What Is a Directed Acyclic Graph (DAG) in Cryptocurrency?

Introduction

When discussing cryptocurrencies, many people immediately think of blockchain or "distributed ledger technology." Since the inception of Bitcoin, hundreds of cryptocurrencies have emerged in the market. Although most have similar network architectures, users can still transfer value or interact with decentralized applications (DApps) through these data structures.

In a blockchain, new blocks are added regularly to an ever-expanding chain, with each block linked to the previous one through some form of cryptographic connection (essentially a hash value). Each block contains the latest transactions published by users.

However, there is often a waiting period between when a transaction is published and when it is included in a block, much like waiting at a train station. Depending on the block size and the number of pending transactions, users may not be able to catch the next "train," and the time for transaction confirmation can vary from seconds to hours.

For many, despite the high level of security and the lack of reliance on centralized institutions, there are concerns that scalability issues with blockchain technology could hinder its widespread adoption. Meanwhile, supporters believe that future cryptocurrency payment networks will be built on a new architecture known as Directed Acyclic Graphs (DAG).

What is a Directed Acyclic Graph?

A Directed Acyclic Graph (DAG) is a novel data structure that can be viewed as a database connecting different pieces of information. The term "Directed Acyclic Graph" is rich in meaning, and we will break it down step by step.

Conceptually, a DAG consists of vertices (like spheres) and edges (like connections). Both elements are directional and do not form cycles, meaning that it is impossible to return to the starting vertex once you have left. This data structure is often used for data modeling, especially in scientific or medical fields, to observe the relationships between variables and their mutual effects. For instance, a DAG can be used to establish connections between nutrition, sleep cycles, and physical symptoms, clarifying how these factors impact patients.

Our primary focus is on how this structure can be used to achieve consensus in distributed cryptocurrency networks.

How Does a Directed Acyclic Graph Work?

In cryptocurrencies based on DAGs, each vertex represents a transaction. Unlike blockchain, there is no concept of blocks here, and there is no need for mining to expand the database. Therefore, transactions are not concentrated in blocks but are built on top of other transactions. When nodes submit transactions, they perform a small amount of proof-of-work to ensure that the network is not disturbed by spam while also validating previous transactions.

To add a new transaction, it must reference prior transactions. For instance, if Alice creates a new transaction, hers must reference previous transactions, similar to how a block in Bitcoin references the preceding block, but here it references multiple transactions.

In some systems, algorithms determine which "tip" transactions new transactions must build upon. The higher the cumulative weight of the tip transactions, the more likely they are to be selected. Cumulative weight measures the number of confirmed paths leading to the tip.

If the previous transaction that Alice's transaction needs to reference is not yet confirmed, once Alice references it, those transactions will gain confirmation. At this point, other users must create new transactions on top of hers before her transaction is accepted.

Users tend to confirm transactions with higher weights so that the system can continue to evolve. Otherwise, users might indiscriminately create new transactions on old transactions.

Blockchain effectively prevents double-spending issues because the same funds cannot be reused within a block, and nodes can easily detect such attempts and reject blocks containing conflicting transactions. The high cost for miners to generate blocks incentivizes fair competition.

DAGs can also prevent double-spending, with a similar mechanism but without the involvement of miners. When nodes confirm older transactions, they assess the entire path back to the first transaction in the DAG to ensure that the sender's balance is sufficient. While there may be many paths, only one needs to be verified.

If Users Build Transactions on Invalid Paths

If a user's transaction is built on an invalid path, it may be disregarded. Although the user's transaction itself is valid, the invalidity of the preceding transaction may lead other users in the network to choose not to extend this path.

At first glance, this seems unreasonable—do the different branches really not recognize each other's existence? Is it possible for users to spend the same funds on different branches?

While this possibility exists, it can be mitigated by assigning higher weights to the cumulative weights of the tips through the selection algorithm. Over time, one branch will thrive more than others, while weaker branches may be abandoned, allowing the network to continue developing on the branch with the highest weight.

Similar to blockchain, this network does not have absolute confirmations, and it can never be entirely guaranteed that a transaction will not be revoked. Although such cases are extremely rare, theoretically, blocks in Bitcoin or Ethereum can be "reversed," leading to the reversal of all transactions within them. As subsequent blocks are added, the security of transactions increases; this is why we recommend that users wait for six confirmations before investing.

In directed acyclic graphs like IOTA's Tangle, there is a concept known as "confirmation confidence." The selection algorithm runs 100 times, calculating the number of directly or indirectly approved transactions among the selected tips. The higher the percentage, the greater the confidence that the transaction remains in a "settled" state.

While this approach may seem to affect user experience, it actually does not. If Alice sends 10 MagicDAGTokens to Bob, she does not need to worry about selecting the correct tip, as her wallet will perform the following actions in the background:

Select tips with higher weights (i.e., those with the most cumulative confirmation information).

Trace back along the path to previous transactions to ensure the tip has sufficient balance for the payment.

Once these conditions are met, the transaction will be added to the directed acyclic graph and receive confirmation.

For Alice, this process is no different from conventional cryptocurrency transaction flows. She simply inputs Bob's address and the payment amount, then presses the send button. The steps above represent the proof-of-work that each participant must execute when creating a transaction.

Advantages and Disadvantages of Directed Acyclic Graphs

Advantages of Directed Acyclic Graphs

Speed

With no block time constraints, anyone can publish and process transactions at any time. As long as earlier transactions are confirmed, users are not limited in the number of submitted transactions.

No Mining Required

Directed acyclic graphs do not rely on traditional proof-of-work consensus algorithms. Compared to cryptocurrencies that depend on mining to maintain blockchain networks, DAGs have a significantly smaller carbon footprint.

No Transaction Fees

Since there are no miners involved, users do not have to pay fees when publishing transactions, although in some cases, a small fee may be required to certain types of nodes. This is particularly attractive for users making small payments, as high network fees can often render their efforts fruitless.

No Scalability Issues

Unlike traditional blockchain networks, directed acyclic graphs are not constrained by block times, enabling them to handle a higher volume of transactions. Many supporters believe this makes DAGs more valuable in scenarios that require extensive machine interaction, such as the Internet of Things (IoT).

Disadvantages of Directed Acyclic Graphs

Not Completely Decentralized

Protocols based on directed acyclic graphs exhibit certain centralized characteristics. Some consider this a short-term solution, but it remains to be seen whether DAGs can sustain themselves without third-party intervention. If unsuccessful, the network may face attack risks, ultimately leading to significant impacts.

Not Widely Tested

Although cryptocurrencies based on directed acyclic graphs have existed for several years, widespread adoption still requires time. Therefore, it is currently difficult to predict what incentive mechanisms future users will enjoy when using the system.

Conclusion

It is evident that directed acyclic graphs represent an interesting technology for building cryptocurrency networks. Although there are currently relatively few projects using this data structure and they remain immature, if DAGs can fully realize their potential, they will undoubtedly drive multiple scalability ecosystems. In fields requiring high throughput and low costs, such as the Internet of Things (IoT) and micropayments, DAG technology shows immense promise for application.

Risk Warning

While the cryptocurrency market offers significant growth potential and innovation opportunities, it also carries a high level of market risk and price volatility. The value of crypto assets can fluctuate dramatically in a short period, potentially leading to substantial financial losses for investors. Additionally, the cryptocurrency market faces multiple risk factors, including technical risks, legal and regulatory uncertainties, cybersecurity threats, and market manipulation. We strongly advise users to conduct thorough research and due diligence before making any investment decisions and to consult professional financial advisors. All investment decisions are made at the user’s own risk. Thank you for your trust and support of Venkate!

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