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检索条件"主题词=Graph Representation Learning"

共 843 条记录，以下是31-40 订阅

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graph representation learning via graphical Mutual Information Maximization 20

Graph Representation Learning via Graphical Mutual Informati...

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29th World Wide Web Conference (WWW)

作者： Peng, Zhen Huang, Wenbing Luo, Minnan Zheng, Qinghua Rong, Yu Xu, Tingyang Huang, Junzhou Xi An Jiao Tong Univ Sch Comp Sci & Technol Key Lab Intelligent Networks & Network Secur Minist Educ Xian Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing Natl Res Ctr Informat Sci & Technol BNRis State Key Lab Intelligent Technol & Syst Beijing Peoples R China Tencent AI Lab Shenzhen Peoples R China

ISBN: (纸本)9781450370233

The richness in the content of various information networks such as social networks and communication networks provides the unprecedented potential for learning high-quality expressive representations without external supervision. This paper investigates how to preserve and extract the abundant information from graph-structured data into embedding space in an unsupervised manner. To this end, we propose a novel concept, graphical Mutual Information (GMI), to measure the correlation between input graphs and high-level hidden representations. GMI generalizes the idea of conventional mutual information computations from vector space to the graph domain where measuring mutual information from two aspects of node features and topological structure is indispensable. GMI exhibits several benefits: First, it is invariant to the isomorphic transformation of input graphs-an inevitable constraint in many existing graph representation learning algorithms;Besides, it can be efficiently estimated and maximized by current mutual information estimation methods such as MINE;Finally, our theoretical analysis confirms its correctness and rationality. With the aid of GMI, we develop an unsupervised learning model trained by maximizing GMI between the input and output of a graph neural encoder. Considerable experiments on transductive as well as inductive node classification and link prediction demonstrate that our method outperforms state-of-the-art unsupervised counterparts, and even sometimes exceeds the performance of supervised ones.

关键词： graph representation learning Mutual information InfoMax

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graph representation learning for Merchant Incentive Optimization in Mobile Payment Marketing 19

Graph Representation Learning for Merchant Incentive Optimiz...

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28th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Liu, Ziqi Wang, Dong Yu, Qianyu Zhang, Zhiqiang Shen, Yue Ma, Jian Zhong, Wenliang Gu, Jinjie Zhou, Jun Yang, Shuang Qi, Yuan Ant Financial Serv Grp Hangzhou Peoples R China

ISBN: (纸本)9781450369763

Mobile payment such as Alipay has been widely used in our daily lives. To further promote the mobile payment activities, it is important to run marketing campaigns under a limited budget by providing incentives such as coupons, commissions to merchants. As a result, incentive optimization is the key to maximizing the commercial objective of the marketing campaign. With the analyses of online experiments, we found that the transaction network can subtly describe the similarity of merchants' responses to different incentives, which is of great use in the incentive optimization problem. In this paper, we present a graph representation learning method atop of transaction networks for merchant incentive optimization in mobile payment marketing. With limited samples collected from online experiments, our end-to-end method first learns merchant representations based on an attributed transaction networks, then effectively models the correlations between the commercial objectives each merchant may achieve and the incentives under varying treatments. Thus we are able to model the sensitivity to incentive for each merchant, and spend the most budgets on those merchants that show strong sensitivities in the marketing campaign. Extensive offline and online experimental results at Alipay demonstrate the effectiveness of our proposed approach.

关键词： incentive optimization mobile payment marketing graph representation learning

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graph representation learning of Banking Transaction Network with EdgeWeight-Enhanced Attention and Textual Information 22

Graph Representation Learning of Banking Transaction Network...

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31st ACM Web Conference (WWW)

作者： Minakawa, Naoto Izumi, Kiyoshi Sakaji, Hiroki Sano, Hitomi Univ Tokyo Tokyo Japan

ISBN: (纸本)9781450391306

In this paper, we propose a novel approach to capture inter-company relationships from banking transaction data using graph neural networks with a special attention mechanism and textual industry or sector information. Transaction data owned by financial institutions can be an alternative source of information to comprehend real-time corporate activities. Such transaction data can be applied to predict stock price and miscellaneous macroeconomic indicators as well as to sophisticate credit and customer relationship management. Although the inter-company relationship is important, traditional methods for extracting information have not captured that enough. With the recent advances in deep learning on graphs, we can expect better extraction of inter-company information from banking transaction data. Especially, we analyze common issues that arise when we represent banking transactions as a network and propose an efficient solution to such problems by introducing a novel edge weight-enhanced attention mechanism, using textual information, and designing an efficient combination of existing graph neural networks.

