Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.
This document provides an overview of graph representation learning and various methods for learning embeddings of nodes in graph-structured data. It introduces shallow methods like DeepWalk and Node2Vec that learn embeddings by generating random walks. It then discusses deep methods like graph convolutional networks (GCN) and GraphSAGE that learn embeddings through neural network aggregation of node neighborhoods. Graph attention networks are also introduced as a learnable aggregator for GCN. Finally, applications of these methods at Pinterest for pin recommendation and at Uber Eats for dish recommendation are briefly described.
This document provides an overview of graph neural networks (GNNs). GNNs are a type of neural network that can operate on graph-structured data like molecules or social networks. GNNs learn representations of nodes by propagating information between connected nodes over many layers. They are useful when relationships between objects are important. Examples of applications include predicting drug properties from molecular graphs and program understanding by modeling code as graphs. The document explains how GNNs differ from RNNs and provides examples of GNN variations, datasets, and frameworks.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
This document provides an overview of machine learning with graphs. It discusses graph neural networks and deep learning in graphs. It covers representing graphs using adjacency matrices and lists. It also discusses node and graph level features, as well as node embeddings using random walks. Finally, it summarizes several graph neural network models like GCN and GraphSAGE and their applications to citation networks, social networks, and knowledge graphs.
The document compares DeepWalk and Node2Vec network embedding algorithms. DeepWalk learns representations by treating random walks as sentences, but cannot capture mixtures of homophily and structural equivalence. Node2Vec addresses this by introducing parameters p and q to control the walk's behavior between BFS and DFS, allowing it to explore neighborhoods more flexibly. The algorithm samples multiple random walks per node and learns embeddings by predicting contexts within those walks using Skip-Gram.
Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. However, traditionally machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph. In this talk I will discuss methods that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. I will provide a conceptual review of key advancements in this area of representation learning on graphs, including random-walk based algorithms, and graph convolutional networks.
This document provides an overview of graph representation learning and various methods for learning embeddings of nodes in graph-structured data. It introduces shallow methods like DeepWalk and Node2Vec that learn embeddings by generating random walks. It then discusses deep methods like graph convolutional networks (GCN) and GraphSAGE that learn embeddings through neural network aggregation of node neighborhoods. Graph attention networks are also introduced as a learnable aggregator for GCN. Finally, applications of these methods at Pinterest for pin recommendation and at Uber Eats for dish recommendation are briefly described.
This document provides an overview of graph neural networks (GNNs). GNNs are a type of neural network that can operate on graph-structured data like molecules or social networks. GNNs learn representations of nodes by propagating information between connected nodes over many layers. They are useful when relationships between objects are important. Examples of applications include predicting drug properties from molecular graphs and program understanding by modeling code as graphs. The document explains how GNNs differ from RNNs and provides examples of GNN variations, datasets, and frameworks.
Deep Learning for Recommendations: Fundamentals and Advances
In this part, we focus on Graph Neural Networks for Recommendations.
Tutorial Website/slides: https://advanced-recommender-systems.github.io/ijcai2021-tutorial/
https://youtu.be/4aXk3LNTJRc
Introduction to Graph neural networks @ Vienna Deep Learning meetupLiad Magen
Graphs are useful data structures that can be used to model various sorts of data: from molecular protein structures to social networks, pandemic spreading models, and visually rich content such as websites & invoices. In the recent few years, graph neural networks have done a huge leap forward. It is a powerful tool that every data scientist should know. In this talk, we will review their basic structure, show some example usages, and explore the existing (python) tools.
Slides for a talk about Graph Neural Networks architectures, overview taken from very good paper by Zonghan Wu et al. (https://arxiv.org/pdf/1901.00596.pdf)
This document provides an overview of machine learning with graphs. It discusses graph neural networks and deep learning in graphs. It covers representing graphs using adjacency matrices and lists. It also discusses node and graph level features, as well as node embeddings using random walks. Finally, it summarizes several graph neural network models like GCN and GraphSAGE and their applications to citation networks, social networks, and knowledge graphs.
The document compares DeepWalk and Node2Vec network embedding algorithms. DeepWalk learns representations by treating random walks as sentences, but cannot capture mixtures of homophily and structural equivalence. Node2Vec addresses this by introducing parameters p and q to control the walk's behavior between BFS and DFS, allowing it to explore neighborhoods more flexibly. The algorithm samples multiple random walks per node and learns embeddings by predicting contexts within those walks using Skip-Gram.
