Graph Representation Learning

INDUCTIVE AND UNSUPERVISED REPRESENTATION LEARNING ON ...

INDUCTIVE AND UNSUPERVISED REPRESENTATION LEARNING ON ...

Published as a conference paper at ICLR 2020 INDUCTIVE AND UNSUPERVISED REPRESENTATION LEARNING ON GRAPH STRUCTURED OBJECTS Lichen Wang1, Bo Zong 2, Qianqian Ma3, Wei Cheng2, Jingchao Ni2, Wenchao Yu , Yanchi Liu 2, Dongjin Song , Haifeng Chen2, and Yun Fu1 1Northeastern University, Boston, USA 2NEC Laboratories America, Princeton, USA 3Boston University, Boston, USA

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ECE 6504: Advanced Topics in Machine Learning

ECE 6504: Advanced Topics in Machine Learning

– Graph Theory + Probability • Compact representation for exponentially-large probability distributions – Exploit conditional independencies • Generalize – naïve Bayes – logistic regression – Many more … (C) Dhruv Batra 25 . Types of PGMs (C) Dhruv Batra 26 Graphical Models Directed Directed Factor Graph Bayesian Networks Dynamic Bayes nets Markov chains HMM LDS Latent ...

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Representation Learning with Weighted Inner Product for ...

Representation Learning with Weighted Inner Product for ...

We propose weighted inner product similarity (WIPS) for neural-network based graph em-bedding, where we optimize the weights of the inner product in addition to the parameters of neural networks. Despite its simplicity, WIPS can approximate arbitrary general similarities including positive de nite, conditionally positive de nite, and inde nite kernels. WIPS is free from similarity model ...

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Introductory Chemistry An Active Learning Approach 5th ...

Introductory Chemistry An Active Learning Approach 5th ...

3. Which is the best definition of the term model, as it is used in chemistry? a. A product from a kit from which molecules can be constructed b. A computer image of a molecule c. A representation of something else d. A person who is photographed for scientific journals e. A graph that shows the relationship between two variables ANS: C Analysis:

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Semi-Supervised Learning with Graphs

Semi-Supervised Learning with Graphs

gives higher accuracy, it is of great interest both in theory and in practice. We present a series of novel semi-supervised learning approaches arising from a graph representation, where labeled and unlabeled instances are represented as vertices, and edges encode the similarity between instances. They address the fol-

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INTEGRATIVE LEARNING VALUE RUBRIC

INTEGRATIVE LEARNING VALUE RUBRIC

meaning, making clear the interdependence of language and meaning, thought , and expression. Fulfills the assignment(s) by choosing a format, language, or graph (or other visual representation) to explicitly connect content and form, demonstrating awareness of purpose and audience. Fulfills the assignment(s) by choosing a

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A arXiv:1911.12247v2 [stat.ML] 5 Jan 2020

A arXiv:1911.12247v2 [stat.ML] 5 Jan 2020

world model from raw sensory data remains a challenge. As a step towards this goal, we introduce Contrastively-trained Structured World Models (C-SWMs). C- SWMs utilize a contrastive approach for representation learning in environments with compositional structure. We structure each state embedding as a set of ob-ject representations and their relations, modeled by a graph neural network. This ...

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arXiv:1803.04051v2 [cs.LG] 16 Mar 2018

arXiv:1803.04051v2 [cs.LG] 16 Mar 2018

Representation learning over graph structured data has emerged as keystone machine learning task due to its ubiquitous applicability in variety of domains such as social networks, bioinformatics, natural language processing, and relational knowledge bases. The key idea behind this task is to encode structural information at node (or subgraph) level into low-dimensional embedding vectors that ...

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Devign: Effective Vulnerability Identification by Learning ...

Devign: Effective Vulnerability Identification by Learning ...

structure with comprehensive program semantics; 2) Gated Graph Recurrent Layers, which learn the features of nodes through aggregating and passing information on neighboring nodes in graphs; and 3) the Conv module that extracts meaningful node representation for graph-level prediction. 2.1 Problem Formulation Most machine learning or pattern based approaches predict vulnerability at the coarse ...

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Discovering the Structure of a Reactive Environment by ...

Discovering the Structure of a Reactive Environment by ...

developing an algorithm to learn the FSA directly, there are several arguments against doing so (Schapire, 1988). Most important is that the FSA often does not capture struc­ ture inherent in the environment. Rather than trying to learn the FSA, Rivest and Scbapire suggest learning another representation of the environment called an update graph. The advantage of the update graph is that in ...

