🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.

💥Free recorded course by Jure Leskovec, Computer Science, PhD


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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #DGNN #GNN

📄Graph Attention Networks

💥Technical paper

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📲Channel: @ComplexNetworkAnalysis

#paper #Graph #GAT #Machine_Learning #Attention #Neural_Network

🎞 Machine Learning with Graphs: Generative Models for Graphs

💥Free recorded course by Jure Leskovec, Computer Science, PhD

💥In this lecture, we will cover generative models for graphs. The goal of generative models for graphs is to generate synthetic graphs which are similar to given example graphs. Graph generation is important as it can offer insight on the formulation process of graphs, which is crucial for predictions, simulations and anomaly detections on graphs. In the first part, we will introduce the properties of real-world graphs, where a successful graph generative model should fit these properties. These graph statistics include degree distribution, clustering coefficient, connected components and path length.

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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #Generative_Models

🎞 Graph Analytics and Graph-based Machine Learning

💥Free recorded course by Clair Sullivan(Neo4j)

💥Machine learning has traditionally revolved around creating models around data that is characterized by embeddings attributed to individual observations. However, this ignores a signal that could potentially be very strong: the relationships between data points. Network graphs provide great opportunities for identifying relationships that we may not even realize exist within our data. Further, a variety of methods exist to create embeddings of graphs that can enrich models and provide new insights.
In this talk we will look at some examples of common ML problems and demonstrate how they can take advantage of graph analytics and graph-based machine learning. We will also demonstrate how graph embeddings can be used to enhance existing ML pipelines.

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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning

📄Machine Learning for Refining Knowledge Graphs: A Survey

📘 Journal: acm digital library (I.F=14.324)
🗓Publish year: 2020

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📲Channel: @ComplexNetworkAnalysis
#paper #Machine_Learning #Knowledge_Graphs #Survey

📹 Graph Embedding For Machine Learning in Python

💥In this video, you will learn how to embed graphs into n-dimensional space to use them for machine learning.

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📲Channel: @ComplexNetworkAnalysis
#video #Graph_Embedding #Machine_Learning

🎞 Machine Learning with Graphs: Applications of Deep Graph Generation.

💥Free recorded course by Jure Leskovec, Computer Science, PhD

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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #Machine_Learning #DGNN #GNN

🎞 Machine Learning with Graphs - Node Embeddings

💥SDML is partnering with Houston Machine Learning on a series about machine learning with graphs.

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📱Channel: @ComplexNetworkAnalysis

#video #Machine_Learning #Graph #Node_Embedding

📄Graph-Based Data Science, Machine Learning, and AI

💥Technical Paper

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📲Channel: @ComplexNetworkAnalysis

#paper #Graph #AI #Data_Science #Machine_Learning

🎞 Machine Learning with Graphs: Graph Neural Networks in Computational Biology

💥Free recorded course by Prof. Marinka Zitnik

💥In this lecture, Prof. Marinka gives an overview of why graph learning techniques can greatly help with computational biology research. Concretely, this talk covers 3 exemplar use cases: (1) Discovering safe drug-drug combinations via multi-relational link prediction on heterogenous knowledge graphs; (2) Classify patient outcomes and diseases via learning subgraph embeddings; and (3) Learning effective disease treatments through few-shot learning for graphs.

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📲Channel: @ComplexNetworkAnalysis

#video #course #Graph #GNN #Machine_Learning #computational_biology

🎞 Machine Learning with Graphs: Pre-Training Graph Neural Networks

💥Free recorded course by Prof. Jure Leskovec

💥There are two challenges in applying GNNs to scientific domains: scarcity of labeled data and out-of-distribution prediction. In this video we discuss methods for pre-training GNNs to resolve these challenges. The key idea is to pre-train both node and graph embeddings, which leads to significant performance gains on downstream tasks.