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Hands on Explainable Recommender Systems with Knowledge Graphs @ RecSys22GiacomoBalloccu
This document provides an overview of an upcoming tutorial on explainable recommender systems with knowledge graphs. The tutorial will include two sessions - an introductory session on explainable recommendation principles and modeling approaches, and a hands-on session using Jupyter notebooks to build and evaluate recommendation models using knowledge graphs. Attendees will learn about explainable recommendation methods, loading and preprocessing interaction datasets with knowledge graphs, building recommendation models with knowledge graphs, and evaluating and generating explanations from models. The tutorial aims to help attendees understand explainable recommender systems and apply techniques using knowledge graphs.
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
This thesis presents research on using deep learning methods for feature extraction from satellite imagery to identify landslide pixels. The objectives are to classify land cover using machine learning algorithms like SVM and random forests in Google Earth Engine, design and evaluate a deep neural network for landslide identification, and compare performance of deep learning models in MATLAB. Results show that a neural network achieved over 98% accuracy at identifying landslide pixels. Future work proposes developing new indices for improved identification and an automatic landslide monitoring platform.
Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
The ArangoML Group had a detailed discussion on the topic "GraphSage Vs PinSage" where they shared their thoughts on the difference between the working principles of two popular Graph ML algorithms. The following slidedeck is an accumulation of their thoughts about the comparison between the two algorithms.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
This document introduces graph neural networks and discusses a claim that they are essentially low-pass filters. It provides an overview of graph neural network operations, including combining node features, aggregating information from neighbors, and updating node representations over multiple layers. The document notes that while graph neural networks may be less powerful than other deep learning methods, they are interesting for problems involving graphs, such as drug discovery and web analytics. It questions how graph neural network classifications operate and whether the low-pass filter behavior is caused by the graph Laplacian matrix.
This document summarizes a presentation on graph kernels in chemoinformatics. It discusses using graph kernels to measure similarity between molecular graphs to analyze large families of structural and numerical objects. Specific graph kernels discussed include the treelets kernel, which extracts small labeled subtrees from graphs, and kernels based on cyclic similarity, which analyze relevant cycles in molecules. The treelets kernel is shown to outperform other graph kernels and molecular descriptors in predicting boiling points of molecules.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Deep learning techniques are increasingly being used for recommender systems. Neural network models such as word2vec, doc2vec and prod2vec learn embedding representations of items from user interaction data that capture their relationships. These embeddings can then be used to make recommendations by finding similar items. Deep collaborative filtering models apply neural networks to matrix factorization techniques to learn joint representations of users and items from rating data.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
network mining and representation learningsun peiyuan
This document discusses two papers related to network embedding and ranking over multilayer networks.
The first paper proposes metapath2vec, a network embedding technique for heterogeneous networks. It extends word2vec to learn latent representations of nodes in a heterogeneous network by considering metapath-guided random walks.
The second paper proposes CrossRank and CrossQuery algorithms for ranking and querying over a network of networks (NoN). CrossRank learns global ranking vectors for each domain network in the NoN by optimizing for within-network smoothness, query preference, and cross-network consistency. CrossQuery efficiently finds the top-k most relevant nodes in a target network for a query node in a source network. Both methods are evaluated on
Introduction to Graph Neural Networks: Basics and Applications - Katsuhiko Is...Preferred Networks
This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.
Many powerful Machine Learning algorithms are based on graphs, e.g., Page Rank (Pregel), Recommendation Engines (collaborative filtering), text summarization, and other NLP tasks. Also, the recent developments with Graph Neural Networks connect the worlds of Graphs and Machine Learning even further.
Considering data pre-processing and feature engineering which are both vital tasks in Machine Learning Pipelines extends this relationship across the entire ecosystem. In this session, we will investigate the entire range of Graphs and Machine Learning with many practical exercises.
Scikit-Learn is a powerful machine learning library implemented in Python with numeric and scientific computing powerhouses Numpy, Scipy, and matplotlib for extremely fast analysis of small to medium sized data sets. It is open source, commercially usable and contains many modern machine learning algorithms for classification, regression, clustering, feature extraction, and optimization. For this reason Scikit-Learn is often the first tool in a Data Scientists toolkit for machine learning of incoming data sets.
The purpose of this one day course is to serve as an introduction to Machine Learning with Scikit-Learn. We will explore several clustering, classification, and regression algorithms for a variety of machine learning tasks and learn how to implement these tasks with our data using Scikit-Learn and Python. In particular, we will structure our machine learning models as though we were producing a data product, an actionable model that can be used in larger programs or algorithms; rather than as simply a research or investigation methodology.
Hands on Explainable Recommender Systems with Knowledge Graphs @ RecSys22GiacomoBalloccu
This document provides an overview of an upcoming tutorial on explainable recommender systems with knowledge graphs. The tutorial will include two sessions - an introductory session on explainable recommendation principles and modeling approaches, and a hands-on session using Jupyter notebooks to build and evaluate recommendation models using knowledge graphs. Attendees will learn about explainable recommendation methods, loading and preprocessing interaction datasets with knowledge graphs, building recommendation models with knowledge graphs, and evaluating and generating explanations from models. The tutorial aims to help attendees understand explainable recommender systems and apply techniques using knowledge graphs.
