Social network based blockchain storage method and system

Abstract

This invention provides a blockchain storage method and system based on social networks. The method includes: constructing a network transaction graph based on node transaction data, mining the social relationships between nodes from the graph to represent the probability of future transactions; dividing nodes into different communities based on social relationships, with each node storing related transactions from nodes within the same community, avoiding the burden of storing the entire blockchain ledger; modeling the total storage and query costs of the entire network into a multi-objective optimization model based on the storage and query costs of individual nodes, minimizing the storage and query costs in the network; and employing a multi-swarm ant colony algorithm to solve the problem, allocating transaction storage locations by adjusting the storage saving rate and query cost tolerance rate to achieve a balance between storage and query costs in the network. This invention solves the technical problems of high node storage burden, low storage and query efficiency, and low degree of storage cost balance.

Description

Technical Field

[0001] This invention relates to the field of blockchain data processing, and more specifically to a blockchain storage method and system based on social networks. Background Technology

[0002] Blockchain is a distributed ledger technology that enables decentralized, tamper-proof, and trustworthy data storage and transaction verification. It revolutionizes traditional data management, providing secure, efficient, and reliable data exchange, empowering individuals and organizations with greater control, and improving transaction transparency and traceability. As a revolutionary technology, blockchain holds the promise of driving further social and economic development.

[0003] As the number of blockchain users increases and application areas expand, scalability has become a bottleneck in its development. Data storage plays a crucial and inseparable role in blockchain scalability. Nodes in a blockchain network need to maintain a complete data ledger, and increased user transaction volume inevitably increases the overhead of node data storage. Furthermore, the cost of retrieving specific data also increases. This prevents resource-constrained devices (such as mobile phones and tablets) from joining the blockchain network. In addition, as blockchain network throughput increases, the storage capacity requirements of devices become more critical, increasing the burden on blockchain nodes to store and retrieve massive amounts of data. In extreme cases, the number of nodes capable of handling the storage and retrieval overhead in a blockchain network may gradually decrease, causing the blockchain to evolve from a distributed to a centralized network, clearly contradicting the decentralized nature of blockchain.

[0004] To improve the scalability of blockchain systems in terms of data storage, current work mainly focuses on on-chain storage and off-chain storage. For example, the existing publication "Research on Blockchain Storage Scalability Technology and Its Application" proposes a storage expansion scheme based on application data pruning. This scheme expands storage by pruning unspent transaction outputs (UTXOs) that have a low probability of being spent in the future. Through statistical analysis of a large amount of UTXO set data across different block heights in real system history, the corresponding lifetime distribution is obtained, and a pruning strategy for UTXO-type application data sets is proposed. First, to record the distribution of the lifetimes of all spent UTXOs within the pruning period, a dynamic update algorithm for the UTXO distribution is proposed. During block generation, this algorithm calculates the lifetimes of UTXOs referenced in on-chain transactions based on the distribution inherited from the previous block and pruning boundary parameters, updates the corresponding distribution, and packages it into the block to achieve verifiable and continuous transmission of the distribution state at each block height within the pruning period. Furthermore, to ensure the stability of the pruning results and conformity to transaction patterns, a dynamic pruning algorithm for UTXO sets is proposed. This algorithm, based on the lifetime distribution of UTXOs, prunes UTXOs with a low probability of being spent in the future according to periodic pruning boundary parameters, while simultaneously updating the pruning boundary parameters. This paper conducts a security analysis of the proposed storage expansion scheme, proving that the pruned data still possesses integrity and verifiability. Experimental results also show that the expansion scheme, while performing high-intensity pruning on the complete UTXO set, ensures the stability and accuracy of the pruned data. On-chain storage primarily processes data using methods such as light nodes / full nodes, pruning, encoding, and sharding. Off-chain storage, on the other hand, moves data outside the blockchain (such as to a third-party server) to achieve off-chain processing and on-chain verification, or integrates the blockchain into an existing distributed database to achieve hybrid distributed database access control and data management. However, most of these approaches rely on trust assumptions and may cause excessive load on network bandwidth and computing resources. For example, the existing invention patent application document CN115547369A, entitled "A Blockchain-Based Distributed Storage Device," describes a device comprising: a PC, a server unit, a storage unit, a detection module, a control module, and an environmental parameter adjustment module. The server unit is equipped with a blockchain distributed storage network. The blockchain distributed storage network is used to store data uploaded from the PC within the storage unit. The detection module is located within a preset range of the storage unit and is used to detect parameters of the environment in which the storage unit is located. The control module and the environmental parameter adjustment module are connected. The control module controls the environmental parameter adjustment module based on the parameters of the environment in which the storage unit is located. The environmental parameter adjustment module adjusts the parameters of the environment in which the storage unit is located.The aforementioned EBSF divides nodes into committees, where nodes in a committee jointly store the complete blockchain ledger data, thus reducing the amount of data each node shares. However, the nodes within a committee rely on the assumption of mutual trust, which is difficult to achieve in reality. Furthermore, the paper does not specify how to divide nodes to improve trust among them. Some strategies compress data by encoding blocks, allowing nodes to recover the original block data by decoding the encoded data, a process that consumes network computing resources. Additionally, off-chain storage, since it processes transactions outside the blockchain, generally requires a third-party guarantor to verify the transaction's correctness; in this scenario, the trust of on-chain nodes in the guarantor is particularly important. To avoid trust-based limitations on data storage, Jidar allows nodes to store only transactions of interest and related Merkle branches. For example, the existing invention patent application document CN116303329A, "A Distributed Storage Method Based on Blockchain," includes: S1. The front end segments the file to obtain the content identifier of each byte segment. S2. Content identifier for all byte fragments Construct a Merkle tree to obtain the root. ,this That is, the entire file S3. Obtain online storage node information, and match the node address hash with the file... S4. Perform an XOR operation to obtain the distance between each node and the file; S5. Based on the distance, select one or more of the closest nodes for storage. However, the text does not specify how to determine the transactions of interest; it simply stores the transactions associated with the nodes.

