Graph Learning: A Survey (2105.00696v1)

Abstract: Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, 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. Major models and algorithms under these categories are reviewed respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.

A Comprehensive Survey of Graph Learning Techniques

Graph learning has emerged as a crucial domain within the field of artificial intelligence, underscoring its importance as a method for processing the unique and complex structures of graph data. The paper "Graph Learning: A Survey," authored by Feng Xia et al., provides a detailed review of contemporary graph learning methodologies and applications. The significance of this survey is rooted in its comprehensive categorization of graph learning techniques and the delineation of their practical utility in varying domains, from social to biological networks.

Core Categories of Graph Learning

The authors categorize graph learning techniques into four primary groups: graph signal processing (GSP), matrix factorization, random walk, and deep learning. Each methodology offers distinct mechanisms for extracting and processing graph-based features, which are crucial for applications such as classification, link prediction, and network representation.

1. Graph Signal Processing (GSP): Stemming from traditional signal processing, GSP involves transforming and interpreting graph signals through methods like spectral analysis. The paper discusses the complexity and computational demands of GSP approaches, as well as their stringent data requirements.
2. Matrix Factorization: This involves simplifying a graph into its constituent matrices to reveal latent structures. Although earlier matrix factorization techniques provided foundational insights, their demand for large memory allocation and lack of adaptability to supervised learning tasks remain significant limitations.
3. Random Walk Methods: These leverage the power of random sequences to sample graph structures effectively. The paper underscores the versatility of random walk methods in different network contexts, including homogeneous and heterogeneous networks, though it acknowledges the inherent randomness and associated computational costs.
4. Deep Learning Techniques: The most recent and potentially impactful category, deep learning involves adapting neural network frameworks for graphs. Methods such as graph convolutional networks and graph generative networks are highlighted for their efficacy in mapping complex relationships within data, paving the way for advancements in vertex and graph analysis.

Strong Numerical Results & Bold Claims

The survey presents a balanced examination of each method's strengths and empirical results. For instance, deep learning methods demonstrate superior performance in tasks involving large and dynamic datasets, showcasing their robustness and adaptability compared to other techniques. Meanwhile, random walk approaches prove effective in heterogeneous network scenarios, offering nuanced insights into structural relationships.

Implications and Future Directions

The implications of graph learning are profound, spanning text interpretation, image classification, knowledge graph analysis, and even combinatorial optimization. Each application reflects the adaptability of graph learning methods to specific domain challenges. Textual data processing, for example, gains enhanced contextual insights through graph-based classification models. Similarly, graph neural networks open new frontiers in image recognition by drawing complex socio-spatial relationships.

Looking forward, the survey pinpoints several open issues ripe for exploration. Dynamic graph learning, fair graph learning, and the interpretability of graph models are highlighted as critical areas for development. The paper suggests that addressing these challenges could significantly enhance the reliability and transparency of graph-based systems.

In conclusion, "Graph Learning: A Survey" serves as an essential resource for researchers looking to navigate the complex landscape of graph learning. By providing a structured overview and identifying future directions, the paper advocates for continued innovation and interdisciplinary research in graph-based AI solutions.