Learning metrics for persistence-based summaries and applications for graph classification
Q Zhao, Y Wang - Advances in neural information …, 2019 - proceedings.neurips.cc
Recently a new feature representation and data analysis methodology based on a
topological tool called persistent homology (and its persistence diagram summary) has …
topological tool called persistent homology (and its persistence diagram summary) has …
Link prediction with persistent homology: An interactive view
Link prediction is an important learning task for graph-structured data. In this paper, we
propose a novel topological approach to characterize interactions between two nodes. Our …
propose a novel topological approach to characterize interactions between two nodes. Our …
Persistence augmented graph convolution network for information popularity prediction
Y Zeng, K Xiang - IEEE Transactions on Network Science and …, 2023 - ieeexplore.ieee.org
In recent years, information popularity prediction of online social media plays a vital role in
crisis early warning and malicious content propagation identification within public opinion …
crisis early warning and malicious content propagation identification within public opinion …
A persistent homology perspective to the link prediction problem
Persistent homology is a powerful tool in Topological Data Analysis (TDA) to capture
topological properties of data succinctly at different spatial resolutions. For graphical data …
topological properties of data succinctly at different spatial resolutions. For graphical data …
[PDF][PDF] Persistence homology for link prediction: an interactive view
Link prediction is an important learning task for graph-structured data. In this paper, we
propose a novel topological approach to characterize interactions between two nodes. Our …
propose a novel topological approach to characterize interactions between two nodes. Our …
[图书][B] Combining Geometric and Topological Ideas with Machine Learning for Analyzing Complex Data
Q Zhao - 2022 - search.proquest.com
Recently there has been an increasing number of learning problems arising in complex data
domains, like graph-structured data, such as social networks and knowledge graphs …
domains, like graph-structured data, such as social networks and knowledge graphs …
Cover Filtration and Stable Paths in the Mapper
The contributions of this paper are two-fold. We define a new filtration called the cover
filtration built from a single cover based on a generalized Steinhaus distance, which is a …
filtration built from a single cover based on a generalized Steinhaus distance, which is a …
Steinhaus Filtration and Stable Paths in the Mapper
DL Arendt, M Broussard, B Krishnamoorthy… - arXiv preprint arXiv …, 2019 - arxiv.org
Two central concepts from topological data analysis are persistence and the Mapper
construction. Persistence employs a sequence of objects built on data called a filtration. A …
construction. Persistence employs a sequence of objects built on data called a filtration. A …