This document provides an overview of using deep learning techniques for recommender systems. It begins with establishing the need for recommender systems due to increasing information overload. It then gives a basic introduction and agenda for the talk, covering motivation, basics, deep learning for vehicle recommendations, and scalability/production. The talk discusses using deep learning approaches like wide and deep learning as well as sequential models to improve recommendation relevance for applications like vehicle recommendations. It provides details on preprocessing, training a classifier, candidate generation and ranking for recommendations. The document concludes with discussing deploying such a system at scale and current trends in recommender system research.
This thesis presents research on using deep learning methods for feature extraction from satellite imagery to identify landslide pixels. The objectives are to classify land cover using machine learning algorithms like SVM and random forests in Google Earth Engine, design and evaluate a deep neural network for landslide identification, and compare performance of deep learning models in MATLAB. Results show that a neural network achieved over 98% accuracy at identifying landslide pixels. Future work proposes developing new indices for improved identification and an automatic landslide monitoring platform.
Semantic Segmentation on Satellite ImageryRAHUL BHOJWANI
This is an Image Semantic Segmentation project targeted on Satellite Imagery. The goal was to detect the pixel-wise segmentation map for various objects in Satellite Imagery including buildings, water bodies, roads etc. The data for this was taken from the Kaggle competition <https://www.kaggle.com/c/dstl-satellite-imagery-feature-detection>.
We implemented FCN, U-Net and Segnet Deep learning architectures for this task.
The ArangoML Group had a detailed discussion on the topic "GraphSage Vs PinSage" where they shared their thoughts on the difference between the working principles of two popular Graph ML algorithms. The following slidedeck is an accumulation of their thoughts about the comparison between the two algorithms.
Robust Feature Learning with Deep Neural Networks
http://snu-primo.hosted.exlibrisgroup.com/primo_library/libweb/action/display.do?tabs=viewOnlineTab&doc=82SNU_INST21557911060002591
This document introduces graph neural networks and discusses a claim that they are essentially low-pass filters. It provides an overview of graph neural network operations, including combining node features, aggregating information from neighbors, and updating node representations over multiple layers. The document notes that while graph neural networks may be less powerful than other deep learning methods, they are interesting for problems involving graphs, such as drug discovery and web analytics. It questions how graph neural network classifications operate and whether the low-pass filter behavior is caused by the graph Laplacian matrix.
This document summarizes a presentation on graph kernels in chemoinformatics. It discusses using graph kernels to measure similarity between molecular graphs to analyze large families of structural and numerical objects. Specific graph kernels discussed include the treelets kernel, which extracts small labeled subtrees from graphs, and kernels based on cyclic similarity, which analyze relevant cycles in molecules. The treelets kernel is shown to outperform other graph kernels and molecular descriptors in predicting boiling points of molecules.
Talk with Yves Raimond at the GPU Tech Conference on Marth 28, 2018 in San Jose, CA.
Abstract:
In this talk, we will survey how Deep Learning methods can be applied to personalization and recommendations. We will cover why standard Deep Learning approaches don't perform better than typical collaborative filtering techniques. Then we will survey we will go over recently published research at the intersection of Deep Learning and recommender systems, looking at how they integrate new types of data, explore new models, or change the recommendation problem statement. We will also highlight some of the ways that neural networks are used at Netflix and how we can use GPUs to train recommender systems. Finally, we will highlight promising new directions in this space.
발표자: 최윤제(고려대 석사과정)
최윤제 (Yunjey Choi)는 고려대학교에서 컴퓨터공학을 전공하였으며, 현재는 석사과정으로 Machine Learning을 공부하고 있는 학생이다. 코딩을 좋아하며 이해한 것을 다른 사람들에게 공유하는 것을 좋아한다. 1년 간 TensorFlow를 사용하여 Deep Learning을 공부하였고 현재는 PyTorch를 사용하여 Generative Adversarial Network를 공부하고 있다. TensorFlow로 여러 논문들을 구현, PyTorch Tutorial을 만들어 Github에 공개한 이력을 갖고 있다.
개요:
Generative Adversarial Network(GAN)은 2014년 Ian Goodfellow에 의해 처음으로 제안되었으며, 적대적 학습을 통해 실제 데이터의 분포를 추정하는 생성 모델입니다. 최근 들어 GAN은 가장 인기있는 연구 분야로 떠오르고 있고 하루에도 수 많은 관련 논문들이 쏟아져 나오고 있습니다.
수 없이 쏟아져 나오고 있는 GAN 논문들을 다 읽기가 힘드신가요? 괜찮습니다. 기본적인 GAN만 완벽하게 이해한다면 새로 나오는 논문들도 쉽게 이해할 수 있습니다.