[0005] In summary, existing technologies suffer from several technical problems, including heavy node storage burden, low storage and query efficiency, and uneven distribution of storage costs. Summary of the Invention

[0006] The technical problem to be solved by this invention is: how to solve the technical problems of large node storage burden, low storage and query efficiency, and low degree of balance of storage costs in the prior art.

[0007] This invention solves the above-mentioned technical problems by employing the following technical solution: A blockchain storage method based on social networks includes:

[0008] S1. Collect node transaction data, construct a network transaction graph based on the node transaction data, mine the social distance between nodes from the network transaction graph to obtain social relationship data and the probability of transactions, and construct a multi-objective optimization problem for node allocation;

[0009] S2. Based on social relationships, divide the nodes in the network transaction graph to obtain different communities, such that the nodes store related transactions of nodes in the same community, and solve the multi-objective optimization problem.

[0010] S3. Based on the single-node storage cost and single-node query cost, model the overall network storage cost and overall network query cost in the network transaction graph into a multi-objective optimization model to minimize the overall network storage cost and overall network query cost in the network transaction graph.

[0011] S4. Employ multi-group ant colony algorithms to solve the overall network storage cost and overall network query cost, and allocate transaction storage locations to balance the storage cost and query cost of the network transaction graph.

[0012] This invention designs blockchain data storage technology based on social networks. It allocates nodes to specific communities according to their social relationships. By modeling storage costs and query costs, it constructs a node allocation optimization problem, thereby achieving data storage optimization and reaching a balance between storage costs and query costs.

[0013] This invention realizes blockchain storage based on social networks. By combining the social relationships of users in the blockchain network, this invention jointly models the network's storage and query costs, and uses a multi-population genetic ant colony algorithm to solve a multi-objective optimization problem, thereby minimizing the overall storage cost while ensuring that the query cost remains within a tolerable range.

[0014] In a more specific technical solution, S1 includes:

[0015] S11. Set the amount of data stored by all nodes in the network transaction graph as the overall network storage cost, and minimize the overall network storage cost.

[0016] S12. Define the query cost as the consumption parameter generated when the node obtains transaction data from other nodes in the network transaction graph, set it as the overall network query cost, and minimize the overall network query cost.

[0017] S13. Define constraints on the amount of data stored by nodes and their membership relationships within time slots.

