Balanced Multi-Relational Graph Clustering (2407.16863v1)

Abstract: Multi-relational graph clustering has demonstrated remarkable success in uncovering underlying patterns in complex networks. Representative methods manage to align different views motivated by advances in contrastive learning. Our empirical study finds the pervasive presence of imbalance in real-world graphs, which is in principle contradictory to the motivation of alignment. In this paper, we first propose a novel metric, the Aggregation Class Distance, to empirically quantify structural disparities among different graphs. To address the challenge of view imbalance, we propose Balanced Multi-Relational Graph Clustering (BMGC), comprising unsupervised dominant view mining and dual signals guided representation learning. It dynamically mines the dominant view throughout the training process, synergistically improving clustering performance with representation learning. Theoretical analysis ensures the effectiveness of dominant view mining. Extensive experiments and in-depth analysis on real-world and synthetic datasets showcase that BMGC achieves state-of-the-art performance, underscoring its superiority in addressing the view imbalance inherent in multi-relational graphs. The source code and datasets are available at https://github.com/zxlearningdeep/BMGC.

• The paper introduces BMGC, a framework that addresses view imbalances in multi-relational graphs using unsupervised dominant view mining and co-aligned representation learning.
• It proposes the Aggregation Class Distance metric to evaluate structural disparities, yielding superior clustering performance across metrics like NMI, ARI, ACC, and F1.
• Extensive experiments on synthetic and real-world data validate BMGC's state-of-the-art results, highlighting its potential in recommendation systems and network analysis.

Balanced Multi-Relational Graph Clustering

The proliferation of multi-relational graphs in real-world applications, such as citation and social networks, presents unique clustering challenges. This academic paper addresses these challenges with a particular focus on the imbalance across different views within these graphs. It introduces a novel approach termed Balanced Multi-Relational Graph Clustering (BMGC), which effectively tackles the inherent view imbalance through unsupervised dominant view mining and co-aligned representation learning.

Key Contributions

1. View Imbalance in Multi-Relational Graphs: The paper identifies that differing views in multi-relational graphs possess varying degrees of relevance to clustering tasks. Traditional methods, which aim to align all views equally, may overlook this discrepancy, leading to suboptimal performance. BMGC, conversely, incorporates mechanisms to dynamically determine the dominant view, harnessing its strengths to guide representation learning.
2. Aggregation Class Distance (ACD): ACD is proposed as a metric to evaluate structural disparities between views. It provides a more comprehensive analysis by accounting for node aggregation processes and node class distributions, outperforming traditional metrics like homophily ratios, especially in scenarios where graph structure homogeneity doesn't directly translate to better task performance.
3. Balanced Multi-Relational Graph Clustering (BMGC): The BMGC framework is designed to dynamically mine the dominant view of the graph and synergizes this information with representation learning processes. BMGC leverages dual signal guidance from both dominant views and original node features, aligning their representations through contrastive learning. It further enhances clustering quality with dominant assignments.
4. Theoretical Validation: The efficacy of dominant view mining is supported by theoretical analysis, indicating a strong correlation between minimized similarity loss in aggregated features and optimal clustering partitions.
5. Experimental Validation: Extensive experiments on both synthetic and real-world datasets demonstrate BMGC's superiority. Notably, BMGC achieves state-of-the-art results across various metrics like NMI, ARI, ACC, and F1, outperforming traditional and modern graph clustering methods.

Implications and Future Directions

The development of BMGC showcases a significant advancement in handling view imbalances in multi-relational graphs, with implications that extend beyond clustering. Practically, this approach can improve the accuracy of network analysis in domains such as recommendation systems and biochemical networks, where understanding multiple relational perspectives is crucial.

Theoretically, BMGC opens avenues for future exploration such as further refining ACD to include additional graph properties or extending the framework to semi-supervised scenarios. Further exploration into the scalability of BMGC with respect to ultra-large datasets will also be essential, potentially leveraging distributed computing resources.

In conclusion, BMGC offers a well-founded, empirically validated framework for multi-relational graph clustering, setting a new benchmark in the domain. It highlights the importance of considering view imbalances, providing a roadmap for future research to build upon its foundational concepts.