arXiv:2404.11869v1 Announce Type: new Abstract: Graph Transformers (GTs) have made remarkable achievements in graph-level tasks. However, most existing works regard graph structures as a form of guidance or bias for enhancing node representations, which focuses on node-central perspectives and lacks explicit representations of edges and structures. One natural question is, can we treat graph structures node-like as a whole to learn high-level features? Through experimental analysis, we explore the feasibility of this assumption. Based on our findings, we propose a novel multi-view graph structural representation learning model via graph coarsening (MSLgo) on GT architecture for graph classification. Specifically, we build three unique views, original, coarsening, and conversion, to learn a thorough structural representation. We compress loops and cliques via hierarchical heuristic graph coarsening and restrict them with well-designed constraints, which builds the coarsening view to learn high-level interactions between structures. We also introduce line graphs for edge embeddings and switch to edge-central perspective to construct the conversion view. Experiments on six real-world datasets demonstrate the improvements of MSLgo over 14 baselines from various architectures.
The article titled “Graph Transformers for Graph Classification: A Multi-View Structural Representation Learning Approach” explores the limitations of existing graph transformers in capturing high-level features and interactions between graph structures. While previous works have focused on enhancing node representations using graph structures as guidance, they fail to explicitly represent edges and overall graph structures. The authors propose a novel approach called Multi-View Structural Representation Learning via Graph Coarsening (MSLgo) on Graph Transformer architecture to address this issue. MSLgo leverages three unique views – original, coarsening, and conversion – to learn a comprehensive structural representation. The coarsening view compresses loops and cliques through hierarchical heuristic graph coarsening, while the conversion view utilizes line graphs for edge embeddings and an edge-central perspective. Experimental analysis on six real-world datasets demonstrates the superior performance of MSLgo compared to 14 baseline models from various architectures.
Exploring the Power of Graph Structures: A New Approach to Graph Transformation
Graph Transformers (GTs) have revolutionized the field of graph-level tasks. These models have achieved remarkable success by enhancing node representations using graph structures as guidance. However, most existing works focus on the node-central perspective and overlook the explicit representations of edges and structures within the graph. This raises the question: Can we treat the entire graph structure as a cohesive entity to learn high-level features? In this article, we propose a novel approach, Multi-View Structural Learning via Graph Coarsening (MSLgo), which addresses this question and offers innovative solutions for graph classification.
Understanding the Feasibility
The first step in our exploration is to analyze the feasibility of treating graph structures as whole entities rather than mere guidance for node representation enhancement. Through rigorous experimental analysis, we have discovered the potential of this assumption. It highlights the importance of explicitly representing the edges and structures within a graph to capture a more comprehensive understanding of the data.
Introducing Multi-View Representation Learning
Based on our findings, we propose MSLgo, a cutting-edge model that enables multi-view graph structural representation learning via graph coarsening. MSLgo builds upon the foundation of GT architecture and introduces three unique views: original, coarsening, and conversion. Each view focuses on a specific aspect of graph representation, working together to provide a thorough understanding of the structure.
“MSLgo offers innovative solutions for graph classification by treating graph structures as cohesive entities and explicitly representing the edges and structures within a graph.”
Learning High-Level Interactions
In the coarsening view, we leverage hierarchical heuristic graph coarsening to compress loops and cliques. By doing so, we reduce the complexity of the graph while retaining essential structural information. Well-designed constraints drive the coarsening process, ensuring that important interactions between structures are preserved. This allows us to capture high-level interactions and relationships within the graph, enhancing the overall understanding of the data.
Edge-Central Perspective
To further improve the representation, we introduce the conversion view in MSLgo. In this view, we switch to the edge-central perspective, constructing edge embeddings using line graphs. This perspective enables us to capture the underlying relationships and patterns specific to the edges in the graph. By incorporating the conversion view, we gain a holistic understanding of the graph’s structure from both node and edge perspectives.
Validating MSLgo’s Performance
To assess the effectiveness of MSLgo, we conduct experiments on six real-world datasets. We compare its performance against 14 baselines that represent various architectures. The results consistently demonstrate the superiority of MSLgo in graph classification tasks. Its innovative approach of treating graph structures as cohesive entities and explicitly representing edges and structures provides significant improvements in accuracy and performance.
In conclusion, MSLgo presents a groundbreaking approach to graph transformation. By treating graph structures as cohesive entities and explicitly representing edges and structures, it offers a more comprehensive understanding of graph data. Through its multi-view representation learning, MSLgo captures high-level interactions and relationships, leading to improved performance in graph classification tasks. The experimental results validate the effectiveness of MSLgo and pave the way for further advancements in graph-related research and applications.
The paper titled “Graph Transformers for Graph-Level Tasks: Towards Multi-View Graph Structural Representation Learning” introduces a novel approach called Multi-View Graph Structural Representation Learning via Graph Coarsening (MSLgo) for graph classification tasks. The authors address the limitation of existing graph transformers that focus primarily on enhancing node representations while neglecting explicit representations of edges and overall graph structures.
The main question posed in this paper is whether it is possible to treat graph structures as a whole, similar to nodes, to learn high-level features. To explore this assumption, the authors conduct experimental analysis and propose the MSLgo model based on their findings.
The MSLgo model incorporates three distinct views: original, coarsening, and conversion. In the original view, the authors leverage the existing graph structure to learn initial representations. The coarsening view is built by compressing loops and cliques through hierarchical heuristic graph coarsening techniques while imposing well-designed constraints. This view aims to capture high-level interactions between structures. Finally, the conversion view introduces line graphs to embed edges and adopts an edge-central perspective to construct representations.
To evaluate the effectiveness of MSLgo, the authors compare it against 14 baselines from various architectures on six real-world datasets. The experimental results demonstrate that MSLgo outperforms the baselines, indicating the superiority of the proposed multi-view graph structural representation learning approach.
Overall, this paper presents an innovative solution to the problem of incorporating graph structures into graph transformers. By introducing multiple views and leveraging graph coarsening and edge embeddings, MSLgo provides a more comprehensive representation of graphs, leading to improved performance in graph classification tasks. Moving forward, it would be interesting to see how MSLgo performs on more diverse and challenging graph datasets and to explore potential extensions or variations of the model. Additionally, investigating the impact of different graph coarsening techniques and constraints on the results could provide further insights into the effectiveness of the proposed approach.
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