[0018] This invention proposes a blockchain data storage technology based on social networks. It improves the storage and retrieval efficiency of the blockchain system by mining the social relationships between users within the blockchain network.

[0019] In a more specific technical solution, S11 utilizes the following logic to minimize the overall storage cost of the processing network:

[0020]

[0021] In the formula, For the number of communities, For the number of nodes, For nodes Related data volume, variables Represents a node Is it in the community? In, variables Represents a node With the community The subordinate relationship.

[0022] In a more specific technical solution, in S12, the consumption parameters include: network consumption and the consumption of nodes processing query requests.

[0023] In a more specific technical solution, S12 utilizes the following logic to minimize the overall query cost of the processing network:

[0024]

[0025] In the formula, For the number of nodes, Represents a node The set of probabilities for visiting other nodes, arbitrary. , representing a node Access Node The probability, For nodes The amount of relevant data, For the node To the node The communication cost per unit of data volume, This indicates whether two nodes are located in the same community.

[0026] This invention defines transactions that users are interested in as including transactions they participate in and transactions with potential future partners. It innovatively applies social relationships to blockchain storage research to predict users' future transaction partners, thereby enabling better community segmentation of users in the network.

[0027] In a more specific technical solution, S2 includes:

[0028] S21. To address the multi-objective optimization problem of overall network storage cost and overall network query cost, create an ant colony;

[0029] S22. Set the storage saving rate and query cost growth rate, and obtain the threshold constraints for the overall network storage cost and the overall network query cost;

[0030] S23, enables the storage population Query population Each entity performs a solution search operation within its own search space to obtain the stored population solution.

[0031] S24. Calculate the solution X for the stored population;

[0032] Specifically, X is an n x m matrix consisting of 0s and 1s, where n is the number of nodes in the network and m is the number of communities, recording the community a node belongs to. Based on the solution X, the storage cost can be calculated using the following formula:

[0033]

[0034] In the formula, For the number of communities, For the number of nodes, For nodes Related data volume, variables Represents a node Is it in the community? In, variables Represents a node With the community The membership relationship, and the variables The value in the i-th row and k-th column of matrix X. Similarly, the query cost can be calculated:

[0035]

[0036] In the formula, For the number of nodes, Represents a node The set of probabilities for visiting other nodes, arbitrary. , representing a node Access Node The probability, For nodes The amount of relevant data, For the node To the node The communication cost per unit of data volume, This indicates whether two nodes belong to the same community, i.e., whether the i-th and j-th rows of matrix X have the same column value of 1. This yields the storage cost and query cost for storing the population solution, which are then compared with the constraints.

[0037] S25. Obtain and process the stored population based on the comparison results. The pheromone data that is maintained.

[0038] In this invention, users are treated as nodes in the blockchain network, and each node selects data of interest to store. This interest primarily refers to transaction data involving the user and transaction data that may be used in the future. This approach reduces the burden on nodes storing the entire ledger while also respecting the nodes' preferred data storage methods.

[0039] In a more specific technical solution, S21, the ant colony includes: a storage colony. Query population .

[0040] In a more specific technical solution, S25 involves updating, discounting, mutating, and crossing pheromone data.

[0041] This invention jointly models and solves the storage cost and query cost of network nodes, and proves that this multi-objective optimization problem is NP-hard. By setting the storage saving rate and the query cost growth rate, the range of community size and the community allocation of network nodes can be obtained, thereby minimizing the overall storage cost while ensuring that the query cost is within a tolerable range.

[0042] In a more specific technical solution, S4 adjusts the storage saving rate and query cost tolerance rate to allocate transaction storage locations.

[0043] In more specific technical solutions, blockchain storage systems based on social networks include:

[0044] The node social relationship mining module is used to collect node transaction data, construct a network transaction graph based on the node transaction data, mine the social distance between nodes from the network transaction graph to obtain social relationship data, the probability of transactions, and construct a multi-objective optimization problem for node allocation;

[0045] The multi-objective optimization problem solving module is used to divide the nodes in the network transaction graph according to social relationships, obtain different communities, and solve the multi-objective optimization problem by storing related transactions of nodes in the same community. The multi-objective optimization problem solving module is connected to the node social relationship mining module.