이번 발표를 통해 제가 GAN에 대해 알고 있는 모든 것들을 전달해드리고자 합니다. GAN을 아예 모르시는 분들, GAN에 대한 이론적인 내용이 궁금하셨던 분들, GAN을 어떻게 활용할 수 있을지 궁금하셨던 분들이 발표를 들으면 좋을 것 같습니다.
발표영상: https://youtu.be/odpjk7_tGY0
Deep learning techniques are increasingly being used for recommender systems. Neural network models such as word2vec, doc2vec and prod2vec learn embedding representations of items from user interaction data that capture their relationships. These embeddings can then be used to make recommendations by finding similar items. Deep collaborative filtering models apply neural networks to matrix factorization techniques to learn joint representations of users and items from rating data.
Supervised learning and Unsupervised learning Usama Fayyaz
This document discusses supervised and unsupervised machine learning. Supervised learning uses labeled training data to learn a function that maps inputs to outputs. Unsupervised learning is used when only input data is available, with the goal of modeling underlying structures or distributions in the data. Common supervised algorithms include decision trees and logistic regression, while common unsupervised algorithms include k-means clustering and dimensionality reduction.
network mining and representation learningsun peiyuan
This document discusses two papers related to network embedding and ranking over multilayer networks.
The first paper proposes metapath2vec, a network embedding technique for heterogeneous networks. It extends word2vec to learn latent representations of nodes in a heterogeneous network by considering metapath-guided random walks.
The second paper proposes CrossRank and CrossQuery algorithms for ranking and querying over a network of networks (NoN). CrossRank learns global ranking vectors for each domain network in the NoN by optimizing for within-network smoothness, query preference, and cross-network consistency. CrossQuery efficiently finds the top-k most relevant nodes in a target network for a query node in a source network. Both methods are evaluated on
Sub-Graph Finding Information over Nebula Networksijceronline
Social and information networks have been extensively studied over years. This paper studies a new query on sub graph search on heterogeneous networks. Given an uncertain network of N objects, where each object is associated with a network to an underlying critical problem of discovering, top-k sub graphs of entities with rare and surprising associations returns k objects such that the expected matching sub graph queries efficiently involves, Compute all matching sub graphs which satisfy "Nebula computing requests" and this query is useful in ranking such results based on the rarity and the interestingness of the associations among nebula requests in the sub graphs. "In evaluating Top k-selection queries, "we compute information nebula using a global structural context similarity, and our similarity measure is independent of connection sub graphs". We need to compute the previous work on the matching problem can be harnessed for expected best for a naive ranking after matching for large graphs. Top k-selection sets and search for the optimal selection set with the large graphs; sub graphs may have enormous number of matches. In this paper, we identify several important properties of top-k selection queries, We propose novel top–K mechanisms to exploit these indexes for answering interesting sub graph queries efficiently.
Image Segmentation Using Deep Learning : A surveyNUPUR YADAV
1. The document discusses various deep learning models for image segmentation, including fully convolutional networks, encoder-decoder models, multi-scale pyramid networks, and dilated convolutional models.
2. It provides details on popular architectures like U-Net, SegNet, and models from the DeepLab family.
3. The document also reviews datasets commonly used to evaluate image segmentation methods and reports accuracies of different models on the Cityscapes dataset.
For non-grid 3D images like point clouds and meshes, and inherently graph-based data.
Inherently graph-based data include for example brain connectivity analysis, scientific article citation networks, (social) network analysis, etc.
Alternative download link:
https://www.dropbox.com/s/2o3cofcd6d6e2qt/geometricGraph_deepLearning.pdf?dl=0
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
This document discusses big data mining and the Internet of Things. It first presents challenges with big data mining including modeling big data characteristics, identifying key challenges, and issues with statistical analysis of IoT data. It then describes an architecture called IOT-StatisticDB that provides a generalized schema for storing sensor data from IoT devices and a distributed system for parallel computing and statistical analysis of IoT big data. The system includes query operators for data retrieval and statistical analysis of IoT data in areas like transportation networks.
- Tsuyoshi Murata from the Tokyo Institute of Technology discusses using deep learning approaches for complex networks and graph neural networks.
- He summarizes recent work on network embedding, including a paper on learning community structure with variational autoencoders and another on embedding multiplex networks.
- Murata then discusses applications of graph neural networks, challenges in training deep GCNs, the representational power and limitations of GNNs, and open problems in the field like handling shallow structures, dynamic graphs, and scalability issues.
