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arXiv · 2505.17764

Dynamic Graph Embedding Through Hub-aware Random Walks

Aleksandar Tomčić·Miloš Savić·Dušan Simić·Miloš Radovanović
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Abstract

The role of high-degree nodes, or hubs, in shaping graph dynamics and structure is well-recognized in network science, yet their influence remains underexplored in the context of dynamic graph embedding. Recent advances in representation learning for graphs have shown that random walk-based methods can capture both structural and temporal patterns, but often overlook the impact of hubs on walk trajectories and embedding stability. In this paper, we introduce DeepHub, a method for dynamic graph embedding that explicitly integrates hub sensitivity into random walk sampling strategies. Focusing on dynnode2vec as a representative dynamic embedding method, we systematically analyze the effect of hub-biased walks across nine real-world temporal networks. Our findings reveal that standard random walks tend to overrepresent hub nodes, leading to embeddings that underfit the evolving local context of less-connected nodes. By contrast, hub-aware walks can balance exploration, resulting in embeddings that better preserve temporal neighborhood structure and improve downstream task performance. These results suggest that hub-awareness is an important yet overlooked factor in dynamic graph embedding, and our work provides a foundation for more robust, structure-sensitive representation learning in evolving networks.

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Aleksandar Tomčić, Miloš Savić, Dušan Simić, Miloš Radovanović. 2025-07-23. Dynamic Graph Embedding Through Hub-aware Random Walks. https://arxiv.org/abs/2505.17764

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