[0046] The storage and query cost optimization module is used to model the overall storage cost and overall query cost of the network in the network transaction graph into a multi-objective optimization model based on the storage cost and query cost of a single node. The storage and query cost optimization module is connected to the multi-objective optimization problem solving module.

[0047] The cost balancing module is used to solve the overall storage cost and overall query cost of the network using multiple ant colony algorithms, and to allocate transaction storage locations to balance the storage cost and query cost of the network transaction graph. The cost balancing module is connected to the storage and query cost optimization module.

[0048] The present invention has the following advantages over the prior art:

[0049] This invention designs blockchain data storage technology based on social networks. It allocates nodes to specific communities according to their social relationships. By modeling storage costs and query costs, it constructs a node allocation optimization problem, thereby achieving data storage optimization and reaching a balance between storage costs and query costs.

[0050] This invention realizes blockchain storage based on social networks. By combining the social relationships of users in the blockchain network, this invention jointly models the network's storage and query costs, and uses a multi-population genetic ant colony algorithm to solve a multi-objective optimization problem, thereby minimizing the overall storage cost while ensuring that the query cost remains within a tolerable range.

[0051] This invention proposes a blockchain data storage technology based on social networks. It improves the storage and retrieval efficiency of the blockchain system by mining the social relationships between users within the blockchain network.

[0052] This invention defines transactions that users are interested in as including transactions they participate in and transactions with potential future partners. It innovatively applies social relationships to blockchain storage research to predict users' future transaction partners, thereby enabling better community segmentation of users in the network.

[0053] In this invention, users are treated as nodes in the blockchain network, and each node selects data of interest to store. This interest primarily refers to transaction data involving the user and transaction data that may be used in the future. This approach reduces the burden on nodes storing the entire ledger while also respecting the nodes' preferred data storage methods.

[0054] This invention jointly models and solves the storage cost and query cost of network nodes, and proves that this multi-objective optimization problem is NP-hard. By setting the storage saving rate and the query cost growth rate, the range of community size and the community allocation of network nodes can be obtained, thereby minimizing the overall storage cost while ensuring that the query cost is within a tolerable range.

[0055] This invention solves the technical problems of high node storage burden, low storage and query efficiency, and low degree of balance of storage costs in the prior art. Attached Figure Description

[0056] Figure 1 This is a schematic diagram of the basic steps of the blockchain storage method based on social networks according to Embodiment 1 of the present invention;

[0057] Figure 2 This is a schematic diagram of the data flow of the blockchain storage method based on social networks according to Embodiment 1 of the present invention;

[0058] Figure 3 This is a schematic diagram illustrating the specific steps of the node allocation optimization problem in Embodiment 1 of the present invention;

[0059] Figure 4 This is a schematic diagram illustrating the specific steps of solving a multi-objective optimization problem according to Embodiment 1 of the present invention;

[0060] Figure 5 This is a schematic diagram illustrating the principle of multi-objective optimization solution in Embodiment 1 of the present invention. Detailed Implementation

[0061] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0062] Example 1

[0063] like Figure 1 As shown, the blockchain storage method based on social networks provided by this invention includes the following basic steps:

[0064] Step S1: Construct a network transaction graph based on node transaction data, and extract the social distance between nodes from the graph;

[0065] like Figure 2 As shown, in this embodiment, the aforementioned social distance in the social network is the social relationship, and the social relationship represents the probability that a transaction may occur in the future.

[0066] like Figure 3 As shown, in this embodiment, the operation of constructing the node allocation optimization problem further includes the following specific steps:

[0067] Step S11: Define the storage cost as the amount of data stored by all nodes in the network;

[0068] In this embodiment, since the data stored by a node includes its own transactions as well as related transactions from other nodes in the same community, the storage cost of the entire network is related to the number of communities and the composition of nodes within those communities. The formula for minimizing the storage cost of the entire network is expressed as:

[0069]

[0070] in, For the number of communities, For the number of nodes, For nodes Related data volume, variables Represents a node Is it in the community? In the middle. Variables Represents a node Belonging to the community , and when When, it indicates a node Not part of the community .

