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Found 23 papers, 11 papers with code
EMP: Effective Multidimensional Persistence for Graph Representation Learning
This framework empowers the exploration of data by simultaneously varying multiple scale parameters.
Time-Aware Knowledge Representations of Dynamic Objects with Multidimensional Persistence
In particular, we propose a new approach, named \textit{Temporal MultiPersistence} (TMP), which produces multidimensional topological fingerprints of the data by using the existing single parameter topological summaries.
Topological Pooling on Graphs
By invoking the machinery of persistent homology and the concept of landmarks, we propose a novel topological pooling layer and witness complex-based topological embedding mechanism that allow us to systematically integrate hidden topological information at both local and global levels.
Seven open problems in applied combinatorics
We present and discuss seven different open problems in applied combinatorics.
Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT
Second, we introduce a pruning algorithm for graphs to compute their persistence diagrams by removing the dominated vertices.
Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets
Nowadays, it is broadly recognized in the power system community that to meet the ever expanding energy sector's needs, it is no longer possible to rely solely on physics-based models and that reliable, timely and sustainable operation of energy systems is impossible without systematic integration of artificial intelligence (AI) tools.
BScNets: Block Simplicial Complex Neural Networks
Simplicial neural networks (SNN) have recently emerged as the newest direction in graph learning which expands the idea of convolutional architectures from node space to simplicial complexes on graphs.
Topological Relational Learning on Graphs
As a result, the new framework enables us to harness both the conventional information on the graph structure and information on the graph higher order topological properties.
Using NASA Satellite Data Sources and Geometric Deep Learning to Uncover Hidden Patterns in COVID-19 Clinical Severity
As multiple adverse events in 2021 illustrated, virtually all aspects of our societal functioning -- from water and food security to energy supply to healthcare -- more than ever depend on the dynamics of environmental factors.
Z-GCNETs: Time Zigzags at Graph Convolutional Networks for Time Series Forecasting
There recently has been a surge of interest in developing a new class of deep learning (DL) architectures that integrate an explicit time dimension as a fundamental building block of learning and representation mechanisms.
Smart Vectorizations for Single and Multiparameter Persistence
We derive theoretical guarantees on the stability of the new saw and multi-persistence grid functions and illustrate their applicability for graph classification tasks.
Modeling Weather-induced Home Insurance Risks with Support Vector Machine Regression
Insurance industry is one of the most vulnerable sectors to climate change.
Ensemble Forecasting of the Zika Space-TimeSpread with Topological Data Analysis
As per the records of theWorld Health Organization, the first formally reported incidence of Zika virus occurred in Brazil in May 2015.
How to Not Get Caught When You Launder Money on Blockchain?
The number of blockchain users has tremendously grown in recent years.
LFGCN: Levitating over Graphs with Levy Flights
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds, coined Geometric Deep Learning (GDL), continues to gain an ever increasing interest.
Defending against Backdoors in Federated Learning with Robust Learning Rate
In addition, we also provide convergence rate analysis for our proposed scheme.
Dissecting Ethereum Blockchain Analytics: What We Learn from Topology and Geometry of Ethereum Graph
Blockchain technology and, in particular, blockchain-based cryptocurrencies offer us information that has never been seen before in the financial world.
Harnessing the power of Topological Data Analysis to detect change points in time series
While the applications of topological data analysis to change point detection are potentially very broad, in this paper we primarily focus on integrating topological concepts with the existing nonparametric methods for change point detection.
Fractional Graph Convolutional Networks (FGCN) for Semi-Supervised Learning
Due to high utility in many applications, from social networks to blockchain to power grids, deep learning on non-Euclidean objects such as graphs and manifolds continues to gain an ever increasing interest.
ChainNet: Learning on Blockchain Graphs with Topological Features
With emergence of blockchain technologies and the associated cryptocurrencies, such as Bitcoin, understanding network dynamics behind Blockchain graphs has become a rapidly evolving research direction.
BitcoinHeist: Topological Data Analysis for Ransomware Detection on the Bitcoin Blockchain
To our knowledge, none of the previous approaches have employed advanced data analytics techniques to automatically detect ransomware related transactions and malicious Bitcoin addresses.
Topological Data Analysis
Cryptography and Security
Distributed, Parallel, and Cluster Computing
CRAD: Clustering with Robust Autocuts and Depth
We develop a new density-based clustering algorithm named CRAD which is based on a new neighbor searching function with a robust data depth as the dissimilarity measure.
Blockchain: A Graph Primer
Our goal is to provide a concise but complete description of blockchain technology that is accessible to readers with no prior expertise in the field.
Computers and Society
