The Challenge of Graph Data Processing 📊

Graphs consist of nodes and edges, where nodes can be any objects (e.g., users in a social network), and edges show the connections between them (e.g., friendships). Such data structures require specialized approaches for analysis.

🧵 thread 🧵

Ethereum can be represented as a graph, where each participant in a tx corresponds to a node, and each transaction is an edge.
There can be multiple txs between two parties in both directions. Based on this system, a prediction market can be built using GNNs, analyzing users and their participants.

1/4

Advantages of GNN 💡

Graph Neural Network is a type of neural network designed to work directly with graph-structured data. GNN opens up new features for data analysis, where info is presented in complex and heterogeneous structures. GNN helps to take into account the context and relationships between elements.

2/4

GNN cases📌

Physics: Modeling interactions between objects in physical systems

Protein Interface Prediction: An important task for drug development

Financial Markets: Price analysis and prediction, risk management

Fraud Detection: Creating systems to predict fraud transactions, anomalies, etc

3/4

There are very few cases of GNN model in crypto, with POND being the only one I've seen. Pond is designed to learn on-chain behaviors and predict future behaviors in blockchain.

https://doc.cryptopond.xyz/docs/the-endgame-for-the-brave-a-crypto-native-foundation-model

4/4