[0071] Step S12: Define the query cost as the network consumption incurred by a node when obtaining transaction data from other nodes and the consumption incurred by a node when processing query requests;

[0072] In this embodiment, minimizing the query cost of the entire network is expressed as follows:

[0073]

[0074] in, For the number of nodes, Represents a node The set of probabilities for visiting other nodes, arbitrary. , representing a node Access Node The probability, For nodes The amount of relevant data, For the node To the node The communication cost per unit of data volume, This indicates whether two nodes are located in the same community. If the nodes are in the same community... and nodes If they are located in the same community, then ,on the contrary, .

[0075] Step S13: Define the constraints.

[0076] In this embodiment, the aforementioned restrictions include, but are not limited to: the amount of data stored by each node does not exceed the maximum capacity of the node, and each node can only belong to one community in the same time slot.

[0077] Step S2: Based on the social relationships of the nodes, divide the nodes in the network into different communities, and store the related transactions of the nodes in the same community;

[0078] In this embodiment, nodes within the same community have closer relationships, meaning they are more likely to intersect in the future. Nodes only need to store transactions related to nodes within the same community, avoiding the burden of storing the entire blockchain ledger.

[0079] like Figure 4 and Figure 5 As shown in this embodiment, the operation of solving the multi-objective optimization problem further includes the following specific steps:

[0080] Step S21: To address the two optimization problems of storage cost and query cost, create a storage population. and query population Two ant colonies;

[0081] Step S22: Set the storage saving rate and query cost growth rate, and calculate the threshold limits for storage cost and query cost;

[0082] Step S23: Let the storage population and the query population search for solutions within their respective search spaces;

[0083] Step S24: For the solutions found by the storage population, calculate the storage cost and query cost corresponding to the solutions, compare them with the constraints, and perform update, discount, mutation and crossover operations on the pheromone data maintained by the storage population according to the comparison results.

[0084] Step S3: Based on the storage and query costs of a single node, model the total storage and query costs of the entire network into a multi-objective optimization model to minimize the storage and query costs in the network.

[0085] Step S4: Use multiple ant colony algorithms to solve for storage cost and query cost, allocate the storage location of transactions, and balance the storage cost and query cost in the network.

[0086] In this embodiment, since the aforementioned problem is NP-hard, multiple ant colony algorithms can be used to solve the problem. By adjusting the storage saving rate and the query cost tolerance rate, the storage location of transactions can be effectively allocated, thereby achieving a balance between storage cost and query cost in the network.

[0087] In summary, this invention designs blockchain data storage technology based on social networks. It allocates nodes to specific communities according to their social relationships. By modeling storage costs and query costs, it constructs a node allocation optimization problem, thereby achieving data storage optimization and reaching a balance between storage costs and query costs.

[0088] This invention realizes blockchain storage based on social networks. By combining the social relationships of users in the blockchain network, this invention jointly models the network's storage and query costs, and uses a multi-population genetic ant colony algorithm to solve a multi-objective optimization problem, thereby minimizing the overall storage cost while ensuring that the query cost remains within a tolerable range.

[0089] This invention proposes a blockchain data storage technology based on social networks. It improves the storage and retrieval efficiency of the blockchain system by mining the social relationships between users within the blockchain network.

[0090] This invention defines transactions that users are interested in as including transactions they participate in and transactions with potential future partners. It innovatively applies social relationships to blockchain storage research to predict users' future transaction partners, thereby enabling better community segmentation of users in the network.

[0091] In this invention, users are treated as nodes in the blockchain network, and each node selects data of interest to store. This interest primarily refers to transaction data involving the user and transaction data that may be used in the future. This approach reduces the burden on nodes storing the entire ledger while also respecting the nodes' preferred data storage methods.

[0092] This invention jointly models and solves the storage cost and query cost of network nodes, and proves that this multi-objective optimization problem is NP-hard. By setting the storage saving rate and the query cost growth rate, the range of community size and the community allocation of network nodes can be obtained, thereby minimizing the overall storage cost while ensuring that the query cost is within a tolerable range.