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Knowledge-aware Graph Neural Networks with Label ...

Knowledge-aware Graph Neural Networks with Label ...

from node’s local network neighborhood using neural networks, represent a promising advancement in graph-based representation learning [3, 5–7, 11, 15]. Recently, several works developed GNNs architecture for recommender systems [14, 19, 28, 31, 32], but these approaches are mostly designed for homogeneous bipartite user-item interaction graphs or user-/item-similarity graphs. It remains ...

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Representation Learning on Graphs: Methods and Applications

Representation Learning on Graphs: Methods and Applications

Machine learning applications seek to make predictions, or discover new patterns, using graph-structured data as feature information. For example, one might wish to classify the role of a protein in a biological interaction graph [28], predict the role of a person in a collaboration network, recommend new friends to a user in a social network [3], or predict new therapeutic applications of ...

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Graphical Models Inference and Learning Lecture 8

Graphical Models Inference and Learning Lecture 8

• Graphical Models – Directed vs Undirected – Representation and Modeling • Problem formulation – Energy/cost function • MAP estimation – Belief propagation, TRW, graph cuts, LP relaxation, primal-dual, dual decomposition • Learning – Maximum likelihood, max-margin learning Recall This class • Bayesian Networks – Parameter Learning – Structure Learning – Inference But ...

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Robust Representation For Data Analytics Models And ...

Robust Representation For Data Analytics Models And ...

This book introduces the concepts and models of robust representation learning, and provides a set of solutions to deal with real-world data analytics tasks, such as clustering, classification, time series modeling, outlier detection, collaborative filtering, community detection, etc. Three types of robust feature representations are developed, which extend the understanding of graph, subspace ...

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Type-Constrained Representation Learning in Knowledge Graphs

Type-Constrained Representation Learning in Knowledge Graphs

Keywords: Knowledge Graph, Representation Learning, Latent Vari-able Models, Type-Constraints, Local Closed-World Assumption, Link-Prediction 1 Introduction Knowledge graphs (KGs), i.e., graph-based knowledge-bases, have proven to be sources of valuable information that have become important for various applica-tions like web-search or question answering. Whereas, KGs were initially driven by ...

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DySAT: Deep Neural Representation Learning on Dynamic ...

DySAT: Deep Neural Representation Learning on Dynamic ...

Existing graph representation learning methods primarily target static graphs while many real-world graphs evolve over time. Complex time-varying graph structures make it chal-lenging to learn informative node representations over time. We present Dynamic Self-Attention Network (DySAT), a novel neural architecture that learns node representations to capture dy-namic graph structural evolution ...

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ...

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE ...

kernel based methods were also proposed [16], [17]. In [18], Yan et al. proposed a general graph embedding framework, where different dimensionality reduction and subspace learning methods such as PCA [7], LDA [8], LPP [19], ISOMAP [20], and LLE [21] can all be reformu-lated within this framework. Recently, Wright et al. [22] proposed a sparse representation framework for face rec-ognition in ...

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Teacher and Learning Activities Across The Curriculum

Teacher and Learning Activities Across The Curriculum

Health and Physical Education • Discuss the role of emotions in the story. How do the Gruffalo’s Child’s feelings change throughout the story? • Graph or draw a pictorial representation of the Gruffalo's Child's changes of emotion through bored, brave, frustrated, annoyed, scared, and relieved. Mime the emotions or play them on an instrument. • Move like the Gruffalo’s Child when ...

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Hierarchical Graph Representation Learning with ...

Hierarchical Graph Representation Learning with ...

data ??? or graph-based representations of molecules ???. The general approach with GNNs is to view the underlying graph as a computation graph and learn neural network primitives that generate individual node embeddings by passing, transforming, and aggregating node feature information across the graph ??. The generated node embeddings can then be used as input to any differentiable ...

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Hyperbolic Graph Convolutional Neural Networks

Hyperbolic Graph Convolutional Neural Networks

1 Introduction Graph Convolutional Neural Networks (GCNs) are state-of-the-art models for representation learning in graphs, where nodes of the graph are embedded into points in Euclidean space [15, 21, 41, 45]. However, many real-world graphs, such as protein interaction networks and social networks, often exhibit scale-free or hierarchical structure [7, 50] and Euclidean embeddings, used by ...

