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Thematische Bibliographien / Node embeddings / Dissertationen

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Dissertationen zum Thema „Node embeddings“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 25. Juli 2025

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1

Yandrapally, Aruna Harini. "Combining Node Embeddings From Multiple Contexts Using Multi Dimensional Scaling." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1627658906149105.

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2

Li, Mengzhen. "Integration of Node Embeddings for Multiple Versions of A Network." Case Western Reserve University School of Graduate Studies / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=case1595435155975104.

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3

Sabo, Jozef. "Aplikace metody učení bez učitele na hledání podobných grafů." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445517.

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Goal of this master's thesis was in cooperation with the company Avast to design a system, which can extract knowledge from a database of graphs. Graphs, used for data mining, describe behaviour of computer systems and they are anonymously inserted into the company's database from systems of the company's products users. Each graph in the database can be assigned with one of two labels: clean or malware (malicious) graph. The task of the proposed self-learning system is to find clusters of graphs in the graph database, in which the classes of graphs do not mix. Graph clusters with only one cla

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Wåhlin, Lova. "Towards Machine Learning Enabled Automatic Design of IT-Network Architectures." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-249213.

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There are many machine learning techniques that cannot be performed on graph-data. Techniques such as graph embedding, i.e mapping a graph to a vector, can open up a variety of machine learning solutions. This thesis addresses to what extent static graph embedding techniques can capture important characteristics of an IT-architecture graph, with the purpose of embedding the graphs in a common euclidean vector space that can serve as the state space in a reinforcement learning setup. The metric used for evaluating the performance of the embedding is the security of the graph, i.e the time it wo

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5

Zhu, Xiaoting. "Systematic Assessment of Structural Features-Based Graph Embedding Methods with Application to Biomedical Networks." University of Cincinnati / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1592394966493963.

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6

Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Thesis, Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023/document.

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L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’all

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Jmila, Houda. "Dynamic resource allocation and management in virtual networks and Clouds." Electronic Thesis or Diss., Evry, Institut national des télécommunications, 2015. http://www.theses.fr/2015TELE0023.

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L’informatique en nuage (Cloud computing) est une technologie prometteuse facilitant la réservation et de l'utilisation des ressources d’une manière flexible et dynamique. En plus des ressources informatiques traditionnelles, les utilisateurs du Cloud attendent à ce que des ressources réseaux leurs soient dédiées afin de faciliter le déploiement des fonctions et services réseau. Ils souhaitent pouvoir gérer l'ensemble d'un réseau virtuel (VN) ou infrastructure. Ainsi, les fournisseurs du Cloud doivent déployer des solutions de provisionnement des ressources dynamiques et adaptatives afin d’all

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8

Lin, Christy. "Unsupervised random walk node embeddings for network block structure representation." Thesis, 2021. https://hdl.handle.net/2144/43083.

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There has been an explosion of network data in the physical, chemical, biological, computational, and social sciences in the last few decades. Node embeddings, i.e., Euclidean-space representations of nodes in a network, make it possible to apply to network data, tools and algorithms from multivariate statistics and machine learning that were developed for Euclidean-space data. Random walk node embeddings are a class of recently developed node embedding techniques where the vector representations are learned by optimizing objective functions involving skip-bigram statistics computed from rando

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9

Feng, Ming-Han, and 馮銘漢. "Multi-relational Network Embeddings Considering Link Structures and Node Attributes." Thesis, 2017. http://ndltd.ncl.edu.tw/handle/466w9j.

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碩士<br>國立臺灣大學<br>資訊網路與多媒體研究所<br>105<br>Multi-relational networks are ubiquitous in real world. It is, however, difficult to be analyzed due to the complex structure of the network. A plausible approach to analyze such network is to embed the entity information as an informative feature vector. However, present embedding methods either consider only single-relational information, or neglect the importance of structural information. In addition, some of them require fine-tuning of hyperparameters, which might not be feasible for an unsupervised embedding generation task. In this work we propose M

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10

Bandyopadhyay, Sambaran. "Representing Networks: Centrality, Node Embeddings, Community Outliers and Graph Representation." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4646.

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Networks are ubiquitous. We start our technical work in this thesis by exploring the classical concept of node centrality (also known as influence measure) in information networks. Like clustering, node centrality is also an ill-posed problem. There exist several heuristics and algorithms to compute the centrality of a node in a graph, but there is no formal definition of centrality available in the network science literature. Lately, researchers have proposed axiomatic frameworks for the centrality of a node in a network. However, these existing formal frameworks are not generic in nature in

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11

Aggarwal, Manasvi. "Embedding Networks: Node and Graph Level Representations." Thesis, 2020. https://etd.iisc.ac.in/handle/2005/4638.

