Incomplete Graph Learning: A Comprehensive Survey
- URL: http://arxiv.org/abs/2502.12412v1
- Date: Tue, 18 Feb 2025 01:14:53 GMT
- Title: Incomplete Graph Learning: A Comprehensive Survey
- Authors: Riting Xia, Huibo Liu, Anchen Li, Xueyan Liu, Yan Zhang, Chunxu Zhang, Bo Yang,
- Abstract summary: We conduct a comprehensive review of the literature on incomplete graph learning.
We classify incomplete graph learning methods according to the types of incompleteness.
We discuss the current challenges and propose future directions for incomplete graph learning.
- Score: 6.766729965822966
- License:
- Abstract: Graph learning is a prevalent field that operates on ubiquitous graph data. Effective graph learning methods can extract valuable information from graphs. However, these methods are non-robust and affected by missing attributes in graphs, resulting in sub-optimal outcomes. This has led to the emergence of incomplete graph learning, which aims to process and learn from incomplete graphs to achieve more accurate and representative results. In this paper, we conducted a comprehensive review of the literature on incomplete graph learning. Initially, we categorize incomplete graphs and provide precise definitions of relevant concepts, terminologies, and techniques, thereby establishing a solid understanding for readers. Subsequently, we classify incomplete graph learning methods according to the types of incompleteness: (1) attribute-incomplete graph learning methods, (2) attribute-missing graph learning methods, and (3) hybrid-absent graph learning methods. By systematically classifying and summarizing incomplete graph learning methods, we highlight the commonalities and differences among existing approaches, aiding readers in selecting methods and laying the groundwork for further advancements. In addition, we summarize the datasets, incomplete processing modes, evaluation metrics, and application domains used by the current methods. Lastly, we discuss the current challenges and propose future directions for incomplete graph learning, with the aim of stimulating further innovations in this crucial field. To our knowledge, this is the first review dedicated to incomplete graph learning, aiming to offer valuable insights for researchers in related fields.We developed an online resource to follow relevant research based on this review, available at https://github.com/cherry-a11y/Incomplete-graph-learning.git
Related papers
- The Graph Lottery Ticket Hypothesis: Finding Sparse, Informative Graph
Structure [18.00833762891405]
Graph Lottery Ticket (GLT) Hypothesis: There is an extremely sparse backbone for every graph.
We study 8 key metrics of interest that directly influence the performance of graph learning algorithms.
We propose a straightforward and efficient algorithm for finding these GLTs in arbitrary graphs.
arXiv Detail & Related papers (2023-12-08T00:24:44Z) - A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and
Future Directions [64.84521350148513]
Graphs represent interconnected structures prevalent in a myriad of real-world scenarios.
Effective graph analytics, such as graph learning methods, enables users to gain profound insights from graph data.
However, these methods often suffer from data imbalance, a common issue in graph data where certain segments possess abundant data while others are scarce.
This necessitates the emerging field of imbalanced learning on graphs, which aims to correct these data distribution skews for more accurate and representative learning outcomes.
arXiv Detail & Related papers (2023-08-26T09:11:44Z) - State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
Graph-level learning has been applied to many tasks including comparison, regression, classification, and more.
Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures.
Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
arXiv Detail & Related papers (2023-01-14T09:15:49Z) - Graph Learning and Its Advancements on Large Language Models: A Holistic Survey [37.01696685233113]
This survey focuses on the most recent advancements in integrating graph learning with pre-trained language models.
We provide a holistic review that analyzes current works from the perspective of graph structure, and discusses the latest applications, trends, and challenges in graph learning.
arXiv Detail & Related papers (2022-12-17T22:05:07Z) - Learning node embeddings via summary graphs: a brief theoretical
analysis [55.25628709267215]
Graph representation learning plays an important role in many graph mining applications, but learning embeddings of large-scale graphs remains a problem.
Recent works try to improve scalability via graph summarization -- i.e., they learn embeddings on a smaller summary graph, and then restore the node embeddings of the original graph.
We give an in-depth theoretical analysis of three specific embedding learning methods based on introduced kernel matrix.
arXiv Detail & Related papers (2022-07-04T04:09:50Z) - Graph Lifelong Learning: A Survey [6.545297572977323]
This paper focuses on the motivations, potentials, state-of-the-art approaches, and open issues of graph lifelong learning.
We expect extensive research and development interest in this emerging field.
arXiv Detail & Related papers (2022-02-22T06:14:07Z) - Graph Self-supervised Learning with Accurate Discrepancy Learning [64.69095775258164]
We propose a framework that aims to learn the exact discrepancy between the original and the perturbed graphs, coined as Discrepancy-based Self-supervised LeArning (D-SLA)
We validate our method on various graph-related downstream tasks, including molecular property prediction, protein function prediction, and link prediction tasks, on which our model largely outperforms relevant baselines.
arXiv Detail & Related papers (2022-02-07T08:04:59Z) - Unbiased Graph Embedding with Biased Graph Observations [52.82841737832561]
We propose a principled new way for obtaining unbiased representations by learning from an underlying bias-free graph.
Based on this new perspective, we propose two complementary methods for uncovering such an underlying graph.
arXiv Detail & Related papers (2021-10-26T18:44:37Z) - Graph Learning: A Survey [38.245120261668816]
We present a comprehensive overview on the state-of-the-art of graph learning.
Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning.
We examine graph learning applications in areas such as text, images, science, knowledge graphs, and optimization.
arXiv Detail & Related papers (2021-05-03T09:06:01Z) - A Survey of Adversarial Learning on Graphs [59.21341359399431]
We investigate and summarize the existing works on graph adversarial learning tasks.
Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks.
We emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively.
arXiv Detail & Related papers (2020-03-10T12:48:00Z)
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.