Learn how directed acyclic graphs offer a scalable, secure, and eco-friendly alternative to blockchain.
after reading this, you'll understand:
Directed acyclic graphs (DAGs) are good for mapping an efficient process.
Hashgraph and other DAGs are a viable replacement for blockchains due to their speed and data-storage capabilities.
DAG-based distributed ledgers consume less energy than blockchains.
Cryptocurrency is typically associated with blockchain technology, but directed acyclic graph (DAG) technology has become a compelling alternative. Many people are unfamiliar with DAGs and graph theory. If you are a cryptocurrency enthusiast, it is a good idea to learn more about DAGs, how they work, and how they may impact distributed ledger technology.
A DAG is a way to organize, show, or map a process that moves forward and has no part that loops back on itself.
Graphs consist of vertices (also called points or nodes) and edges (or lines). In a traditional graph, edges connect pairs of vertices and have no defined direction. In directed acyclic graphs, the lines indicate a path from one point to the next, with an arrow to indicate the direction of flow, and the lines never lead back to any point to form a loop.
To say a graph is "directed" means the edges have defined directions, and "acyclic graph" means there are no feedback loops.
DAGs have many uses, ranging from family trees to cryptocurrency. For example, directional graphs are often used for spreadsheet formulas involving multiple cells with causal relationships. If the value of cell A1 is dependent on the value of cell B2, and B2 is dependent on the value of two other cells, the spreadsheet must update those values in a specific order. In a case like this, a DAG is used to make sure the cells are updated correctly.
DAG-based systems have proven to be useful in many applications, including payment systems and financial networks. They offer:
Scalability. DAGs enable parallel processing of transactions, allowing for greater throughput than traditional blockchain systems. This parallel structure supports enterprise-level payment systems that require high transaction volumes.
Security. With their multiple validation points, DAGs make it more difficult for malicious actors to compromise the network.
Cost reduction. Without the need for traditional mining operations for validation and consensus, DAG-based systems significantly reduce operational costs and energy consumption, making them an attractive investment for environmentally conscious organizations.
Risk management. The structure of DAGs allows for better tracking and management of transaction dependencies, enabling more sophisticated risk management systems.
Why is this so? DAG-based distributed ledgers like Hedera, with its gossip protocol, enable nodes to communicate with each other about any and all new information they learn. It's very different from the linear arrangement that blockchain uses.
DAGs and blockchain are different types of distributed ledger technologies. Neither of these peer-to-peer networks requires a central validating authority. Instead, they use a consensus mechanism to achieve decentralization and to validate and record transactions. They also provide an immutable trail and transparency.
In a blockchain, blocks of information are validated, stored, and linked together in one chain. The two key consensus mechanisms are Proof of Work (PoW) and Proof of Stake (PoS).
In PoW, miners compete for the right to verify transactions by racing to be first to solve a complex mathematical puzzle. The rewards for winning are lucrative, so miners invest heavily to build data centers to solve the puzzles. These mining operations use massive amounts of electricity.
PoS networks rely on validators who are chosen based on the amount of cryptocurrency they hold and are willing to "stake." The more coins a validator stakes, the higher their chances of being selected and earning rewards.
Blockchain technologies allow validators to create only one block at a time. Validation of transactions relies on the creation of those blocks. Because DAGs have nodes that are developed simultaneously, transactions can be processed faster and with greater efficiency.
When one node in a DAG network becomes aware of new information, it will share it with a random node. That first node and the randomly chosen node then share that information with two other randomly chosen nodes that do the same. This process continues until every node is aware of the new information. And in this way, consensus is reached.
The history of these information-sharing events is referred to as gossip about gossip. The gossip about gossip history is represented as a type of DAG known as a hashgraph.
Some blockchain-based distributed ledgers are suitable for only high-value transactions due to their fee structure. Alternatively, ledgers that use the DAG model are ideal for transactions of all sizes. Since DAG-based distributed ledgers aren't reliant on miners, they have lower transaction fees and consume less energy.
To help get a grip on directed acyclic graphs, let's look at some of the basic terms.
We've already described the two most basic parts of a DAG: the vertex and the edge. Let's look at some other key elements that show how DAGs provide a useful data structure for mapping complex processes.
Topological sorting is the linear ordering of a DAG where every edge marks the end of one vertex and the beginning of another. This topological ordering is essential in computer science as it dictates that each node may be visited only after prior dependencies are completed.
In many ways, topological sorting is a type of schedule that ensures tasks are completed in a specific order. If, for example, you designed a cryptocurrency raffle tool, it would likely require a random number to be generated before a winner was awarded any tokens. A DAG illustrating the raffle process would include a vertex for random number generation with at least one edge pointing toward a vertex for awarding tokens.
It's easy to see how this would be useful in working with a smart contract, computer coding on a distributed ledger that automatically carries out the terms of an agreement once the criteria have been met.
Transitive closure is one of the concepts that separate directional acyclic graphs from a blockchain structure. When a transitive closure is constructed in a DAG, it may allow programs to reach nodes in fewer steps.
For example, if your program has four nodes and a linear structure, it could be assumed that you could only reach node four by starting at node one, followed by node two, node three, and then node four. However, a transitive closure could determine that you could jump straight from node one to node four.
A transitive reduction produces a graph with the fewest possible edges. The transitive reduction method is a helpful way to create DAGs from a partially ordered set.
Causal inference is the process of inferring that one thing is likely to be the cause of something else. For example, if you find food crumbs on your carpet, you could assume that was likely caused by someone eating.
Causal inference is often accomplished by analyzing responses of effect variables when the cause of the variable is altered.
A causal effect is when something happens because of something else that occurred. For example, a DAG for financial transactions would show that a product is delivered because digital assets have been received as payment. Causal effect is an important element of DAGs, as each node may influence the way the following node works.
Causal relationships exist when one variable directly influences another. For example, each payment of the correct amount causes a product to be delivered.
Instrumental variables can help determine causal relationships when controlled experiments aren't practical. When trying to determine the causal effect of one variable (variable X) on another (variable Y), an instrumental variable is a third variable (variable Z) that can impact X via its effect on Y or vice versa.
Observational data is data collected based on the observation of a particular subject where the subject doesn't have to be directly involved in the data collection process. DAGs can be an excellent tool for mapping the causal effect of observational data.
DAG-based distributed ledgers offer a promising future for the digital currency ecosystem. Hedera is a leader in DAG-based technology thanks to its hashgraph consensus mechanism and gossip protocol. Developers are using Hedera's powerful tools to lead innovation in supply chain management, financial markets, and many other areas.
Hedera's DAG-based distributed technology offers low, predictable fees and lighting-fast transactions. And because Hedera is fully compatible with the Ethereum Virtual Machine, developers can work in a DAG environment with no trouble.