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Graph neural networks gained significant attention for graph representation and classification in the machine learning community. For graph classification, different pooling techniques are introduced, but none of them has considered both the node's neighborhood and the long-range effects on nodes. For this, we propose a novel graph pooling layer R2POOL, which balances both the region-based and the graph-level importance of the nodes and retains the most informative nodes for the next coarser version of the graph. Further, we integrate R2POOL with our multi-level prediction and branch training

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12

"Numerical Performance of the Holomorphic Embedding Method." Master's thesis, 2018. http://hdl.handle.net/2286/R.I.50476.

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abstract: Recently, a novel non-iterative power flow (PF) method known as the Holomorphic Embedding Method (HEM) was applied to the power-flow problem. Its superiority over other traditional iterative methods such as Gauss-Seidel (GS), Newton-Raphson (NR), Fast Decoupled Load Flow (FDLF) and their variants is that it is theoretically guaranteed to find the operable solution, if one exists, and will unequivocally signal if no solution exists. However, while theoretical convergence is guaranteed by Stahl’s theorem, numerical convergence is not. Numerically, the HEM may require extended precision

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13

Liu, Hsien Jen, and 劉獻仁. "Node Fault Tolerant Hamiltonian Cycle Embedding in Honeycomb Rectangular Torus Network." Thesis, 2001. http://ndltd.ncl.edu.tw/handle/41779530434993281960.

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碩士<br>國立交通大學<br>資訊科學系<br>89<br>In this thesis, we propose a variation of Honeycomb Torus, called Honeycomb Rectangular Torus Networks.The Honeycomb Rectangular Torus is Hamlitonian and to remove a pair of nodes from each partite sets of graph, still has Hamlitonian cycle. Honeycomb Rectangular Torus Network is homogeneous graph. This property is very helpful, because we can reduce the complexity of the Honeycomb Rectangular Torus. The Honeycomb Rectangular Torus HReT(m,n) is defined in the paper written by Stojmenovic Honeycomb Rectangular

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14

DiCarlo, Cynthia F. "Embedding sensory preferences into toys to enhance play in toddlers with disabilities." 2004. http://louisdl.louislibraries.org/u?/NOD,123.

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Thesis (Ph. D.)--University of New Orleans, 2004.<br>Title from electronic submission form. "A dissertation ... in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Special Education and Habililative Services."--Dissertation t.p. Vita. Includes bibliographical references.

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Chien, Li-Hsin, and 錢力新. "Embedding of Complete Binary Trees onto Supercubes Faulty Nodes." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/76961995078735199080.

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16

Jiang, Feng Shr, and 江逢時. "Embedding of Generalized Fibonacci Cubes in Hypercubes with Faulty Nodes." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/77547631018104810862.

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碩士<br>國立臺灣科技大學<br>電機工程研究所<br>82<br>The generalized Fibonacci cubes ( abbreviated to GFCs ) were recently proposed as a class of interconnection topologies, which cover a spectrum ranging from regular graphs such as the hypercube to semi-regular graphs such as the second order Fibonacci cube. It has been shown that the kth order GFC of dimension n + k is equivalent to an n cube for 0 .ltoreq. n < k; and it is a proper subgraph of an n-cube for n. gtoreq. k. Thus, a kth order G

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17

"Exploration of a Scalable Holomorphic Embedding Method Formulation for Power System Analysis Applications." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.45022.

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abstract: The holomorphic embedding method (HEM) applied to the power-flow problem (HEPF) has been used in the past to obtain the voltages and flows for power systems. The incentives for using this method over the traditional Newton-Raphson based nu-merical methods lie in the claim that the method is theoretically guaranteed to converge to the operable solution, if one exists. In this report, HEPF will be used for two power system analysis purposes: a. Estimating the saddle-node bifurcation point (SNBP) of a system b. Developing reduced-order network equivalents for distribution systems. T

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Zou, Yun-Hao, and 鄒雲豪. "A study on the Path Embedding of Various Lengths in Crossed Cube with Faulty Nodes." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/23599247148482788071.

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碩士<br>國立勤益科技大學<br>資訊管理系<br>102<br>In computer science, network topology is important and widely discussed by researchers since it is essential for parallel and distributed computation. Recently, many network topologies have been proposed. Among these topologies, the hypercube is one of the most popular topologies since it has good properties such as regularity, symmetry, small diameter, strong connectivity, recursive structure, flexible partition, and relatively low link complexity. Not only is it ideally suited to both special-purpose and general-purpose tasks, but it can efficiently simulate

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19

RANJBAR, Fariba. "Bounds on the maximal number of corrupted nodes via Boolean Network Tomography." Doctoral thesis, 2021. http://hdl.handle.net/11573/1563383.

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In this thesis we are concentrating on identifying defective items in larger sets which is a main problem with many applications in real life situations, e.g., fault diagnosis, medical screening and DNA screening. We consider the problem of localizing defective nodes in networks through an approach based on Boolean Network Tomography (BNT), which is grounded on inferring informations from the Boolean outcomes of end-to-end measurement paths. In particular, we focus on the following three: • Studying Maximal Identifiability, which was recently introduced in BNT to measure the maximal number of

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