EMP: Effective Multidimensional Persistence for Graph Representation Learning

Yuzhou Chen, Ignacio Segovia-Dominguez, Cuneyt Gurcan Akcora, Zhiwei Zhen, Murat Kantarcioglu, Yulia Gel, Baris Coskunuzer
Proceedings of the Second Learning on Graphs Conference, PMLR 231:24:1-24:12, 2024.

Abstract

Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH), which furnishes an exclusive topological imprint of data by tracing the evolution of latent structures as a scale parameter changes. Present PH tools are confined to analyzing data through a single filter parameter. However, many scenarios necessitate the consideration of multiple relevant parameters to attain finer insights into the data. We address this issue by introducing the Effective Multidimensional Persistence (EMP) framework. This framework empowers the exploration of data by simultaneously varying multiple scale parameters. The framework integrates descriptor functions into the analysis process, yielding a highly expressive data summary. It seamlessly integrates established single PH summaries into multidimensional counterparts like EMP Landscapes, Silhouettes, Images, and Surfaces. These summaries represent data’s multidimensional aspects as matrices and arrays, aligning effectively with diverse ML models. We provide theoretical guarantees and stability proofs for EMP summaries. We demonstrate EMP’s utility in graph classification tasks, showing its effectiveness. Results reveal EMP enhances various single PH descriptors, outperforming cutting-edge methods on multiple benchmark datasets.

Cite this Paper

BibTeX
@InProceedings{pmlr-v231-chen24a, title = {EMP: Effective Multidimensional Persistence for Graph Representation Learning}, author = {Chen, Yuzhou and Segovia-Dominguez, Ignacio and Akcora, Cuneyt Gurcan and Zhen, Zhiwei and Kantarcioglu, Murat and Gel, Yulia and Coskunuzer, Baris}, booktitle = {Proceedings of the Second Learning on Graphs Conference}, pages = {24:1--24:12}, year = {2024}, editor = {Villar, Soledad and Chamberlain, Benjamin}, volume = {231}, series = {Proceedings of Machine Learning Research}, month = {27--30 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v231/chen24a/chen24a.pdf}, url = {https://proceedings.mlr.press/v231/chen24a.html}, abstract = {Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH), which furnishes an exclusive topological imprint of data by tracing the evolution of latent structures as a scale parameter changes. Present PH tools are confined to analyzing data through a single filter parameter. However, many scenarios necessitate the consideration of multiple relevant parameters to attain finer insights into the data. We address this issue by introducing the Effective Multidimensional Persistence (EMP) framework. This framework empowers the exploration of data by simultaneously varying multiple scale parameters. The framework integrates descriptor functions into the analysis process, yielding a highly expressive data summary. It seamlessly integrates established single PH summaries into multidimensional counterparts like EMP Landscapes, Silhouettes, Images, and Surfaces. These summaries represent data’s multidimensional aspects as matrices and arrays, aligning effectively with diverse ML models. We provide theoretical guarantees and stability proofs for EMP summaries. We demonstrate EMP’s utility in graph classification tasks, showing its effectiveness. Results reveal EMP enhances various single PH descriptors, outperforming cutting-edge methods on multiple benchmark datasets.} }
Endnote
%0 Conference Paper %T EMP: Effective Multidimensional Persistence for Graph Representation Learning %A Yuzhou Chen %A Ignacio Segovia-Dominguez %A Cuneyt Gurcan Akcora %A Zhiwei Zhen %A Murat Kantarcioglu %A Yulia Gel %A Baris Coskunuzer %B Proceedings of the Second Learning on Graphs Conference %C Proceedings of Machine Learning Research %D 2024 %E Soledad Villar %E Benjamin Chamberlain %F pmlr-v231-chen24a %I PMLR %P 24:1--24:12 %U https://proceedings.mlr.press/v231/chen24a.html %V 231 %X Topological data analysis (TDA) is gaining prominence across a wide spectrum of machine learning tasks that spans from manifold learning to graph classification. A pivotal technique within TDA is persistent homology (PH), which furnishes an exclusive topological imprint of data by tracing the evolution of latent structures as a scale parameter changes. Present PH tools are confined to analyzing data through a single filter parameter. However, many scenarios necessitate the consideration of multiple relevant parameters to attain finer insights into the data. We address this issue by introducing the Effective Multidimensional Persistence (EMP) framework. This framework empowers the exploration of data by simultaneously varying multiple scale parameters. The framework integrates descriptor functions into the analysis process, yielding a highly expressive data summary. It seamlessly integrates established single PH summaries into multidimensional counterparts like EMP Landscapes, Silhouettes, Images, and Surfaces. These summaries represent data’s multidimensional aspects as matrices and arrays, aligning effectively with diverse ML models. We provide theoretical guarantees and stability proofs for EMP summaries. We demonstrate EMP’s utility in graph classification tasks, showing its effectiveness. Results reveal EMP enhances various single PH descriptors, outperforming cutting-edge methods on multiple benchmark datasets.
APA
Chen, Y., Segovia-Dominguez, I., Akcora, C.G., Zhen, Z., Kantarcioglu, M., Gel, Y. & Coskunuzer, B.. (2024). EMP: Effective Multidimensional Persistence for Graph Representation Learning. Proceedings of the Second Learning on Graphs Conference, in Proceedings of Machine Learning Research 231:24:1-24:12 Available from https://proceedings.mlr.press/v231/chen24a.html.

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