basée sur l’équité individuelle et sensible au domaine qui traite le biais de popularité dans les systèmes de recommandation basés sur les réseaux de 5 neurones graphiques (GNN). L’un des éléments clés de ce travail est notre capacité à ancrer notre notion d’équité dans le domaine musical. Afin de faciliter cette prise de conscience du domaine, nous effectuons une augmentation étendue des ensembles de données, en prenant deux ensembles de données de recommandation musicale à la pointe de la technologie et en les augmentant avec de riches fonctionnalités multimodales au niveau des noeuds. Enfin, nous fondons notre évaluation sur le démarrage à froid, montrant l’importance des méthodologies inductives dans l’espace musical.

L'application exponentielle duale est une façon d'encoder les matrices de Stokes d'une connexion sur un fibré trivial sur la sphère de Riemann avec deux pôles : un pôle double en 0 et un pôle simple en l'infini.On donne ici une formule pour l'application exponentielle duale comme une série formelle non commutative. D'autres généralisations de cette formule sont données. / The exponential dual map is a way to encode Stokes data of a connection on a trivial vector bundle on the Riemann sphere with two poles: one double pole at 0 and one simple pole at infinity.We give here a formula for the exponential dual map expressed as a non commutative serie. Others generalizations of this formula are given.

The thesis introduces the basic concepts of graph theory and graph representation both in mathematics and programming. Furthermore, it presents basic methods and problems of graphs searching and theory in general. There are presented graph data management capabilities of different database systems including those directly based on the graph theory. In the practical part, there is designed an efficient method of graphs traversing in PostgreSQL database. The method was tested and demonstrated by the graph search algorithms, coloring and isomorphism.