Predictive Query-based Pipeline for Graph Data

• URL: http://arxiv.org/abs/2412.09940v1
• Date: Fri, 13 Dec 2024 08:03:57 GMT
• Title: Predictive Query-based Pipeline for Graph Data
• Authors: Plácido A Souza Neto,
• Abstract summary: Graph embedding techniques simplify the analysis and processing of large-scale graphs. Several approaches, such as GraphSAGE, Node2Vec, and FastRP, offer efficient methods for generating graph embeddings. By storing embeddings as node properties, it is possible to compare different embedding techniques and evaluate their effectiveness.
• Score: 0.0
• License:
• Abstract: Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an efficient approach. By projecting complex graphs into a lower-dimensional space, these techniques simplify the analysis and processing of large-scale graphs. By transforming graphs into vectors, it simplifies the analysis and processing of large-scale datasets. Several approaches, such as GraphSAGE, Node2Vec, and FastRP, offer efficient methods for generating graph embeddings. By storing embeddings as node properties, it is possible to compare different embedding techniques and evaluate their effectiveness for specific tasks. This flexibilityallows for dynamic updates to embeddings and facilitates experimentation with different approaches. By analyzing these embeddings, one can extract valuable insights into the relationships between nodes and their similarities within the embedding space

Related papers

• Bootstrapping Heterogeneous Graph Representation Learning via Large Language Models: A Generalized Approach [19.83520243242148]
  Graph representation learning methods are highly effective in handling complex non-Euclidean data by capturing intricate relationships and features within graph structures. Existing Heterogeneous Graph Neural Networks (HGNNs) have shown promising results but require prior knowledge of node and edge types and unified node feature formats. Recent advancements in graph representation learning using Large Language Models (LLMs) offer new solutions.
  arXiv  Detail & Related papers  (2024-12-11T02:37:32Z)
• Hierarchical Aggregations for High-Dimensional Multiplex Graph Embedding [7.271256448682229]
  HMGE is a novel embedding method based on hierarchical aggregation for high-dimensional multiplex graphs. We leverage mutual information between local patches and global summaries to train the model without supervision. Detailed experiments on synthetic and real-world data illustrate the suitability of our approach to downstream supervised tasks.
  arXiv  Detail & Related papers  (2023-12-28T05:39:33Z)
• Improving embedding of graphs with missing data by soft manifolds [51.425411400683565]
  The reliability of graph embeddings depends on how much the geometry of the continuous space matches the graph structure. We introduce a new class of manifold, named soft manifold, that can solve this situation. Using soft manifold for graph embedding, we can provide continuous spaces to pursue any task in data analysis over complex datasets.
  arXiv  Detail & Related papers  (2023-11-29T12:48:33Z)
• Condensing Graphs via One-Step Gradient Matching [50.07587238142548]
  We propose a one-step gradient matching scheme, which performs gradient matching for only one single step without training the network weights. Our theoretical analysis shows this strategy can generate synthetic graphs that lead to lower classification loss on real graphs. In particular, we are able to reduce the dataset size by 90% while approximating up to 98% of the original performance.
  arXiv  Detail & Related papers  (2022-06-15T18:20:01Z)
• Edge but not Least: Cross-View Graph Pooling [76.71497833616024]
  This paper presents a cross-view graph pooling (Co-Pooling) method to better exploit crucial graph structure information. Through cross-view interaction, edge-view pooling and node-view pooling seamlessly reinforce each other to learn more informative graph-level representations.
  arXiv  Detail & Related papers  (2021-09-24T08:01:23Z)
• Effective and Efficient Graph Learning for Multi-view Clustering [173.8313827799077]
  We propose an effective and efficient graph learning model for multi-view clustering. Our method exploits the view-similar between graphs of different views by the minimization of tensor Schatten p-norm. Our proposed algorithm is time-economical and obtains the stable results and scales well with the data size.
  arXiv  Detail & Related papers  (2021-08-15T13:14:28Z)
• A Robust and Generalized Framework for Adversarial Graph Embedding [73.37228022428663]
  We propose a robust framework for adversarial graph embedding, named AGE. AGE generates the fake neighbor nodes as the enhanced negative samples from the implicit distribution. Based on this framework, we propose three models to handle three types of graph data.
  arXiv  Detail & Related papers  (2021-05-22T07:05:48Z)
• Accurate Learning of Graph Representations with Graph Multiset Pooling [45.72542969364438]
  We propose a Graph Multiset Transformer (GMT) that captures the interaction between nodes according to their structural dependencies. Our experimental results show that GMT significantly outperforms state-of-the-art graph pooling methods on graph classification benchmarks.
  arXiv  Detail & Related papers  (2021-02-23T07:45:58Z)
• Understanding graph embedding methods and their applications [1.14219428942199]
  Graph embedding techniques can be effective in converting high-dimensional sparse graphs into low-dimensional, dense and continuous vector spaces. The generated nonlinear and highly informative graph embeddings in the latent space can be conveniently used to address different downstream graph analytics tasks.
  arXiv  Detail & Related papers  (2020-12-15T00:30:22Z)
• Understanding Coarsening for Embedding Large-Scale Graphs [3.6739949215165164]
  Proper analysis of graphs with Machine Learning (ML) algorithms has the potential to yield far-reaching insights into many areas of research and industry. The irregular structure of graph data constitutes an obstacle for running ML tasks on graphs. We analyze the impact of the coarsening quality on the embedding performance both in terms of speed and accuracy.
  arXiv  Detail & Related papers  (2020-09-10T15:06:33Z)
• Graph Pooling with Node Proximity for Hierarchical Representation Learning [80.62181998314547]
  We propose a novel graph pooling strategy that leverages node proximity to improve the hierarchical representation learning of graph data with their multi-hop topology. Results show that the proposed graph pooling strategy is able to achieve state-of-the-art performance on a collection of public graph classification benchmark datasets.
  arXiv  Detail & Related papers  (2020-06-19T13:09:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.