On the Feasibility of Simple Transformer for Dynamic Graph Modeling
WWW 2024(2024)
摘要
Dynamic graph modeling is crucial for understanding complex structures in web
graphs, spanning applications in social networks, recommender systems, and
more. Most existing methods primarily emphasize structural dependencies and
their temporal changes. However, these approaches often overlook detailed
temporal aspects or struggle with long-term dependencies. Furthermore, many
solutions overly complicate the process by emphasizing intricate module designs
to capture dynamic evolutions. In this work, we harness the strength of the
Transformer's self-attention mechanism, known for adeptly handling long-range
dependencies in sequence modeling. Our approach offers a simple Transformer
model tailored for dynamic graph modeling without complex modifications. We
re-conceptualize dynamic graphs as a sequence modeling challenge and introduce
an innovative temporal alignment technique. This technique not only captures
the inherent temporal evolution patterns within dynamic graphs but also
streamlines the modeling process of their evolution. As a result, our method
becomes versatile, catering to an array of applications. Our model's
effectiveness is underscored through rigorous experiments on four real-world
datasets from various sectors, solidifying its potential in dynamic graph
modeling.
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