An Integrated Inductive-Deductive Framework for Data Mapping in Wireless Sens...M H
Wireless sensor networks (WSNs) havean intrinsic interdependency with the environments inwhich they operate. The part of the world with whichan application is concerned is defined as that applica-tion’sdomain.Thispaperadvocatesthatanapplicationdomain of a WSN can serve as a supplement to analysis,interpretation,andvisualisationmethodsandtools.Webelieve it is critical to elevate the capabilities of thedata mapping services proposed in [1] to make use of the special characteristics of an application domain. Inthis paper, we propose an adaptive Multi-DimensionalApplication Domain-driven (M-DAD) mapping frame-work that is suitable for mapping an arbitrary num-ber of sense modalities and is capable of utilising therelations between different modalities as well as otherparameters of the application domain to improve themapping performance. M-DAD starts with an initialuser defined model that is maintained and updatedthroughout the network lifetime. The experimentalresults demonstrate that M-DAD mapping frameworkperforms as well or better than mapping services with-out its extended capabilities.
Graph Signal Processing for Machine Learning A Review and New Perspectives - ...lauratoni4
This document provides an overview of graph signal processing (GSP) for machine learning applications. It discusses how GSP can be used to exploit data structure for problems like node/graph classification, community detection, and time series prediction on graphs. GSP tools like graph filtering, sampling, and kernels can be applied to tasks in domains such as healthcare, neuroscience, recommender systems, and for analyzing the COVID-19 pandemic. Open challenges include developing GSP methods for online learning, inference of problem structure from data, and improving model interpretability.
SCALABLE LOCAL COMMUNITY DETECTION WITH MAPREDUCE FOR LARGE NETWORKSIJDKP
This document summarizes a research paper that proposes a MapReduce algorithm called 3MA for scalable local community detection in large networks. 3MA parallelizes an existing iterative expansion algorithm that uses the M metric to evaluate communities. It distributes the computation of node degrees and community M measures across multiple systems. Experimental results showed 3MA can detect communities in networks with millions of nodes faster than sequential algorithms.
Scalable Local Community Detection with Mapreduce for Large NetworksIJDKP
Community detection from complex information networks draws much attention from both academia and
industry since it has many real-world applications. However, scalability of community detection algorithms
over very large networks has been a major challenge. Real-world graph structures are often complicated
accompanied with extremely large sizes. In this paper, we propose a MapReduce version called 3MA that
parallelizes a local community identification method which uses the $M$ metric. Then we adopt an
iterative expansion approach to find all the communities in the graph. Empirical results show that for large
networks in the order of millions of nodes, the parallel version of the algorithm outperforms the traditional
sequential approach to detect communities using the M-measure. The result shows that for local community
detection, when the data is too big for the original M metric-based sequential iterative expension approach
to handle, our MapReduce version 3MA can finish in a reasonable time.
MULTI-LEVEL FEATURE FUSION BASED TRANSFER LEARNING FOR PERSON RE-IDENTIFICATIONijaia
Most of the currently known methods treat person re-identification task as classification problem and used commonly neural networks. However, these methods used only high-level convolutional feature or to express the feature representation of pedestrians. Moreover, the current data sets for person reidentification is relatively small. Under the limitation of the number of training set, deep convolutional networks are difficult to train adequately. Therefore, it is very worthwhile to introduce auxiliary data sets to help training. In order to solve this problem, this paper propose a novel method of deep transfer learning, and combines the comparison model with the classification model and multi-level fusion of the convolution features on the basis of transfer learning. In a multi-layers convolutional network, the characteristics of each layer of network are the dimensionality reduction of the previous layer of results, but the information of multi-level features is not only inclusive, but also has certain complementarity. We can using the information gap of different layers of convolutional neural networks to extract a better feature expression. Finally, the algorithm proposed in this paper is fully tested on four data sets (VIPeR, CUHK01, GRID and PRID450S). The obtained re-identification results prove the effectiveness of the algorithm.
Lambda Data Grid: An Agile Optical Platform for Grid Computing and Data-inten...Tal Lavian Ph.D.
Lambda Data Grid
An Agile Optical Platform for Grid Computing
and Data-intensive Applications
Integrated SW System Provide the “Glue”
Dynamic optical network as a fundamental Grid service in data-intensive Grid application, to be scheduled, to be managed and coordinated to support collaborative operations
International Journal of Computational Engineering Research(IJCER)ijceronline
This document summarizes a research paper on reengineering relational databases to object-oriented databases. It discusses developing an integrated environment that maps a relational schema to an object-oriented schema without modifying the existing relational schema. The proposed system architecture has two major components - one for mapping the relational schema to an object-oriented schema, and another for mapping relational data to objects. The schema mapping process is two-phased - the first phase transforms the relational schema, and the second phase extracts object-oriented structures. The system aims to allow existing applications and data in a relational database to be accessible from object-oriented programs.
