Episode 1 – Exploring Real-World Use CasesLarge language models (LLMs) like ChatGPT have taken the world by storm in 2023 due to their ability to understand and generate human-like text. Their capacity to adapt to different conversational contexts, answer questions… Read more →
Learn how to implement a context-aware chatbot in GPT-4 that bases its answers on the information retrieved from a graph database. Read more →
Learn how to use ChatGPT 4 as a domain expert to help you extract knowledge and turn it into a graph from a video transcript. Read more →
Natural Language Processing (NLP) has seen rapid advancements in recent years. One important aspect of this progress has been the use of embeddings, which are numerical representations of words or phrases that capture their meaning and relationships to other words… Read more →
Graph Neural Networks (GNNs) are gaining tons of recognition in the machine learning community due to their potential for solving complex tasks in social networks, drug discovery, recommendation systems, and more. Read more →
ChatGPT has changed how I, and probably most of you, look at AI and chatbots. We can use chatbots to help us find information, construct creative works, and more.However, one problem with ChatGPT and similar chatbots is that they can hallucinate… Read more →
NODES 2022 is a free, multi-track, two-day virtual conference of highly technical presentations by developers and data scientists solving problems with graphs. Read more →
Knowing your users is vital to any business. When your users can interact with each other on a social media platform, content sharing platform, or even work-related platforms, you can construct a network between your users based on their interactions and extract graph-based features to segment your users. Of course, these same approaches can be applied to other platforms that are not user-centric. Read more →
A wave of graph-based approaches to data science and machine learning is rising. We live in an era where the exponential growth of graph technology is predicted [1]. The ability to analyze data points through the context of their relationships… Read more →
In part 4 of our fraud detection series, we will cover how to apply graph machine learning to predict the high fraud risk user accounts we labeled in parts 1, 2, and 3. Read more →
In part 3 of our fraud detection series, we may want to expand beyond our business logic to automatically identify other users that are suspiciously similar to the fraud risks already identified. Read more →
In part 2 of this fraud detection series, we will provide more formal definitions for resolving entities that will allow us to partition well-defined communities in a scalable manner. Read more →
In the first part of this fraud detection series, we will introduce the sample graph dataset we are using and begin exploring the graph for potential fraud patterns. Read more →
Fraud Detection is one of today's most challenging data science problems. Thankfully, Neo4j Graph Data Science (GDS) offers practical solutions that empower data scientists to make rapid progress in fraud detection analytics and machine learning. Read more →