# 🏆 Start Here: Register & Get Aura Credits: Aura Agent Hackathon

**URL:** <https://community.neo4j.com/t/start-here-register-get-aura-credits-aura-agent-hackathon/77191>\
**Category:** Aura Agent Hackathon 2026\
**Created:** [April 13, 2026, 2:56pm UTC](https://community.neo4j.com/t/start-here-register-get-aura-credits-aura-agent-hackathon/77191 "2026-04-13T14:56:02Z")\
**Posts on this page:** 1\
**Showing post:** 100

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**Author:** ![agung\_sidharta\_gungz](https://sea1.discourse-cdn.com/flex021/user_avatar/community.neo4j.com/agung_sidharta_gungz/32/36925_2.png) [@agung\_sidharta\_gungz](https://community.neo4j.com/u/agung_sidharta_gungz)\
**Post date:** [June 15, 2026, 6:54pm UTC](https://community.neo4j.com/t/start-here-register-get-aura-credits-aura-agent-hackathon/77191/100 "2026-06-15T18:54:36Z")

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### 🏆 **Agent Name**

Sybil-Hunter: E-Commerce Review Fraud Network Agent

### 💻 **What it does**

Sybil-Hunter is an intelligent AI agent built to uncover astroturfing, coordinated fake review rings, and Sybil attacks on e-commerce platforms. While traditional relational databases or flat dashboards can easily flag an isolated 5-star or 1-star review, they fail to see structural collusion where networks of fake accounts coordinate to manipulate product ratings.

Using multi-hop graph reasoning, Sybil-Hunter crawls connection paths to find groups of distinct user accounts that consistently review the exact same products within narrow time frames. The agent acts as an automated investigator: users can ask natural language questions like _"Check whether this user is a sybil"_ and the agent translates this directly into a structural graph traversal, surfacing hidden fraud rings instantly.

### 📊 **Dataset and why a graph fits**

- **Dataset:** A dense, highly connected subset of the **Amazon Fine Food Reviews dataset** from Kaggle, containing user profiles, unique product IDs, ratings, timestamps, and review data.

- **Why a graph fits:** Fraud is fundamentally a structural problem, not an isolated data point. In a relational database, finding a ring of X users who colluded to review the same 5 products requires massive, multi-way self-joins and sub-queries that could break at scale.

In Neo4j, this is a clean, natural traversal. By structuring the data as: `(:User)-[:POST]->(:Review)-[:ABOUT]->(:Product)`

The agent can use graph topology to look for closed loops and tightly knit clusters (e.g., matching common paths where `User A` and `User B` share multiple `Product` leaf nodes). A graph doesn't just calculate a statistical correlation; it provides an explicit, audit-ready chain of relationships explaining exactly _why_ a group of accounts is flagged as a coordinated Sybil network.

### 📸 **Screenshot of your agent in the Aura console**

 ![image](https://us1.discourse-cdn.com/flex021/uploads/neo4jcommunity/original/3X/6/d/6d21bd2bd8724ccc66d6888290ea37785866fb0a.png)

### 🎥 **Screenshot or short demo of your agent in action**

 ![image](https://us1.discourse-cdn.com/flex021/uploads/neo4jcommunity/original/3X/1/f/1f0f59bbd7d2deeda871e7f8f1f97ab5d846dfec.jpeg)

 ![image](https://us1.discourse-cdn.com/flex021/uploads/neo4jcommunity/original/3X/2/2/22083472a36cb4d2d4f9e68925cec378fb3a8ed0.jpeg)

 ![image](https://us1.discourse-cdn.com/flex021/uploads/neo4jcommunity/original/3X/4/f/4f13c90c93e9cbf16abe0c4d498c8f9f49a2e7f0.jpeg)

### 🔗 **Optional: link to your agent if available**

[https://api.neo4j.io/v2beta1/organizations/3bb9b4f1-7f16-43d9-9046-8ab0da25ae5d/projects/3bb9b4f1-7f16-43d9-9046-8ab0da25ae5d/agents/7efe4d1b-ccd5-4d2c-bfb1-dd40d25f9dbd/invoke](https://api.neo4j.io/v2beta1/organizations/3bb9b4f1-7f16-43d9-9046-8ab0da25ae5d/projects/3bb9b4f1-7f16-43d9-9046-8ab0da25ae5d/agents/7efe4d1b-ccd5-4d2c-bfb1-dd40d25f9dbd/invoke)

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_[View the full topic](https://community.neo4j.com/t/start-here-register-get-aura-credits-aura-agent-hackathon/77191)._