Presentation of PhD thesis on Location Data Fusion Alket Cecaj
The document outlines Alket Cecaj's doctoral thesis on using information fusion methods for location data analysis. The thesis will introduce data fusion methods and examine fusing location data from call detail records and social media for applications like event detection and description. It will analyze re-identifying anonymized call data records using fused social network data and address related privacy issues. The thesis will evaluate different data fusion techniques and their ability to improve detection of real-world events from aggregated location information.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
ESA/ACT Science Coffee: Diego Blas - Gravitational wave detection with orbita...Advanced-Concepts-Team
Presentation in the Science Coffee of the Advanced Concepts Team of the European Space Agency on the 07.06.2024.
Speaker: Diego Blas (IFAE/ICREA)
Title: Gravitational wave detection with orbital motion of Moon and artificial
Abstract:
In this talk I will describe some recent ideas to find gravitational waves from supermassive black holes or of primordial origin by studying their secular effect on the orbital motion of the Moon or satellites that are laser ranged.
The cost of acquiring information by natural selectionCarl Bergstrom
This is a short talk that I gave at the Banff International Research Station workshop on Modeling and Theory in Population Biology. The idea is to try to understand how the burden of natural selection relates to the amount of information that selection puts into the genome.
It's based on the first part of this research paper:
The cost of information acquisition by natural selection
Ryan Seamus McGee, Olivia Kosterlitz, Artem Kaznatcheev, Benjamin Kerr, Carl T. Bergstrom
bioRxiv 2022.07.02.498577; doi: https://doi.org/10.1101/2022.07.02.498577
PPT on Alternate Wetting and Drying presented at the three-day 'Training and Validation Workshop on Modules of Climate Smart Agriculture (CSA) Technologies in South Asia' workshop on April 22, 2024.
Sexuality - Issues, Attitude and Behaviour - Applied Social Psychology - Psyc...PsychoTech Services
A proprietary approach developed by bringing together the best of learning theories from Psychology, design principles from the world of visualization, and pedagogical methods from over a decade of training experience, that enables you to: Learn better, faster!
When I was asked to give a companion lecture in support of ‘The Philosophy of Science’ (https://shorturl.at/4pUXz) I decided not to walk through the detail of the many methodologies in order of use. Instead, I chose to employ a long standing, and ongoing, scientific development as an exemplar. And so, I chose the ever evolving story of Thermodynamics as a scientific investigation at its best.
Conducted over a period of >200 years, Thermodynamics R&D, and application, benefitted from the highest levels of professionalism, collaboration, and technical thoroughness. New layers of application, methodology, and practice were made possible by the progressive advance of technology. In turn, this has seen measurement and modelling accuracy continually improved at a micro and macro level.
Perhaps most importantly, Thermodynamics rapidly became a primary tool in the advance of applied science/engineering/technology, spanning micro-tech, to aerospace and cosmology. I can think of no better a story to illustrate the breadth of scientific methodologies and applications at their best.
The debris of the ‘last major merger’ is dynamically youngSérgio Sacani
The Milky Way’s (MW) inner stellar halo contains an [Fe/H]-rich component with highly eccentric orbits, often referred to as the
‘last major merger.’ Hypotheses for the origin of this component include Gaia-Sausage/Enceladus (GSE), where the progenitor
collided with the MW proto-disc 8–11 Gyr ago, and the Virgo Radial Merger (VRM), where the progenitor collided with the
MW disc within the last 3 Gyr. These two scenarios make different predictions about observable structure in local phase space,
because the morphology of debris depends on how long it has had to phase mix. The recently identified phase-space folds in Gaia
DR3 have positive caustic velocities, making them fundamentally different than the phase-mixed chevrons found in simulations
at late times. Roughly 20 per cent of the stars in the prograde local stellar halo are associated with the observed caustics. Based
on a simple phase-mixing model, the observed number of caustics are consistent with a merger that occurred 1–2 Gyr ago.
We also compare the observed phase-space distribution to FIRE-2 Latte simulations of GSE-like mergers, using a quantitative
measurement of phase mixing (2D causticality). The observed local phase-space distribution best matches the simulated data
1–2 Gyr after collision, and certainly not later than 3 Gyr. This is further evidence that the progenitor of the ‘last major merger’
did not collide with the MW proto-disc at early times, as is thought for the GSE, but instead collided with the MW disc within
the last few Gyr, consistent with the body of work surrounding the VRM.
Travis Hills of MN is Making Clean Water Accessible to All Through High Flux ...Travis Hills MN
By harnessing the power of High Flux Vacuum Membrane Distillation, Travis Hills from MN envisions a future where clean and safe drinking water is accessible to all, regardless of geographical location or economic status.
