In this article, we propose a new method for learning representations of graphs called HOT (Higher-Order Dynamic Graph Representation Learning with Efficient Transformers). HOT is designed to handle dynamic graphs, which are graphs that change over time. Our approach uses a combination of neural networks and efficient transformers to learn high-quality representations of dynamic graphs.
Related Work
There has been a lot of research in the field of graph representation learning (GRL) in recent years. Graph neural networks (GNNs) have emerged as a successful part of this field, but they are limited to handling static graphs. Our work builds on this foundation by developing HOT, which can handle dynamic graphs.
Methodology
HOT uses a combination of two key components to learn representations of dynamic graphs:
- Neural Networks: We use neural networks to learn node and edge embeddings in the graph. These embeddings capture information about the structure and properties of the graph.
- Efficient Transformers: We use efficient transformers to handle the dynamic nature of the graph. Efficient transformers are lightweight versions of transformer models that have been shown to be effective for a variety of natural language processing tasks. They allow us to efficiently process the graph in an online manner, i.e., as new nodes and edges are added to the graph.
Higher-Order Graph Neural Networks
One limitation of traditional GNNs is that they only capture information about the local structure of the graph. HOT addresses this issue by using higher-order neural networks (HONNs) to capture information about the global structure of the graph. HONNs are neural networks that take as input a set of nodes and their neighborhoods, rather than just a single node or a single edge. This allows HOT to capture more complex patterns in the graph, such as loops and cycles.
Efficient Transformers
Traditional transformer models are computationally expensive and difficult to scale for large graphs. HOT addresses this issue by using efficient transformers, which are lightweight versions of transformer models that have been shown to be effective for a variety of natural language processing tasks. Efficient transformers use a novel attention mechanism that allows them to efficiently process the graph in an online manner, i.e., as new nodes and edges are added to the graph.
Experiments
We evaluate HOT on several benchmark datasets and show that it outperforms state-of-the-art GRL methods for dynamic graphs. We also demonstrate the versatility of HOT by applying it to a variety of tasks, including node classification, edge prediction, and graph classification.
Conclusion
In this article, we proposed HOT, a new method for learning representations of dynamic graphs using neural networks and efficient transformers. HOT addresses the limitations of traditional GRL methods by capturing information about the global structure of the graph and efficiently processing it in an online manner. We demonstrated the effectiveness of HOT on several benchmark datasets and showed that it outperforms state-of-the-art GRL methods for dynamic graphs.
