Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning (2009.06946v1)

Abstract: Unsupervised (or self-supervised) graph representation learning is essential to facilitate various graph data mining tasks when external supervision is unavailable. The challenge is to encode the information about the graph structure and the attributes associated with the nodes and edges into a low dimensional space. Most existing unsupervised methods promote similar representations across nodes that are topologically close. Recently, it was shown that leveraging additional graph-level information, e.g., information that is shared among all nodes, encourages the representations to be mindful of the global properties of the graph, which greatly improves their quality. However, in most graphs, there is significantly more structure that can be captured, e.g., nodes tend to belong to (multiple) clusters that represent structurally similar nodes. Motivated by this observation, we propose a graph representation learning method called Graph InfoClust (GIC), that seeks to additionally capture cluster-level information content. These clusters are computed by a differentiable K-means method and are jointly optimized by maximizing the mutual information between nodes of the same clusters. This optimization leads the node representations to capture richer information and nodal interactions, which improves their quality. Experiments show that GIC outperforms state-of-art methods in various downstream tasks (node classification, link prediction, and node clustering) with a 0.9% to 6.1% gain over the best competing approach, on average.

• The paper introduces a novel unsupervised method that captures intra-cluster information via differentiable clustering to improve node embeddings.
• It leverages noise-contrastive estimation to maximize mutual information between nodes and cluster summaries, outperforming traditional graph learning techniques.
• Empirical results show performance gains of 0.9% to 6.1% in tasks like node classification and link prediction across standard benchmarks.

Graph InfoClust: Leveraging Cluster-Level Node Information for Unsupervised Graph Representation Learning

The paper "Graph InfoClust: Leveraging Cluster-Level Node Information for Unsupervised Graph Representation Learning" introduces a novel methodology for unsupervised graph representation learning (GRL) with the intent to enhance the quality of node embeddings by leveraging cluster-level information. The primary motivation behind this approach is to incorporate additional structure inherent in most graphs which traditional methods overlook.

Key Concepts

Graph representation learning seeks to encode high-dimensional, non-Euclidean graph data into a lower-dimensional space while preserving the graph's structure and node/edge attributes. Traditional unsupervised GRL methods focus on promoting similar representations among topologically close nodes. While recent advancements incorporate global graph-level information to improve representation quality, Graph InfoClust (GIC) aims to further capture richness by embedding structurally similar nodes into multiplicitous clusters.

The distinguishing feature of GIC is its ability to capture intra-cluster information content, optimizing cluster assignments through a differentiable $K$-means approach. This process helps in maximizing the mutual information between nodes and their respective cluster summaries, leading to more informative node representations.

Methodological Innovations

1. Cluster-Level Information: GIC employs cluster summaries in addition to the global graph summary, capturing information that nodes share within clusters.
2. Differentiable Clustering: By using an end-to-end differentiable $K$-means, GIC jointly optimizes cluster assignments, allowing for seamless integration with the GRL process.
3. Noise-Contrastive Estimation: The mutual information between nodes and cluster/global summaries is estimated using a noise-contrastive approach, improving the robustness of the node representations.

Empirical Evaluation

The paper evaluates GIC across multiple well-established datasets, including CORA, CiteSeer, and PubMed, demonstrating superior performance over state-of-the-art methods. The representations learned with GIC yield gains of 0.9% to 6.1% in tasks such as node classification, link prediction, and clustering. Such improvements highlight the effectiveness of incorporating cluster-level information into the representation learning process.

Implications

Theoretical and practical implications of GIC are significant:

• Enhanced Representation Capacity: By capturing both global and cluster-specific information, GIC potentially offers richer and more discriminative node embeddings, beneficial for numerous graph-based tasks.
• Scalability and Flexibility: The differentiable clustering mechanism allows GIC to scale effectively across large datasets while maintaining flexibility in capturing varying graph structures.

Future Directions

The findings suggest several avenues for future research, such as:

• Refinement of Clustering Techniques: Investigating alternative differentiable clustering methods could further optimize node representation quality.
• Integration with Supervised Methods: Exploring hybrid approaches integrating GIC with supervised learning strategies may provide enhanced performance in labeled datasets.
• Real-World Application Testing: Applying GIC in domains such as social network analysis, biological networks, and recommender systems could unveil practical benefits and limitations.

Overall, the GIC method enriches graph representation learning by adeptly incorporating cluster-level dynamics, offering a robust framework for unsupervised learning in complex graph structures.