EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
- URL: http://arxiv.org/abs/2204.07203v1
- Date: Thu, 14 Apr 2022 19:43:34 GMT
- Title: EXPERT: Public Benchmarks for Dynamic Heterogeneous Academic Graphs
- Authors: Sameera Horawalavithana, Ellyn Ayton, Anastasiya Usenko, Shivam
Sharma, Jasmine Eshun, Robin Cosbey, Maria Glenski, and Svitlana Volkova
- Abstract summary: 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)
- Score: 5.4744970832051445
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: Machine learning models that learn from dynamic graphs face nontrivial
challenges in learning and inference as both nodes and edges change over time.
The existing large-scale graph benchmark datasets that are widely used by the
community primarily focus on homogeneous node and edge attributes and are
static. In this work, we present a variety of large scale, dynamic
heterogeneous academic graphs to test the effectiveness of models developed for
multi-step 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). In
addition, we propose a systematic approach to improve the existing evaluation
procedures used in the graph forecasting models.
Related papers
- Revisiting Graph Neural Networks on Graph-level Tasks: Comprehensive Experiments, Analysis, and Improvements [54.006506479865344]
We propose a unified evaluation framework for graph-level Graph Neural Networks (GNNs)
This framework provides a standardized setting to evaluate GNNs across diverse datasets.
We also propose a novel GNN model with enhanced expressivity and generalization capabilities.
arXiv Detail & Related papers (2025-01-01T08:48:53Z) - A Deep Probabilistic Framework for Continuous Time Dynamic Graph Generation [4.568104644312763]
We formalize this approach as DG-Gen, a generative framework for continuous time dynamic graphs.
Our experiments demonstrate that DG-Gen not only generates higher fidelity graphs compared to traditional methods but also significantly advances link prediction tasks.
arXiv Detail & Related papers (2024-12-20T05:34:11Z) - Towards Data-centric Machine Learning on Directed Graphs: a Survey [23.498557237805414]
We introduce a novel taxonomy for existing studies of directed graph learning.
We re-examine these methods from the data-centric perspective, with an emphasis on understanding and improving data representation.
We identify key opportunities and challenges within the field, offering insights that can guide future research and development in directed graph learning.
arXiv Detail & Related papers (2024-11-28T06:09:12Z) - Deep learning for dynamic graphs: models and benchmarks [16.851689741256912]
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.
arXiv Detail & Related papers (2023-07-12T12:02:36Z) - Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs [22.64740740462169]
We propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update.
To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets.
arXiv Detail & Related papers (2023-07-07T06:26:44Z) - Dynamic Graph Representation Learning with Neural Networks: A Survey [0.0]
Dynamic graph representations have emerged as a new machine learning problem.
This paper aims at providing a review of problems and models related to dynamic graph learning.
arXiv Detail & Related papers (2023-04-12T09:39:17Z) - Data-Free Adversarial Knowledge Distillation for Graph Neural Networks [62.71646916191515]
We propose the first end-to-end framework for data-free adversarial knowledge distillation on graph structured data (DFAD-GNN)
To be specific, our DFAD-GNN employs a generative adversarial network, which mainly consists of three components: a pre-trained teacher model and a student model are regarded as two discriminators, and a generator is utilized for deriving training graphs to distill knowledge from the teacher model into the student model.
Our DFAD-GNN significantly surpasses state-of-the-art data-free baselines in the graph classification task.
arXiv Detail & Related papers (2022-05-08T08:19:40Z) - Heterogeneous Graph Neural Networks using Self-supervised Reciprocally
Contrastive Learning [102.9138736545956]
Heterogeneous graph neural network (HGNN) is a very popular technique for the modeling and analysis of heterogeneous graphs.
We develop for the first time a novel and robust heterogeneous graph contrastive learning approach, namely HGCL, which introduces two views on respective guidance of node attributes and graph topologies.
In this new approach, we adopt distinct but most suitable attribute and topology fusion mechanisms in the two views, which are conducive to mining relevant information in attributes and topologies separately.
arXiv Detail & Related papers (2022-04-30T12:57:02Z) - Hyperbolic Graph Neural Networks: A Review of Methods and Applications [55.5502008501764]
Graph neural networks generalize conventional neural networks to graph-structured data.
The performance of Euclidean models in graph-related learning is still bounded and limited by the representation ability of Euclidean geometry.
Recently, hyperbolic space has gained increasing popularity in processing graph data with tree-like structure and power-law distribution.
arXiv Detail & Related papers (2022-02-28T15:08:48Z) - TCL: Transformer-based Dynamic Graph Modelling via Contrastive Learning [87.38675639186405]
We propose a novel graph neural network approach, called TCL, which deals with the dynamically-evolving graph in a continuous-time fashion.
To the best of our knowledge, this is the first attempt to apply contrastive learning to representation learning on dynamic graphs.
arXiv Detail & Related papers (2021-05-17T15:33:25Z) - Model-Agnostic Graph Regularization for Few-Shot Learning [60.64531995451357]
We present a comprehensive study on graph embedded few-shot learning.
We introduce a graph regularization approach that allows a deeper understanding of the impact of incorporating graph information between labels.
Our approach improves the performance of strong base learners by up to 2% on Mini-ImageNet and 6.7% on ImageNet-FS.
arXiv Detail & Related papers (2021-02-14T05:28:13Z)
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.