Inductive Representation Learning on Temporal Graphs (2002.07962v1)

Abstract: Inductive representation learning on temporal graphs is an important step toward salable machine learning on real-world dynamic networks. The evolving nature of temporal dynamic graphs requires handling new nodes as well as capturing temporal patterns. The node embeddings, which are now functions of time, should represent both the static node features and the evolving topological structures. Moreover, node and topological features can be temporal as well, whose patterns the node embeddings should also capture. We propose the temporal graph attention (TGAT) layer to efficiently aggregate temporal-topological neighborhood features as well as to learn the time-feature interactions. For TGAT, we use the self-attention mechanism as building block and develop a novel functional time encoding technique based on the classical Bochner's theorem from harmonic analysis. By stacking TGAT layers, the network recognizes the node embeddings as functions of time and is able to inductively infer embeddings for both new and observed nodes as the graph evolves. The proposed approach handles both node classification and link prediction task, and can be naturally extended to include the temporal edge features. We evaluate our method with transductive and inductive tasks under temporal settings with two benchmark and one industrial dataset. Our TGAT model compares favorably to state-of-the-art baselines as well as the previous temporal graph embedding approaches.

• The paper introduces the TGAT layer that integrates temporal and topological data via self-attention.
• It proposes a novel time encoding technique based on Bochner’s theorem to capture continuous temporal dynamics.
• Empirical evaluations on real-world datasets show improved performance for inductive tasks in dynamic graphs.

Overview of Inductive Representation Learning on Temporal Graphs

The paper under review explores a sophisticated approach for inductive representation learning on temporal graphs using a novel Temporal Graph Attention Network (TGAT). The focus is on developing scalable machine learning methodologies for dynamic networks, where the temporal dimension plays a crucial role.

Core Contributions

The authors introduce the TGAT layer, a mechanism designed to integrate temporal and topological information through self-attention. This approach addresses significant challenges in handling dynamic graphs:

1. Temporal Dynamics: Node embeddings are treated as functions of time, allowing the representation to capture continuously evolving topological structures and temporal node features.
2. Inductive Capability: Unlike many prior models that handle only transductive tasks, TGAT infers embeddings for unseen nodes without retraining, using a single forward pass.
3. Temporal Encoding: A functional time encoding based on Bochner's theorem from harmonic analysis is developed. This encoding effectively replaces traditional positional encodings in self-attention, capturing continuous time relationships.

Methodological Insights

The TGAT model utilizes the self-attention mechanism to aggregate features from temporal neighbors. The authors propose a time encoding scheme that transforms time into a functional space, enabling the self-attention layers to process continuous temporal data efficiently.

Architectural Components

• Temporal Attention: Attends to both topological and temporal aspects using time as an additional feature in the attention mechanism, allowing for temporal constraints in message passing.
• Multi-head Attention: Improves the robustness and stability of the model by attending to multiple aspects of the input simultaneously.
• Edge Features Integration: The approach extends naturally to incorporate edge features, which are essential for realistic temporal graph modeling.

Empirical Evaluation

The TGAT model is evaluated on transductive and inductive tasks using datasets from real-world applications, including Reddit, Wikipedia, and an industrial dataset. The tasks focus on link prediction for both observed and new nodes as well as dynamic node classification.

Key Findings

• Superior Performance: TGAT outperforms state-of-the-art baselines in both transductive and inductive settings, demonstrating its robustness and efficacy in handling evolving graphs.
• Attention Analysis: Analysis of attention weights reveals meaningful patterns, such as reduced attention on temporally distant interactions, highlighting the interpretability of the model.

Practical and Theoretical Implications

This paper's contributions have several implications:

• Scalability: Inductive learning on dynamic graphs without retraining is a significant step towards scalable solutions for large-scale applications.
• Temporal Dynamics Understanding: The proposed model offers a deeper understanding of temporal interactions in graphs, paving the way for more sophisticated temporal analysis in real-time systems.

Future Directions

The paper opens various avenues for future research:

• Model Interpretability: Further exploration of self-attention interpretability could yield more insights into temporal graph dynamics.
• Real-Time Adaptation: Enhancing TGAT for real-time adaptability could significantly impact time-sensitive applications.
• Integration with Other Modalities: Exploring the integration of temporal graph models with other data modalities could expand the applicability of TGAT.

In conclusion, the paper successfully addresses the challenges of inductive representation learning on temporal graphs, offering a robust framework with significant practical and theoretical implications.