A persistent homology perspective to the link prediction problem

S. Bhatia, B. Chatterjee, D. Nathani, M. Kaul, in:, Complex Networks and Their Applications VIII, Springer Nature, 2019, pp. 27–39.

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Author
Bhatia, Sumit; Chatterjee, BapiIST Austria; Nathani, Deepak; Kaul, Manohar
Department
Series Title
SCI
Abstract
Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture the topological properties of data succinctly at different spatial resolutions. For graphical data, the shape, and structure of the neighborhood of individual data items (nodes) are an essential means of characterizing their properties. We propose the use of persistent homology methods to capture structural and topological properties of graphs and use it to address the problem of link prediction. We achieve encouraging results on nine different real-world datasets that attest to the potential of persistent homology-based methods for network analysis.
Publishing Year
Date Published
2019-11-26
Proceedings Title
Complex Networks and their applications VIII
Volume
881
Page
27-39
Conference
COMPLEX: International Conference on Complex Networks and their Applications
Conference Location
Lisbon, Portugal
Conference Date
2019-12-10 – 2019-12-12
ISSN
eISSN
IST-REx-ID

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Bhatia S, Chatterjee B, Nathani D, Kaul M. A persistent homology perspective to the link prediction problem. In: Complex Networks and Their Applications VIII. Vol 881. Springer Nature; 2019:27-39. doi:10.1007/978-3-030-36687-2_3
Bhatia, S., Chatterjee, B., Nathani, D., & Kaul, M. (2019). A persistent homology perspective to the link prediction problem. In Complex Networks and their applications VIII (Vol. 881, pp. 27–39). Lisbon, Portugal: Springer Nature. https://doi.org/10.1007/978-3-030-36687-2_3
Bhatia, Sumit, Bapi Chatterjee, Deepak Nathani, and Manohar Kaul. “A Persistent Homology Perspective to the Link Prediction Problem.” In Complex Networks and Their Applications VIII, 881:27–39. Springer Nature, 2019. https://doi.org/10.1007/978-3-030-36687-2_3.
S. Bhatia, B. Chatterjee, D. Nathani, and M. Kaul, “A persistent homology perspective to the link prediction problem,” in Complex Networks and their applications VIII, Lisbon, Portugal, 2019, vol. 881, pp. 27–39.
Bhatia S, Chatterjee B, Nathani D, Kaul M. 2019. A persistent homology perspective to the link prediction problem. Complex Networks and their applications VIII. COMPLEX: International Conference on Complex Networks and their Applications, SCI, vol. 881. 27–39.
Bhatia, Sumit, et al. “A Persistent Homology Perspective to the Link Prediction Problem.” Complex Networks and Their Applications VIII, vol. 881, Springer Nature, 2019, pp. 27–39, doi:10.1007/978-3-030-36687-2_3.
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