Deep learning for dynamic graphs: models and benchmarks

• URL: http://arxiv.org/abs/2307.06104v4
• Date: Tue, 9 Apr 2024 07:41:38 GMT
• Title: Deep learning for dynamic graphs: models and benchmarks
• Authors: Alessio Gravina, Davide Bacciu,
• Abstract summary: Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved.
• Score: 16.851689741256912
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent progress in research on Deep Graph Networks (DGNs) has led to a maturation of the domain of learning on graphs. Despite the growth of this research field, there are still important challenges that are yet unsolved. Specifically, there is an urge of making DGNs suitable for predictive tasks on realworld systems of interconnected entities, which evolve over time. With the aim of fostering research in the domain of dynamic graphs, at first, we survey recent advantages in learning both temporal and spatial information, providing a comprehensive overview of the current state-of-the-art in the domain of representation learning for dynamic graphs. Secondly, we conduct a fair performance comparison among the most popular proposed approaches on node and edge-level tasks, leveraging rigorous model selection and assessment for all the methods, thus establishing a sound baseline for evaluating new architectures and approaches

Related papers

• Information propagation dynamics in Deep Graph Networks [1.8130068086063336]
  Deep Graph Networks (DGNs) have emerged as a family of deep learning models that can process and learn structured information. This thesis investigates the dynamics of information propagation within DGNs for static and dynamic graphs, focusing on their design as dynamical systems.
  arXiv  Detail & Related papers  (2024-10-14T12:55:51Z)
• Gradient Transformation: Towards Efficient and Model-Agnostic Unlearning for Dynamic Graph Neural Networks [66.70786325911124]
  Graph unlearning has emerged as an essential tool for safeguarding user privacy and mitigating the negative impacts of undesirable data. With the increasing prevalence of DGNNs, it becomes imperative to investigate the implementation of dynamic graph unlearning. We propose an effective, efficient, model-agnostic, and post-processing method to implement DGNN unlearning.
  arXiv  Detail & Related papers  (2024-05-23T10:26:18Z)
• Node-Time Conditional Prompt Learning In Dynamic Graphs [14.62182210205324]
  We propose DYGPROMPT, a novel pre-training and prompt learning framework for dynamic graph modeling. We recognize that node and time features mutually characterize each other, and propose dual condition-nets to model the evolving node-time patterns in downstream tasks.
  arXiv  Detail & Related papers  (2024-05-22T19:10:24Z)
• Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learning [53.81365215811222]
  We provide a review and summary of the latest approaches, strategies, and insights that address distribution shifts within the context of graph learning. We categorize existing graph learning methods into several essential scenarios, including graph domain adaptation learning, graph out-of-distribution learning, and graph continual learning. We discuss the potential applications and future directions for graph learning under distribution shifts with a systematic analysis of the current state in this field.
  arXiv  Detail & Related papers  (2024-02-26T07:52:40Z)
• Towards Graph Foundation Models: A Survey and Beyond [66.37994863159861]
  Foundation models have emerged as critical components in a variety of artificial intelligence applications. The capabilities of foundation models to generalize and adapt motivate graph machine learning researchers to discuss the potential of developing a new graph learning paradigm. This article introduces the concept of Graph Foundation Models (GFMs), and offers an exhaustive explanation of their key characteristics and underlying technologies.
  arXiv  Detail & Related papers  (2023-10-18T09:31:21Z)
• A Comprehensive Survey on Deep Graph Representation Learning [26.24869157855632]
  Graph representation learning aims to encode high-dimensional sparse graph-structured data into low-dimensional dense vectors. Traditional methods have limited model capacity which limits the learning performance. Deep graph representation learning has shown great potential and advantages over shallow (traditional) methods.
  arXiv  Detail & Related papers  (2023-04-11T08:23:52Z)
• Graph Neural Networks for temporal graphs: State of the art, open challenges, and opportunities [15.51428011794213]
  Graph Neural Networks (GNNs) have become the leading paradigm for learning on (static) graph-structured data. Recent years, GNN-based models for temporal graphs have emerged as a promising area of research to extend the capabilities of GNNs. We provide the first comprehensive overview of the current state-of-the-art of temporal GNN, introducing a rigorous formalization of learning settings and tasks. We conclude the survey with a discussion of the most relevant open challenges for the field, from both research and application perspectives.
  arXiv  Detail & Related papers  (2023-02-02T11:12:51Z)
• Graph Pooling for Graph Neural Networks: Progress, Challenges, and Opportunities [128.55790219377315]
  Graph neural networks have emerged as a leading architecture for many graph-level tasks. graph pooling is indispensable for obtaining a holistic graph-level representation of the whole graph.
  arXiv  Detail & Related papers  (2022-04-15T04:02:06Z)
• EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs [5.4744970832051445]
  We present a variety of large scale, dynamic heterogeneous academic graphs to test the effectiveness of models developed for graph forecasting tasks. Our novel datasets cover both context and content information extracted from scientific publications across two communities: Artificial Intelligence (AI) and Nuclear Nonproliferation (NN)
  arXiv  Detail & Related papers  (2022-04-14T19:43:34Z)
• Towards Deeper Graph Neural Networks [63.46470695525957]
  Graph convolutions perform neighborhood aggregation and represent one of the most important graph operations. Several recent studies attribute this performance deterioration to the over-smoothing issue. We propose Deep Adaptive Graph Neural Network (DAGNN) to adaptively incorporate information from large receptive fields.
  arXiv  Detail & Related papers  (2020-07-18T01:11:14Z)
• Graph Representation Learning via Graphical Mutual Information Maximization [86.32278001019854]
  We propose a novel concept, Graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. We develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder.
  arXiv  Detail & Related papers  (2020-02-04T08:33:49Z)

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.