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Learning Knowledge Graph Embeddings for Natural Language ...

Learning Knowledge Graph Embeddings for Natural Language ...

Language Processing Muhao Chen Department of Computer Science University of California, Los Angeles Winter 2017. Abstract Knowledge graph embeddings provide powerful latent semantic representation for the structured knowledge in knowledge graphs, which have been introduced recently. Being different from the already widely-used word embeddings that are conceived from plain text, knowledge graph ...

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Accurate Text-Enhanced Knowledge Graph Representation Learning

Accurate Text-Enhanced Knowledge Graph Representation Learning

knowledge representation by exploiting additional information. For example, both the path informa-tion and logic rules have been proved to be ben-ecial for knowledge representation (Lin et al., 2015a;Toutanova et al.,2016;Xiong et al.,2017; Xie et al.,2016;Xu et al.,2016). One other direction to enhance knowledge representation is to utilize entity descriptions of entities and relations.Socher ...

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kLog: A Language for Logical and Relational Learning with ...

kLog: A Language for Logical and Relational Learning with ...

from interpretations, entity/relationship data mod-eling, and logic programming. Access by the ker-nel to the rich representation is mediated by a tech-nique we call graphicalization: the relational rep- resentation is ?rst transformed into a graph — in particular, a grounded entity/relationship diagram. Subsequently, a choice of graph kernel de?nes the feature space. The kLog framework ...

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Bayesian Networks: Learning from Data

Bayesian Networks: Learning from Data

Bayesian networks were originally developed as a knowledge representation formalism, with human experts their only source. Their two main features are: The ability to represent deep knowledge (knowledge as it is available in textbooks), improving portability, reusability, and modularity. They are grounded in statistics and graph theory. Late ’80s, people realize that the statistical ...

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Graphics Calculators in the Learning of Mathematics ...

Graphics Calculators in the Learning of Mathematics ...

calculator and discussed whether or not the image was a reasonable representation of the function. (The initial window is a default window which, in the case of the Casio fx-7400G , has equally scaled coordinate axes marked at unit intervals. In addition, the x-value steps by 0.1 unit as one traces along the graph.) The workshop

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REGAL: Representation Learning-based Graph Alignment

REGAL: Representation Learning-based Graph Alignment

However, recent advances [9, 28, 35, 39] have auto-mated the process of learning node feature representations and have led to state-of-the-art performance in downstream prediction, classification, and clustering tasks. Motivated by these successes, we propose network alignment via matching latent, learned node representations. Formally, the problem can be stated as: Problem 1. Given two ...

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Quantum Deep Learning - School of Physics

Quantum Deep Learning - School of Physics

Quantum Deep Learning ... •Desire: learn a complex representation (e.g., full Boltzmann machine) •Intractable to learn fully connected graph poorer representation •Pretrain layers? •Learn simpler graph with faster train time? •Desire: efficient computation of true gradient •Intractable to learn actual objective poorer representation •Approximate the gradient? •Desire: training ...

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Deep Learning: State of the Art (2020) - Lex Fridman

Deep Learning: State of the Art (2020) - Lex Fridman

Competition and Convergence of Deep Learning Libraries TensorFlow 2.0 and PyTorch 1.3 •Eager execution by default (imperative programming) •Keras integration + promotion •Cleanup (API, etc.) •TensorFlow.js •TensorFlow Lite •TensorFlow Serving •TorchScript (graph representation) •Quantization •PyTorch Mobile (experimental) •TPU support Python 2 support ended on Jan 1, 2020 ...

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Accelerated Machine Learning Using TensorFlow and SYCL on ...

Accelerated Machine Learning Using TensorFlow and SYCL on ...

learning framework. „e data-…ow graph model of TensorFlow is composed of two main elements: edge and node. An input/output data, called a Tensor, is represented by an edge between node i to j stating the output from node i and the input to node j. A tensor is an n-dimensional representation of an array of primitive data type. A node of the graph represents a unit of computation, shared ...

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edge2vec: Representation learning using edge semantics for ...

edge2vec: Representation learning using edge semantics for ...

nodes are critical for representation learning and knowledge discovery in real world biomedical problems. Results: In this paper, we propose the edge2vec model, which represents graphs considering edge semantics. An edge-type transition matrix is trained by an Expectation-Maximization approach, and a stochastic gradient descent model is employed to learn node embedding on a heterogeneous graph ...

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