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📑More details can be found in the paper: Strategies for Pre-training Graph Neural Networks

📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning

📃 A Survey on Machine Learning Solutions for Graph Pattern Extraction

🗓 Publish year: 2022

🧑‍💻Authors:Kai Siong Yow, Ningyi Liao, Siqiang Luo, Reynold Cheng, Chenhao Ma, Xiaolin Han
🏢University: g, Nanyang Technological University
🗺 China, Singapore

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📲Channel: @ComplexNetworkAnalysis
#paper #Survey #Machine_learning #Pattern

🎓 Machine Learning for Graph Algorithms and Representations

📘A Thesis Submitted to the Faculty in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Engineering Sciences
🗓Publish year: 2024

🧑‍💻Author: Allison Mann
🏢University: College Hanover, New Hampshire

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📱Channel: @ComplexNetworkAnalysis
#Thesis #Machine_Learning #Algorithms #Representations

📃Community detection in social networks using machine learning: a systematic mapping study

🗓 Publish year: 2024
📘Journal: Knowledge and Information Systems (I.F=2.5)

🧑‍💻Authors: Mahsa Nooribakhsh, Marta Fernández-Diego, Fernando González-Ladrón-De-Guevara. Mahdi Mollamotalebi
🏢University: Universitat Politècnica de València, Camino de Vera, s/n, 46022, Valencia, Spain and Islamic Azad University, Qazvin, Iran

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📱Channel: @ComplexNetworkAnalysis
#paper #Community_detection #Machine_learning #mapping

🎓Graph Data Science and machine learning applications

📕Master Degree Thesis by Antonella Cardillo form POLITECNICO DI TORINO

🗓Publish year: 2024

📎 Study thesis

📲Channel: @ComplexNetworkAnalysis
#thesis #Graph #machine_learning #Data_Science

🎞 Machine Learning with Graphs: hyperbolic graph embeddings

💥Free recorded course by Prof. Jure Leskovec

💥 This part focused on graph representation learning in Euclidean embedding spaces. In this lecture, we introduce hyperbolic embedding spaces, which are great for modeling hierarchical, tree-like graphs. Moreover, we introduce basics for hyperbolic geometry models, which leads to the idea of hyperbolic GNNs. More details can be found in the paper: Hyperbolic Graph Convolutional Neural Networks

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📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning

🎞 Machine Learning with Graphs: design space of graph neural networks

💥Free recorded course by Prof. Jure Leskovec

💥 This part discussed the important topic of GNN architecture design. Here, we introduce 3 key aspects in GNN design: (1) a general GNN design space, which includes intra-layer design, inter-layer design and learning configurations; (2) a GNN task space with similarity metrics so that we can characterize different GNN tasks and, therefore, transfer the best GNN models across tasks; (3) an effective GNN evaluation technique so that we can convincingly evaluate any GNN design question, such as “Is BatchNorm generally useful for GNNs?”. Overall, we provide the first systematic investigation of general guidelines for GNN design, understandings of GNN tasks, and how to transfer the best GNN designs across tasks. We release GraphGym as an easy-to-use code platform for GNN architectural design. More information can be found in the paper: Design Space for Graph Neural Networks

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📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning

📄 Graph Data Management and Graph Machine Learning: Synergies and Opportunities

🗓 Publish year: 2025

🧑‍💻Authors: Arijit Kha, Xiangyu Ke, Yinghui Wu
🏢University:
- Aalborg University, Denmark
- Zhejiang University, China
- Case Western Reserve University, USA

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⚡️Channel: @ComplexNetworkAnalysis
#review #graph #machine_learning #data_management

🎞 Machine Learning with Graphs: GraphSAGE Neighbor Sampling

💥Free recorded course by Prof. Jure Leskovec

💥 This part discussed Neighbor Sampling, That is a representative method used to scale up GNNs to large graphs. The key insight is that a K-layer GNN generates a node embedding by using only the nodes from the K-hop neighborhood around that node. Therefore, to generate embeddings of nodes in the mini-batch, only the K-hop neighborhood nodes and their features are needed to load onto a GPU, a tractable operation even if the original graph is large. To further reduce the computational cost, only a subset of neighboring nodes is sampled for GNNs to aggregate.


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📲Channel: @ComplexNetworkAnalysis
#video #course #Graph #GNN #Machine_Learning #GraphSAGE