Related papers: IOTA-based Directed Acyclic Graphs without Orphans
IOTA opened recently a new line of research in distributed ledgers area by targeting algorithms that ensure a high throughput for the transactions generated in IoT systems. Transactions are continuously appended to an acyclic structure…
We show that every directed graph $G$ with $n$ vertices and $m$ edges admits a directed acyclic graph (DAG) with $m^{1+o(1)}$ edges, called a DAG projection, that can either $(1+1/\text{polylog} (n))$-approximate distances between all pairs…
Nowadays, blockchain is becoming a synonym for distributed ledger technology. However, blockchain is only one of the specializations in the field and is currently well-covered in existing literature, but mostly from a cryptographic point of…
Financial fraud detection is essential to safeguard billions of dollars, yet the intertwined entities and fast-changing transaction behaviors in modern financial systems routinely defeat conventional machine learning models. Recent…
Sharding has emerged as a critical technique for enhancing blockchain system scalability. However, existing sharding approaches face unique challenges when applied to Directed Acyclic Graph (DAG)-based protocols that integrate expressive…
Directed graphs naturally model systems with asymmetric, ordered relationships, essential to applications in biology, transportation, social networks, and visual understanding. Generating such graphs enables tasks such as simulation, data…
Graph transaction processing raises many unique challenges such as random data access due to the irregularity of graph structures, low throughput and high abort rate due to the relatively large read/write sets in graph transactions. To…
The specific characteristics of graph workloads make it hard to design a one-size-fits-all graph storage system. Systems that support transactional updates use data structures with poor data locality, which limits the efficiency of…
Community detection on attributed graphs with rich semantic and topological information offers great potential for real-world network analysis, especially user matching in online games. Graph Neural Networks (GNNs) have recently enabled…
Processing, managing, and analyzing dynamic graphs are the cornerstone in multiple application domains including fraud detection, recommendation system, graph neural network training, etc. This demo presents GTX, a latch-free…
The paper formalizes a version of parallel online directed acyclic graph (DAG) exploration, general enough to be readily mapped to many computational scenarios. In both the offline and online versions, vertices are weighted with the work…
The blockchain brought interesting properties for many practical applications. However, some properties, such as the transaction processing throughput remained limited, especially in Proof-of-Work blockchains. Therefore, several promising…
Blockchain assets are increasingly controlled by organizations rather than individuals. DAO treasuries, consortium wallets, and custodial exchanges rely on threshold authorization and multi-party key management, yet existing payment…
Directed Acyclic Graphs (DAGs) are solid structures used to describe and infer the dependencies among variables in multivariate scenarios. Having a thorough comprehension of the accurate DAG-generating model is crucial for causal discovery…
Assuming a directed acyclic graph (DAG) that represents prior knowledge of causal relationships between variables is a common starting point for cause-effect estimation. Existing literature typically invokes hypothetical domain expert…
Tangle is a distributed ledger technology that stores data as a directed acyclic graph (DAG). Unlike blockchain, Tangle does not require dedicated miners for its operation; this makes Tangle suitable for Internet of Things (IoT)…
This paper introduces Slipstream, a Byzantine Fault Tolerance (BFT) protocol where nodes concurrently propose blocks to be added to a Directed Acyclic Graph (DAG) and aim to agree on block ordering. Slipstream offers two types of block…
Causal discovery, the learning of causality in a data mining scenario, has been of strong scientific and theoretical interest as a starting point to identify "what causes what?" Contingent on assumptions and a proper learning algorithm, it…
Blockchain has been widely deployed in various sectors, such as finance, education, and public services. Since blockchain runs as an immutable distributed ledger, it has decentralized mechanisms with persistency, anonymity, and…
Directed Acyclic Graphs (DAGs) provide a powerful framework to model causal relationships among variables in multivariate settings; in addition, through the do-calculus theory, they allow for the identification and estimation of causal…