Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 20 for “"Graph Embedding"”.
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Graph Embedding and Nonlinear Dimensionality Reduction
… analysis (PCA) have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure. However, these methods often perform poorly when applied to data which does not lie exactly …
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Algoritmi avanzati per il Subgraph Isomorphism, Motif Discovery, e Graph Embedding su reti complesse
… alla scoperta di motivi in grafi temporali e all'embedding di grafi multiplex. Presentiamo ArcMatch, un nuovo algoritmo per il matching efficiente di sottografi in grafi etichettati, che permette di ottenere informazioni dettagliate su strutture come le interazioni proteina-proteina e le reti …
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Hyperbolic graph embedding of magnetoencephalography brain networks to study brain alterations in patients with subjective cognitive decline
… classification tasks. Using a Hyperbolic Graph Convolutional Network (HGCN), we embed functional brain connectivity graphs derived from magnetoencephalography data to a Poincare disk instead of traditional Euclidean space. The Poincare disk is a negatively curved unit disk that encourages …
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Question Answering on Dynamic Knowledge Graph for Chemistry
… applications. While the chemistry Knowledge Graph provides a solution for representing this data and information, it poses challenges for human users to access it efficiently. A Knowledge Graph Question Answering system is one of the solutions. However, due to the specific nature of the …
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Graph Representation Learning for Social Networks
… is quite challenging and expensive. Recently, graph embedding emerged to map networked data into low-dimensional representations, i.e. vector embeddings. These representations are fed into off-the-shelf machine learning algorithms to simplify and speed up graph analytic tasks. Given the immense …
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Complexity and Partitions
… of concrete classification problems such as Graph Embedding or Entailment (for propositional logic), this thesis systematically develops tools, in shape of the boolean hierarchy of NP-partitions and its refinements, for the qualitative analysis of the complexity of partitions generated by …
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Enriching Knowledge Graphs Using Machine Learning Techniques
A knowledge graph represents millions of facts and reliable information about people, places, and things. These knowledge graphs have proven their reliability and their usage for providing better search results; answering ambiguous questions regarding entities; and training semantic parsers to …
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ENHANCING DEEP LEARNING WITH SYMBOLIC DOMAIN KNOWLEDGE
… symbolic domain knowledge. We propose logic graph embedding frameworks, Logic Embedding Network with Semantic Regularization (LENSR) and Temporal-Logic Embedded Automata Framework (T-LEAF), which take propositional logic and linear temporal logic as inputs, respectively. Secondly, recent work …
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Vector Embedding Techniques for Player Behaviour in DOTA 2
… numerical format. This work explores three node embedding techniques to represent player behaviour in a compact vector form. I used data from the game DOTA 2 to produce vectors using three graph embedding methods, developed a testing framework, and conducted 270 experiments to explore the effect …
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Plane Permutations and their Applications to Graph Embeddings and Genome Rearrangements
… in many research fields. A map is a 2-cell embedding of a graph on an orientable surface. Motivated by a new way to read the information provided by the skeleton of a map, we introduce new objects called plane permutations. Plane permutations not only provide new insight into enumeration of …
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Performance Enhancement of Unified Recommendation and Knowledge Graph Completion Learning by Relation Rotation
… learning the recommendation and knowledge graph completion (KGC) tasks. Recent studies have established that considering the incomplete nature of knowledge graphs (KG) can further enhance the performance of RS. However, most existing MTL models depend on translation-based knowledge graph …
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Cayley maps for certain cyclic groups with odd generators
… for both oral and written presentation; A Cayley graph provides us with a discrete model for a finite group with specified generating set. It is desirable to represent such structures in their simplest form and also so that certain symmetries are emphasized. By simplest form, we mean to draw these …
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Graph-Based Machine Learning for Passive Network Reconnaissance within Encrypted Networks
… conditions. In contrast, we devise a bipartite graph-based representation to create network reconnaissance solutions that rely only on a single feature (e.g., the Internet protocol (IP) address field). We exploit a widely available feature set to provide network reconnaissance solutions that are …
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Injecting Inductive Biases into Distributed Representations of Text
… vector representations of text (a.k.a. embeddings), learned by neural networks, encode various (linguistic) knowledge. To encode this knowledge into the embeddings the common approach is to train a large neural network on large corpora. There is, however, a growing concern regarding the …
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Unravelling the complexity of metabolic networks
… of this thesis is the development of a novel graph embedding approach, based on low-order network motifs, that compares the structural properties of large numbers of biological networks simultaneously. This method was prototyped on a cohort of 383 bacterial networks, and provides powerful …
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Data-driven and Machine Learning approaches for exploration and inference of Biological Pathways in physiological and pathological states
… in this system. For this purpose, I utilise graph machine learning approaches including graph neural networks and knowledge graph embedding models. I investigate whether incorporating prior biological information can aid pathway prediction and, surprisingly, find that additional biological …
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Three essays on econometrics: Network estimators with applications and assessment of the effects of Covid-19 pandemic
… network. The goal is to reconstruct a (weighted) graph when we are not able to directly observe connections among variables. The first paper focuses on random vectors with multivariate Gaussian distribution. In this specific case, a graph embedding the conditional dependencies can be obtained from …
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Multi-facet graph mining with contextualized projections
… research is to develop a new generation of graph mining techniques, centered around my proposed idea of multi-facet contextualized projections, for more systematic, flexible, and scalable knowledge discovery around massive, complex, and noisy real-world context-rich networks across various …
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Graph feature engineering and coordinate-based learning for transferable and energy-efficient artificial intelligence
… framework for efficient and scalable graph representation learning is presented, emphasizing coordinate-based and explicit structural methods. The research addresses the limitations of Graph Neural Networks (GNNs) in resource-constrained environments, including edge devices and …
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Learning-based Attack and Defense on Recommender Systems
… we introduce a method to use a deep structure embedding approach that preserves highly nonlinear structural information and the dynamic aspects of user reviews to identify and cluster the spam users. It is worth mentioning that, in the experiment with real datasets, our method captures about …