[0093] This invention solves the technical problems of high node storage burden, low storage and query efficiency, and low degree of balance of storage costs in the prior art.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A blockchain storage method based on social networks, characterized in that, The method includes: S1. Collect node transaction data, construct a network transaction graph based on the node transaction data, mine the social distance between nodes from the network transaction graph to obtain social relationship data and the probability of transaction occurrence, and construct a multi-objective optimization problem for node allocation; S1 includes: S11. Set the amount of data stored by all nodes in the network transaction graph as the overall network storage cost, and minimize the overall network storage cost. In S11, the overall storage cost of processing the network is minimized using the following logic: In the formula, For the number of communities, For the number of nodes, For nodes Related data volume, variables Represents a node Is it in the community? In, variables Represents a node With the community Subordinate relationship; S12. Define the query cost as the consumption parameter generated when the acquisition node obtains transaction data from other nodes in the network transaction graph, set it as the overall network query cost, and minimize the overall network query cost. The consumption parameters include: network consumption and the consumption of nodes processing query requests; The overall query cost of processing the network is minimized using the following logic: In the formula, For the number of nodes, Represents a node The set of probabilities for visiting other nodes, arbitrary. , representing a node Access Node The probability, For nodes The amount of relevant data, For the node To the node The communication cost per unit of data volume, Indicates whether two nodes are located in the same community; S13. Set restrictions on the amount of data stored by the nodes and their membership relationships in the same time slot; S2. Based on the social relationships, divide the nodes in the network transaction graph to obtain different communities, and solve the multi-objective optimization problem by storing related transactions of nodes in the same community. S3. Based on the single-node storage cost and single-node query cost, the overall network storage cost and overall network query cost in the network transaction graph are modeled into a multi-objective optimization model to minimize the overall network storage cost and overall network query cost in the network transaction graph. S4. Using a multi-group ant colony algorithm, solve the overall storage cost and overall query cost of the network, and allocate transaction storage locations to balance the storage cost and query cost of the network transaction graph.

2. The blockchain storage method based on social networks according to claim 1, characterized in that, S2 includes: S21. To address the multi-objective optimization problem of overall network storage cost and overall network query cost, create an ant colony; S22. Set the storage saving rate and the query cost growth rate, and obtain the threshold restrictions for the overall network storage cost and the overall network query cost; S23, enables the storage population The queried population Each entity performs a solution search operation within its own search space to obtain the stored population solution. S24. Calculate the storage population solution, obtain the corresponding storage cost and corresponding query cost of the storage population solution, and compare the corresponding storage cost and the corresponding query cost with the constraint conditions; S25. Obtain and process the stored population based on the comparison results. The pheromone data that is maintained.

3. The blockchain storage method based on social networks according to claim 2, characterized in that, In step S21, the ant population includes: a stored population. Query population .

4. The blockchain storage method based on social networks according to claim 2, characterized in that, In step S25, the pheromone data is updated, discounted, mutated, and crossed.

5. The blockchain storage method based on social networks according to claim 1, characterized in that, In step S4, the storage saving rate and query cost tolerance rate are adjusted to allocate the transaction storage location.

6. A blockchain storage system based on a social network, used to perform the method according to any one of claims 1 to 5, characterized in that, The system includes: The node social relationship mining module is used to collect node transaction data, construct a network transaction graph based on the node transaction data, mine the social distance between nodes from the network transaction graph to obtain social relationship data, the probability of transaction occurrence, and construct a multi-objective optimization problem for node allocation; A multi-objective optimization problem solving module is used to divide the nodes in the network transaction graph according to the social relationship to obtain different communities, so that the related transactions of the nodes in the same community stored by the nodes can solve the multi-objective optimization problem. The multi-objective optimization problem solving module is connected to the node social relationship mining module. The storage query cost optimization module is used to model the overall network storage cost and overall network query cost in the network transaction graph into a multi-objective optimization model based on the single node storage cost and single node query cost, so as to minimize the overall network storage cost and overall network query cost in the network transaction graph. The storage query cost optimization module is connected to the multi-objective optimization problem solving module. The cost balancing module is used to solve the overall storage cost and overall query cost of the network using multiple ant colony algorithms, and to allocate transaction storage locations to balance the storage cost and query cost of the network transaction graph. The cost balancing module is connected to the storage and query cost optimization module.

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