MPXGAT: An Attention based Deep Learning Model for Multiplex Graphs Embedding (2403.19246v1)

Abstract: Graph representation learning has rapidly emerged as a pivotal field of study. Despite its growing popularity, the majority of research has been confined to embedding single-layer graphs, which fall short in representing complex systems with multifaceted relationships. To bridge this gap, we introduce MPXGAT, an innovative attention-based deep learning model tailored to multiplex graph embedding. Leveraging the robustness of Graph Attention Networks (GATs), MPXGAT captures the structure of multiplex networks by harnessing both intra-layer and inter-layer connections. This exploitation facilitates accurate link prediction within and across the network's multiple layers. Our comprehensive experimental evaluation, conducted on various benchmark datasets, confirms that MPXGAT consistently outperforms state-of-the-art competing algorithms.

• The paper presents an attention-based dual-phase model that captures both intra-layer and inter-layer relationships in multiplex graphs.
• The model utilizes dedicated modules MPXGAT-H and MPXGAT-V, achieving state-of-the-art link prediction performance on diverse datasets.
• Experimental results demonstrate improved accuracy in complex networks, highlighting its practical impact in recommender systems, cybersecurity, and bioinformatics.

MPXGAT: Elevating Multiplex Graph Embedding with Attention Mechanisms

Introduction

Graph representation learning has experienced a substantial rise in interest due to its utility in capturing complex relationships within data. Traditional approaches to graph embedding have principally focused on single-layer graphs, which capture only a unidimensional aspect of relationships. However, real-world networks often feature multifaceted interactions that cannot be accurately depicted through such a simplified model. This limitation has motivated the development of techniques for embedding multiplex graphs, which consist of multiple layers, each representing a distinct type of relationship. Among these techniques, the use of Graph Attention Networks (GATs) has emerged as a novel approach. This post discusses MPXGAT, a cutting-edge model that leverages attention mechanisms for embedding multiplex graphs, providing a more nuanced view of complex systems through improved link prediction capability.

Model Overview

The MPXGAT model is designed to address the specific challenges of multiplex graph embedding by considering both intra-layer and inter-layer relationships. It operates in two phases to generate embeddings: initially, it processes each layer of the multiplex graph separately to capture the intricacies of intra-layer connections. In the subsequent phase, it constructs embeddings that reflect the inter-layer connections by leveraging the insights obtained from the first phase. This dual-phase approach, characterized by distinct sub-models MPXGAT-H (for horizontal or intra-layer processing) and MPXGAT-V (for vertical or inter-layer processing), ensures that both types of relationships are accurately represented in the final embeddings.

Notably, in the phase handled by MPXGAT-H, the model applies GAT convolutional layers to each horizontal layer independently, harnessing self-attention mechanisms to generate node embeddings based on intra-layer relationships. MPXGAT-V then builds on these horizontal embeddings to produce the final embeddings that also incorporate inter-layer relationships. This process is facilitated by custom mechanisms within MPXGAT-V that allow for the integration of insights from horizontal embeddings, thereby enhancing the model's overall capacity to represent complex multiplex structures.

Experimental Evaluation

The effectiveness of MPXGAT in predictive link accuracy has been corroborated through comprehensive experiments on diverse datasets, including collaboration networks, biological networks, and online social networks. These evaluations have demonstrated MPXGAT's superior performance in predicting both intra-layer and inter-layer links compared to existing state-of-the-art models such as GraphSAGE, GATNE, and MultiplexSAGE. Particularly notable is MPXGAT's ability to outperform these models in inter-layer link prediction, highlighting its advanced representation capabilities for multiplex networks.

Implications and Future Directions

MPXGAT's distinct approach to multiplex graph embedding has practical and theoretical implications. Practically, it enables more precise modeling of complex systems, enhancing applications in recommender systems, cybersecurity, and bioinformatics. Theoretically, it offers insights into the representation of multifaceted relationships in networks, paving the way for further advancements in graph representation learning. Future work will explore the impact of community structures within layers on embedding efficacy and predictive reliability, further refining the model's ability to capture the nuanced dynamics of multiplex networks.

In summary, MPXGAT represents a significant advancement in the representation learning of multiplex graphs, demonstrating the value of attention mechanisms in capturing complex network structures. Its development underscores the importance of considering both intra-layer and inter-layer connections in accurately depicting multiplex relationships, setting a new standard for graph embedding techniques.