Evidence of Jet Activity from the Secondary Black Hole in the OJ 287 Binary S...Sérgio Sacani
Wereport the study of a huge optical intraday flare on 2021 November 12 at 2 a.m. UT in the blazar OJ287. In the binary black hole model, it is associated with an impact of the secondary black hole on the accretion disk of the primary. Our multifrequency observing campaign was set up to search for such a signature of the impact based on a prediction made 8 yr earlier. The first I-band results of the flare have already been reported by Kishore et al. (2024). Here we combine these data with our monitoring in the R-band. There is a big change in the R–I spectral index by 1.0 ±0.1 between the normal background and the flare, suggesting a new component of radiation. The polarization variation during the rise of the flare suggests the same. The limits on the source size place it most reasonably in the jet of the secondary BH. We then ask why we have not seen this phenomenon before. We show that OJ287 was never before observed with sufficient sensitivity on the night when the flare should have happened according to the binary model. We also study the probability that this flare is just an oversized example of intraday variability using the Krakow data set of intense monitoring between 2015 and 2023. We find that the occurrence of a flare of this size and rapidity is unlikely. In machine-readable Tables 1 and 2, we give the full orbit-linked historical light curve of OJ287 as well as the dense monitoring sample of Krakow.
Microbial interaction
Microorganisms interacts with each other and can be physically associated with another organisms in a variety of ways.
One organism can be located on the surface of another organism as an ectobiont or located within another organism as endobiont.
Microbial interaction may be positive such as mutualism, proto-cooperation, commensalism or may be negative such as parasitism, predation or competition
Types of microbial interaction
Positive interaction: mutualism, proto-cooperation, commensalism
Negative interaction: Ammensalism (antagonism), parasitism, predation, competition
I. Mutualism:
It is defined as the relationship in which each organism in interaction gets benefits from association. It is an obligatory relationship in which mutualist and host are metabolically dependent on each other.
Mutualistic relationship is very specific where one member of association cannot be replaced by another species.
Mutualism require close physical contact between interacting organisms.
Relationship of mutualism allows organisms to exist in habitat that could not occupied by either species alone.
Mutualistic relationship between organisms allows them to act as a single organism.
Examples of mutualism:
i. Lichens:
Lichens are excellent example of mutualism.
They are the association of specific fungi and certain genus of algae. In lichen, fungal partner is called mycobiont and algal partner is called
II. Syntrophism:
It is an association in which the growth of one organism either depends on or improved by the substrate provided by another organism.
In syntrophism both organism in association gets benefits.
Compound A
Utilized by population 1
Compound B
Utilized by population 2
Compound C
utilized by both Population 1+2
Products
In this theoretical example of syntrophism, population 1 is able to utilize and metabolize compound A, forming compound B but cannot metabolize beyond compound B without co-operation of population 2. Population 2is unable to utilize compound A but it can metabolize compound B forming compound C. Then both population 1 and 2 are able to carry out metabolic reaction which leads to formation of end product that neither population could produce alone.
Examples of syntrophism:
i. Methanogenic ecosystem in sludge digester
Methane produced by methanogenic bacteria depends upon interspecies hydrogen transfer by other fermentative bacteria.
Anaerobic fermentative bacteria generate CO2 and H2 utilizing carbohydrates which is then utilized by methanogenic bacteria (Methanobacter) to produce methane.
ii. Lactobacillus arobinosus and Enterococcus faecalis:
In the minimal media, Lactobacillus arobinosus and Enterococcus faecalis are able to grow together but not alone.
The synergistic relationship between E. faecalis and L. arobinosus occurs in which E. faecalis require folic acid
MICROBIAL INTERACTION PPT/ MICROBIAL INTERACTION AND THEIR TYPES // PLANT MIC...
Deep Learning for Graphs
1. Deep Learning on Graphs
Sushravya GM
16th June 2018
(@Deep Learning Bangalore Meetup)
2. Contents
1. Quick look at day-to-day graphs & related ML
applications
(social/biological/information/utility/similarity
networks)
2. Overview of graph-based Machine Learning
3. Search for better results with Deep Learning
4. Introduction to Spectral Graph Convolutions
5. Example Applications of Graph Convolutional
Networks
6. Recent Developments in Relational Deep Learning
3. Graphs from Biological Networks
Related ML Tasks:
• Discover interactions
• Reconstruct the
structures
28. How To Increase Accuracy?
Using Deep Learning to exploit:
1. Translation Invariance (weight sharing)
2. Hierarchical Compositionality
Take advantage of the structure of the data!!
But, in case of Graphs, where do we start?
29. Data from Regular domains
(Grids/Lattices)
Data from Irregular domains
31. We pick an assumption and work forwards!
Assumption:
Data from the graph domain are locally stationary and
manifest hierarchical structures
Next Challenge:
How to define compositionality
(convolution and pooling mechanisms) on graph data?
33. Extending the Concept of CNNs to Graph domain
Two possibilities…
1. Spatial Filtering: Sliding a filter of defined receptive
field(patch) across the graph.
