그래프 신경망: 장점, 유형 및 응용 분야

Simply Learn으로 그래프 신경망을 살펴보세요. 그래프의 정의, GNN 유형, 장점 및 응용 분야를 이해하세요. 지금 등록하여 AI 및 ML 커리어 가속화에 참여하세요.

### 그래프 신경망: 이해, 유형, 장점, 단점 및 애플리케이션
* 📚  이 동영상에서는 그래프의 기본 개념, 그래프 분석의 어려움, 그래프 신경망(GNN)의 소개, 다양한 유형의 GNN과 그 장단점을 다룹니다.
* 🔍  GNN은 그래프 구조로 표현된 데이터를 분석하는 전문 딥러닝 기법으로, 에지, 노드, 그래프 수준에서 예측하고 복잡하고 불규칙한 그래프 구조의 데이터를 처리할 수 있습니다.
* 📈  GNN은 반복적 GNN, 공간 컨볼루션 네트워크, 스펙트럼 컨볼루션 신경망으로 분류되며, 각각 그래프 데이터를 처리하는 고유한 접근 방식이 있습니다.
* ✅  GNN의 장점으로는 복잡한 데이터 처리, 로컬 및 글로벌 정보 캡처, 매개변수 공유, 엔드투엔드 학습, 전송 가능성 등이 있지만 확장성, 제한된 일반화, 해석 가능성, 과도한 평활화, 다양한 그래프 크기 처리 등의 단점도 있습니다.
* 🌐  GNN은 소셜 네트워크 분석, 추천 시스템, 교통량 예측, 컴퓨터 비전, 금융 사기 탐지 등 다양한 분야에 적용되어 그래프 구조의 데이터와 관련된 실제 문제를 해결할 수 있는 다용도성을 입증하고 있습니다.

hello everyone. I am Mayank and welcome to this video on graph neural networks by Simply Learn. In this video, we will cover topics like what a graph is. Moving forward, we'll see why it is hard to analyze a graph. After that, we will cover what graph neural networks are. After that, we will look into types of GNNs. GNNs means graph neural networks. After covering all these topics, we will cover some advantages and disadvantages of GNNs. And at the end, we will see some applications of the GNNs. So let me tell you guys that we have regular updates on multiple technologies. If you are a tech geek on a continuous hunt for the latest technological trends, then consider getting subscribed to our YouTube channel and press that Bell icon to never miss any update from Simply Learn. So let's get started. Also, accelerate your career in AI and ML with Copernicus Processor Program in AI and Machine Learning. Gain expertise in machine learning, deep learning, NLP, computer vision, and reinforcement learning. You will receive a prestigious certificate, exclusive alumni membership, and ask me anything sessions by IBM.

three Capstone and 25 plus industry projects using real data sets from Twitter, Uber, and more. You will gain practical experience. Masterclasses by Caltech faculty and IBM experts ensure you the top-notch education. Simply against job assist help you get noticed by leading companies. This program covers statistics, Python, supervised and unsupervised learning, NLP, neural networks, computer vision, genes, Keras, TensorFlow, and many more skills. So enroll now and unlock exciting AI and ML opportunities. Coastlink is mentioned in the description box below. So let's see what is a graph. So at the core of graph neural network (GNNs) is a fundamental concept in computer science. A graph is a data structure comprising two essential elements. First is node or vertices and the edges. So in cases where there are directional relationships between nodes, those edges are considered as a director, okay. It is known as the directed graph. On the other hand, if there are no directional dependencies, the edges are undirected graph, okay. So a graph G is defined as G equals to VE where V represents set of nodes and

the E represents the edges connecting to these nodes. So moving forward, let's see why it is hard to analyze a graph. Graph data present many challenges for the conventional machine learning algorithm due to its inherent complexity. Unlike simple data types these algorithms specialized in, such as images with fixed structure and the sizes resembling grid graphs, or sequences like text and speech representing line graphs, graphs can be far more intricate. Graphs do not conform to a fixed form and vary in size and unordered nodes, where each node can have various neighbors. This dynamic and irregular nature poses a significant obstacle to traditional machine learning tools. For the more exciting machine learning algorithms are built on this core assumption that instances are dependent on each other. So let's move forward and see what are graph neural networks. GNNs, or graph neural networks, are a category of deep learning techniques specifically crafted for analyzing data represented in graph structures. They serve as neural networks tailored to process graphs directly, offering a convenient approach and performing prediction at the node edge.

and the graph levels with GNS. It becomes feasible to conduct challenging tasks for conventional neural network CNN, allowing for more effective and versatile analysis for graph-structured data. So moving forward, let's see types of DNS graph neural network. GNNs are neural network designed explicitly to process graph-structured data, offering a versatile approach for edge, node, and graph-level prediction. So they can be categorized into three main types. First one is recurrent graph neural network. This type of GNNs incorporates recurrent cells into its architecture, enabling the model to capture temporal dependency and sequential patterns in the graph data. The second one is spatial convolutional network. So spatial CNNs are inspired by traditional CNN and operate on a node's local neighborhood. They aggregate information from neighboring nodes to update a node's representation. And the third one is spectral convolutional neural network. So spectral conventional DNNs operate in the spectral domain by using eigenvalues and the eigenvectors of the graph adjacency matrix. This allows them to handle graph signals efficiently and capture global structure. So at the core of GNN lies the fundamental

