Corpus ID: 231847229

Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach

@article{Kaul2021UnderstandingHS,
  title={Understanding Higher-order Structures in Evolving Graphs: A Simplicial Complex based Kernel Estimation Approach},
  author={Manohar Kaul and M. Imaizumi},
  journal={ArXiv},
  year={2021},
  volume={abs/2102.03609}
}
Dynamic graphs are rife with higher-order interactions, such as co-authorship relationships and protein-protein interactions in biological networks, that naturally arise between more than two nodes at once. In spite of the ubiquitous presence of such higher-order interactions, limited attention has been paid to the higher-order counterpart of the popular pairwise link prediction problem. Existing higher-order structure prediction methods are mostly based on heuristic feature extraction… Expand

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References

SHOWING 1-10 OF 38 REFERENCES
Simplicial closure and higher-order link prediction
  • 133
  • Highly Influential
  • PDF
Learning Representations using Spectral-Biased Random Walks on Graphs
  • 1
  • PDF
Link Prediction Based on Graph Neural Networks
  • 365
  • Highly Influential
  • PDF
Weisfeiler-Lehman Graph Kernels
  • 932
  • PDF
Temporal Graph Networks for Deep Learning on Dynamic Graphs
  • 16
  • PDF
Watch Your Step: Learning Node Embeddings via Graph Attention
  • 80
  • PDF
Hyperlink Prediction in Hypernetworks Using Latent Social Features
  • 17
...
1
2
3
4
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