Nomis serves as a powerful outsourced analytics tool, streamlining campaign management and allowing project teams to concentrate on core development and strategic goals.

Its core feature, Nomis Score, is crucial for efficiently and transparently collecting user activity data. This scoring system not only accurately identifies user contributions but also ensures precise reward distribution, creating a fair and productive environment for all stakeholders. Thus, Nomis enhances productivity and supports the effective execution of campaigns.

At Nomis, we specialize in helping web3 projects identify and tackle bot farms through our advanced and unique cluster analytics methods. The technology itself provides a clear view of suspicious patterns that could harm a project's integrity, token distribution and user experience.

How Nomis Can Identify Bot Farms: Methods and Tools

Cluster Analytics

Cluster analytics is a method used in data analysis to group a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense) to each other than to those in other groups. In the context of blockchain, cluster analytics involves the aggregation of wallet addresses based on shared transactional behaviors or attributes.

The process involves multiple steps:

  1. Data Collection: Gathering transaction data from blockchain networks.
  2. Feature Extraction: Identifying and extracting relevant features from the transactions that can indicate behavioral patterns, such as transaction frequency, amounts, and timestamps.
  3. Normalization: Standardizing the data to ensure that the scale of values does not distort the analysis.
  4. Clustering Algorithm Application: Applying the chosen clustering algorithm to group wallets based on the extracted and normalized features.
  5. Analysis: Analyzing the resultant clusters to identify patterns, anomalies, or typical behaviors that characterize each cluster.

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The primary advantage of cluster analytics in blockchain is its ability to detect groups of wallets that exhibit coordinated behaviors, which can be indicative of bot networks or collusive activities, thus enabling projects to safeguard their ecosystems against manipulative practices.

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Graph Analytics

Graph analytics, on the other hand, involves the study of relationships between entities using graph theory. Entities are represented as nodes, and the relationships between them are edges in a graph. This form of analytics is particularly useful in understanding the complex interconnections within data, which in the case of blockchain, helps illustrate how wallets are interconnected through transactions.

Key components of graph analytics include:

  1. Graph Construction: Creating a graph where each node represents a wallet, and each edge represents a transaction between wallets.