Differential privacy from locally adjustable graph algorithms: k-core decomposition, low out-degree ordering, and densest subgraphs
Differentially private algorithms allow large-scale data analytics while preserving user
privacy. Designing such algorithms for graph data is gaining importance with the growth of …
privacy. Designing such algorithms for graph data is gaining importance with the growth of …
Chartalist: Labeled graph datasets for utxo and account-based blockchains
Abstract Machine learning on blockchain graphs is an emerging field with many applications
such as ransomware payment tracking, price manipulation analysis, and money laundering …
such as ransomware payment tracking, price manipulation analysis, and money laundering …
Hypercore decomposition for non-fragile hyperedges: concepts, algorithms, observations, and applications
Hypergraphs are a powerful abstraction for modeling high-order relations, which are
ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (ie, subsets of …
ubiquitous in many fields. A hypergraph consists of nodes and hyperedges (ie, subsets of …
A survey on dynamic graph processing on GPUs: concepts, terminologies and systems
H Gao, X Liao, Z Shao, K Li, J Chen, H Jin - Frontiers of Computer Science, 2024 - Springer
Graphs that are used to model real-world entities with vertices and relationships among
entities with edges, have proven to be a powerful tool for describing real-world problems in …
entities with edges, have proven to be a powerful tool for describing real-world problems in …
Experimental analysis and evaluation of cohesive subgraph discovery
Retrieving cohesive subgraphs in networks is a fundamental problem in social network
analysis and graph data management. These subgraphs can be used for marketing …
analysis and graph data management. These subgraphs can be used for marketing …
Data depth and core-based trend detection on blockchain transaction networks
Blockchains are significantly easing trade finance, with billions of dollars worth of assets
being transacted daily. However, analyzing these networks remains challenging due to the …
being transacted daily. However, analyzing these networks remains challenging due to the …
A topological approach for capturing high-order interactions in graph data with applications to anomaly detection in time-varying cryptocurrency transaction graphs
U Islambekov, H Pathirana, O Khormali… - Foundations of Data …, 2024 - aimsciences.org
Time-varying graphs are increasingly common in financial, social and biological data
analysis applications. Feature extraction that efficiently encodes the complex structure of …
analysis applications. Feature extraction that efficiently encodes the complex structure of …
A fast topological approach for predicting anomalies in time-varying graphs
U Islambekov, H Pathirana, O Khormali… - arXiv preprint arXiv …, 2023 - arxiv.org
Large time-varying graphs are increasingly common in financial, social and biological
settings. Feature extraction that efficiently encodes the complex structure of sparse, multi …
settings. Feature extraction that efficiently encodes the complex structure of sparse, multi …
[PDF][PDF] Uncovering Fraudulent Activities in Ethereum-based Cryptoassets with Distributed Ledger Analytics
F Victor - depositonce.tu-berlin.de
Apart from the popular cryptocurrency Bitcoin, thousands of cryptoassets attract varying
degrees of attention. The increasing complexity of distributed ledger technologies and …
degrees of attention. The increasing complexity of distributed ledger technologies and …