institution that nodes in a graph are defined by their connection and Neighbors. When visualizing this concept, it becomes apparent that removing all of the neighbor's word lead to the node losing all its information. So after covering types of gnns, let's see advantages and the disadvantages of genus. First, we will see some advantages. So first one is handling complex data. So gnn's excel at processing complex and irregular graph structure data, which cannot be easily represented using traditional data structure like grids or sequences. The second one is capturing local and Global Information. Gnns can capture both local and Global Information of nodes in a graph. The message passing mechanism enables nodes to learn from their immediate neighbor while considering higher level structural pattern in the graph. And the third one is parameter sharing. Gnn's share parameter across node in the same layer, reducing the model memory footprint and making them more scalable than fully connected architecture. And the fourth one is end-to-end learning. Gnns can be trained to end-to-end learning hierarchical representation and feature embedding directly from the graph today and data without relying on

the handcart feature, and the last one is transferability. So, GNNs demonstrate transfer learning capabilities. Models pre-trained on a graph can often be fine-tuned or adapted to focus while on a related graph, even with a different size of a structure. Some disadvantages of GNNs are: first one is scalability, so training GNNs on a large labor can be computationally expensive and memory-intensive due to the need to propagate messages between nodes. The second one is limited generalization. DNNs may struggle to generalize well on graphs that significantly differ from the training graph in terms of structure or destruction. This limitation can be problematic when dealing with dynamic or evolving graphs. The third one is interpretability. GNN's Black Box nature can make them challenging to interpret and understand, especially when making critical decisions in domains like healthcare or finance. The fourth one is over-smoothing. DNNs may suffer from over-smoothing where nodes' representations become overly similar after multiple layers of message possible. This can lead to a loss of valuable information and reduce model

performance, and the last one is handling different graph sizes. Genus typically expect graph with a fixed size during training, making it challenging to apply them to graphs with varying node or Edge counts. Graph pooling techniques are often employed to address this issue, but it can still be a concern. So, in conclusion, graph neural networks offer powerful capabilities for handling complex graph-structured data and have shown success in various applications. However, there is scalability, generalization, and adaptability to different graph sizes remain active areas of research and improvement. Understanding the advantages and the disadvantages of GNN is crucial in selecting the appropriate model for a specific task and mitigating potential challenges. Moving forward, let's see application of GNNs. The first one is social network analysis. Genus can be used to model and analyze social networking, making prediction on user interaction, influence propagation, and Community detection. They help in identifying influential users, recommendation connection, and understanding the network Dynamics. The second one is recommender system. GNNs effectively build recommendation engine that provide personal suggestion based on user items interact.

they can learn user preferences and item characteristics to make accurate and relevant recommendation. The third one is traffic prediction. GNNs can be used to analyze Transportation networks, such as Road Network and public transportation system, for predicting traffic flow and congestion patterns, and optimizing routes. The fourth one is computer Visions. GNNs are used in computer vision tasks involving structured data, such as image parsing, sync graph generation, and object relationship understanding. The fifth one is financial fraud detection. GNNs can be employed to analyze financial transaction graphs to identify fraud and activities, detect anomalies, and enhance security measures. So, these applications demonstrate the versatility and the utility of GNNs in addressing complex real-world problems that involve graph restructure data. As research in the field of progressive DNS are expected to find even more application across various domain. So that's all we had for you in this video. We can call it our Apna. If you still have any question or doubt, let us know in the comment section below, and our team of experts will help you. Thank you so much.

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Introduction To Graph Neural Network | What Are Graph Neural Networks ? | GNN | Simplilearn

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Simply Learn의 그래프 신경망 소개

그래프 신경망(GNN)은 이 동영상의 주요 초점으로, 그래프의 정의와 분석이 어려운 이유에 대해 다룹니다.

이 비디오는 또한 GNN의 유형, 장단점 및 응용 분야를 살펴봅니다.

Simply Learn은 다양한 기술에 대한 전문 지식을 제공하는 AI 및 머신러닝 분야의 코페르니쿠스 프로세서 프로그램을 홍보하고 있습니다.

그래프 데이터는 복잡하고 불규칙한 특성으로 인해 기존 머신 러닝 알고리즘에 고유한 문제를 야기합니다.

GNN은 그래프 구조의 데이터를 분석하고 예측하도록 특별히 설계되어 에지, 노드, 그래프 수준의 예측을 위한 다양한 접근 방식을 제공합니다.

이러한 신경망은 순환 신경망, 공간 컨볼루션 네트워크, 스펙트럼 컨볼루션 신경망으로 분류할 수 있으며, 각각 그래프 데이터를 처리하고 분석하는 고유한 방식이 있습니다.

그래프 신경망(GNN)은 복잡한 데이터를 처리하고 그래프에서 로컬 정보뿐만 아니라 글로벌 정보를 캡처하는 데 탁월합니다.

또한 매개변수 공유와 전이 학습 능력도 뛰어납니다.

그러나 GNN은 확장성, 제한된 일반화, 해석 가능성, 정보 손실로 이어질 수 있는 과도한 평활화 위험 등의 문제에 직면해 있습니다.

GNN은 복잡한 그래프 구조의 데이터를 처리하는 데 강력하며 다양한 애플리케이션에서 성공을 거두었습니다.

그러나 확장성, 일반화, 다양한 그래프 크기에 대한 적응성 등에 대한 연구가 활발히 진행되고 있습니다.

GNN은 사용자 상호 작용 예측을 위한 소셜 네트워크 분석, 개인화된 추천을 위한 추천 시스템, 경로 최적화를 위한 트래픽 예측, 컴퓨터 비전 작업, 금융 사기 탐지 등에 적용됩니다.

이러한 응용 사례는 실제 문제를 해결하는 데 있어 GNN의 다재다능함을 보여주며, 앞으로 다양한 영역에서 더 많은 응용 사례를 찾을 수 있을 것으로 기대됩니다.

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