Dynamic Graph Embedding Through Hub-aware Random Walks
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908461677150208 |
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| author | Tomčić, Aleksandar Savić, Miloš Simić, Dušan Radovanović, Miloš |
| author_facet | Tomčić, Aleksandar Savić, Miloš Simić, Dušan Radovanović, Miloš |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_17764 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Dynamic Graph Embedding Through Hub-aware Random Walks Tomčić, Aleksandar Savić, Miloš Simić, Dušan Radovanović, Miloš Social and Information Networks 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. |
| title | Dynamic Graph Embedding Through Hub-aware Random Walks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2505.17764 |