Temporal Graph Multi-Aspect Embeddings

Aimin Sun

doi:10.1109/TKDE.2024.3397491

Temporal Graph Multi-Aspect Embeddings

2024-11-01

0

PRE

AI

DOI：10.1109/TKDE.2024.3397491

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In recent years, graph embedding techniques have exhibited great potential for various downstream tasks, which can leverage both topological structures and the temporal dependencies of nodes in their representations, leading to remarkable achievements. However, the multi-role nature of nodes during their temporally interacting is neglected. To tackle this problem, we propose a novel model, Temporal graph Multi-Aspect Embedding (TMAE), to capture the latent multi-aspect characteristics of nodes in temporal graphs, thereby enhancing the quality of graph embeddings. Specifically, we propose to learn the aspect embeddings of nodes and their weights at different timestamps separately for a better adaptation. In contrast to the conventional fixed aspect number assumption, a Hierarchical Dirichlet Process-based approach is employed to dynamically determine the weight of aspects for nodes at different times. Through this framework, we effectively learn the multi-aspect information through Time-reversed Temporal Walks (TTWs). Extensive experiments performed across eight publicly accessible datasets have demonstrated the significant improvements of the proposed TMAE model over state-of-the-art algorithms by taking advantage of the multi-aspect nature.

Keyword:

Task analysis

Social networking (online)

Recurrent neural networks

Adaptation models

Vectors

Toy manufacturing industry

Sun

Graph embedding

temporal graph

multi-aspect

hierarchical dirichlet process

Temporal Graph Multi-Aspect Embeddings

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