rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Topology and Position Aware Graph Neural Networks
  name:
    name part: Liu, Yi
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Ji, Shuiwang
    role:
      role term: thesis advisor.
  name:
    name part: Texas A&M University
    role:
      role term: degree granting institution.
  type of resource: text
  genre: bibliography
  origin info:
    place:
      place term: txu
    date issued: 2023
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource.
  abstract: In machine learning, real-world objects such as molecules and proteins are naturally formulated as graphs. Two types of information are vital for graph representation learning; those are, topology information and position information. Basically, topology information shows connections between nodes, and position information further determines the final conformation of real-world objects. With the advances of deep learning, graph neural networks (GNNs) are intensively studied for learning from graph data. However, the number of neighboring units is not fixed, thus the spatial locality is not well-defined in graphs. Hence, it is challenging to apply deep learning operations such as pooling to graphs with preserving topology information. In addition, regular GNNs fail to incorporate the important 3D position information in real-world objects, leading to incomplete data representations. To tackle these challenges, we propose topology and position aware graph neural networks to explicitly integrate the important topology and position information. In the category of topology aware graph neural networks, we aim to investigate regular deep learning operations such as convolution and pooling on real-world graphs with preserving topology information. Generally, convolution operation is well-studied while pooling is a challenging yet less explored area. Due to its nature of lacking spatial locality, pooling on the graph usually fails to encode topology information. We propose a novel graph pooling method to explicitly consider graph topology. Our method is essentially a two-stage voting process, which is composed of local voting and global voting. By performing local voting, our method selects important nodes from neighborhood and works similarly as a locality-based max pooling operation on images and texts. In addition, we further incorporate graph topology in global voting to weigh each node globally in the entire graph. Thus, the final pooling is realized by selecting import…
  note: by Yi Liu.
  note: "Major Subject: Computer Science"
  note: Includes vita.
  note: Doctor of Philosophy Texas A&M University 2022 https://hdl.handle.net/1969.1/197875
  note: Includes bibliographical references.
  note: Text (Dissertation)
  subject:
    topic: Major Computer Science
  subject:
    topic: Graph topology
  subject:
    topic: 3D graphs
  subject:
    topic: Graph neural networks
  subject:
    topic: Molecular learning
  subject:
    topic: Protein learning
  subject:
    topic: Quantum systems.
  identifier: https://hdl.handle.net/1969.1/197875
  record info:
    record content source: TXA
    record creation date: 240823
    record change date: 20240829173511.4
    record identifier: in00005468473
    language of cataloging:
      language term: eng