2. Spectral Filtering: Exploiting the concept that
convolutions in spatial domain correspond to
multiplications in Fourier domain.
34. Spatial Filtering:
(+) Most Natural Analogy with the regular
structures. (intuitive)
(-) Requires defining a neighbourhood system and
a node ordering. (not at all intuitive)
Spatial Filtering:
(+) Does not require defining a neighbourhood
system and a node ordering. Can obtain strictly
localized filters.
(-) Extracted features non-transferable between
graphs
35. (from DSP) The convolution theorem states that In other words,
convolution of two functions in one domain equals point-wise
multiplication in the other domain.
For Image data,
Fourier
Transform
Multiplied
with a
Filter
in frequency
domain
Fourier
Transform Inverse
Fourier
Transform
36. What about graph data?
How to project the graph signals into the frequency domain?
Spatial domain (G) => Spectral
domain(G)
Steps:
1. Compute Graph Laplacian.
2. Decompose it into a full matrix of
orthonormal eigenvectors
But,
1. What is Graph Laplacian?
2. What are Eigen Vectors?
3. How to decompose Graph Laplacian into a matrix of orthonormal
eigenvectors?
38. What are Eigen Values and Eigen Vectors?
Eigenvalues are closely related to almost all major invariants
of a graph, linking one extremal property to another
The eigen values reflect the
importance of the corresponding
eigen vectors in reconstructing
the original graph structure.
39. How to decompose Graph Laplacian into a matrix of
orthonormal eigenvectors?
40.
41. Convolution in regular domain
G is circular in structure. Hence, shift invariant!
Filter coefficients do not depend on basis.
51. Recent Developments in
Relational Deep Learning
1. Relational inductive biases, deep learning,
and graph networks
2. Relational Deep Reinforcement Learning
3. Relational recurrent neural networks
52.
53.
54.
55.
56. References:
• Deep Feature Learning for Graphs
https://arxiv.org/pdf/1704.08829.pdf
• Learning Convolutional Neural Networks for Graphs
http://proceedings.mlr.press/v48/niepert16.pdf
• Deep Learning on Graphs with Graph Convolutional Networks [ppt]
http://deeploria.gforge.inria.fr/thomasTalk.pdf
• Graph Convolutional Networks [blog]
http://tkipf.github.io/graph-convolutional-networks/
• Geometric deep learning: going beyond Euclidean data
https://arxiv.org/pdf/1611.08097.pdf
• Convolutional Neural Networks on Graphs [ppt]
http://helper.ipam.ucla.edu/publications/dlt2018/dlt2018_14506.pdf
• CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters
https://arxiv.org/pdf/1705.07664.pdf
57. • Deep Geometric Matrix Completion [ppt]
http://helper.ipam.ucla.edu/publications/dlt2018/dlt2018_14552.pdf
• On Computational Hardness and Graph Neural Networks
http://helper.ipam.ucla.edu/publications/dlt2018/dlt2018_14508.pdf
• Machine Learning Meets Geometry [ppt]
http://geometry.cs.ucl.ac.uk/SGP2017/slides/Rodola_MachineLearningMeetsGeometry_SGP.pdf
• Geometric deep learning on graphs and manifolds [ppt]
http://geometricdeeplearning.com/slides/NIPS-GDL.pdf
• Spectral Graph Convolutions for Population-based Disease Prediction
https://arxiv.org/pdf/1703.03020.pdf .
[Code@ https://github.com/parisots/population-gcn]
• Semi-supervised Classification with Graph Convolutional Networks
https://arxiv.org/pdf/1609.02907.pdf
• Geometric deep learning on graphs and manifolds using mixture model CNNs
https://arxiv.org/pdf/1611.08402.pdf
58. • Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks
• https://arxiv.org/pdf/1704.06803v1.pdf
• [Code@ https://github.com/fmonti/mgcnn]
• Distance Metric Learning using Graph Convolutional Networks Application to Functional
Brain Networks
• https://arxiv.org/abs/1703.02161
• [Code@ https://github.com/sk1712/gcn_metric_learning]
Other code links:
• A tutorial on Graph Convolutional Neural Networks
https://github.com/dbusbridge/gcn_tutorial
• Graph-based Neural Networks
https://github.com/sungyongs/graph-based-nn
https://github.com/LeeDoYup/Graph-Convolutional-Networks
https://github.com/fps7806/Graph-CNN
• FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
https://github.com/matenure/FastGCN
• Graph Convolutional Networks in PyTorch
https://github.com/tkipf/pygcn
• Graph Convolutional Networks
https://github.com/tkipf/gcn
Note: The images/equations used in the slides are borrowed from either Google images, Wikipedia or from respective
research papers/presentations