关键词： graph Attention Network graph Isomorphism Network graph Autoencoder Doc2Vec graph representation learning Natural Language Processing Financial Transaction Network

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graph representation learning for Context-Aware Network Intrusion Detection 5

Graph Representation Learning for Context-Aware Network Intr...

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Conference on Artificial Intelligence and Machine learning for Multi-Domain Operations Applications V

作者： Premkumar, Augustine Schneider, Madeleine Spivey, Carlton Pavlik, John A. Bastian, Nathaniel D. US Mil Acad Mathemat Sci Dept West Point NY 10996 USA US Mil Acad Army Cyber Inst West Point NY 10996 USA

ISBN: (数字)9781510661936

ISBN: (纸本)9781510661929;9781510661936

Detecting malicious activity using a network intrusion detection system (NIDS) is an ongoing battle for the cyber defender. Increasingly, cyber-attacks are sophisticated and occur rapidly, necessitating the use of machine/deep learning (ML/DL) techniques for network intrusion detection. Traditional ML/DL techniques for NIDS classifiers, however, are often unable to sufficiently find context-driven similarities between the various network flows and/or packet captures. In this work, we leverage graph representation learning (GRL) techniques to successfully detect adversarial intrusions by exploiting the graph structure of NIDS data to derive context awareness, as graphs are a universal language for describing entities and their relationships. We explore several methods for NIDS data graph representation at both the network flow and packet level utilizing the CIC-IDS2017 dataset. We leverage graph neural networks and graph embedding algorithms to create a context-aware network intrusion detection system. Results indicate that adding context derived from GRL improves performance for detecting attacks. Our highest-scoring classifier incorporated both GNN embeddings and flow-level features and achieved an accuracy of 99.9%. Adding GRL methods to augment the flow/packet features improved accuracy by as much as 52.41%.

关键词： graph representation learning Network Intrusion Detection Deep learning Context-awareness

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graph representation learning via Adversarial Variational Bayes 21

Graph Representation Learning via Adversarial Variational Ba...

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30th ACM International Conference on Information and Knowledge Management (CIKM)

作者： Li, Yunhe Hu, Yaochen Zhang, Yingxue Univ Montreal Montreal PQ Canada Huawei Noahs Ark Lab Montreal PQ Canada

ISBN: (纸本)9781450384469

Methods that learn representations of nodes in a graph play an important role in network analysis. Most of the existing methods of graph representation learning have focused on embedding each node in a graph as a single vector in a low-dimensional continuous space. However, these methods have a crucial limitation: the lack of modeling the uncertainty about the representation. In this work, inspired by Adversarial Variational Bayes (AVB) [22], we propose graphAVB, a probabilistic generative model to learn node representations that preserve connectivity patterns and capture the uncertainties in the graph. Unlike graph2Gauss [3] which embeds each node as a Gaussian distribution, we represent each node as an implicit distribution parameterized by a neural network in the latent space, which is more flexible and expressive to capture the complex uncertainties in real-world graph-structured datasets. To perform the designed variational inference algorithm with neural samplers, we introduce an auxiliary discriminative network that is used to infer the log probability ratio terms in the objective function and allows us to cast maximizing the objective function as a two-player game. Experimental results on multiple real-world graph datasets demonstrate the effectiveness of our proposed method graphAVB, outperforming many competitive baselines on the task of link prediction. The superior performances of our proposed method graphAVB also demonstrate that the downstream tasks can benefit from the captured uncertainty.

关键词： graph representation learning Adversarial Variational Bayes

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graph representation learning for Multi-Task Settings: a Meta-learning Approach

Graph Representation Learning for Multi-Task Settings: a Met...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Buffelli, Davide Vandin, Fabio Univ Padua Dept Informat Engn Padua Italy

ISBN: (数字)9781728186719

ISBN: (纸本)9781728186719

graph Neural Networks (GNNs) have become the state-of-the-art method for many applications on graph structured data. GNNs are a model for graph representation learning, which aims at learning to generate low dimensional node embeddings that encapsulate structural and feature-related information. GNNs are usually trained in an end-to-end fashion, leading to highly specialized node embeddings. While this approach achieves great results in the single-task setting, the generation of node embeddings that can be used to perform multiple tasks (with performance comparable to single-task models) is still an open problem. We propose the use of meta-learning to allow the training of a GNN model capable of producing multi-task node embeddings. In particular, we exploit the properties of optimization-based meta-learning to learn GNNs that can produce general node representations by learning parameters that can quickly (i.e. with a few steps of gradient descent) adapt to multiple tasks. Our experiments show that the embeddings produced by a model trained with our purposely designed meta-learning procedure can be used to perform multiple tasks with comparable or, surprisingly, even higher performance than both single-task and multi-task end-to-end models.

关键词： Machine learning representation learning Artificial Neural Networks graph Neural Networks graph representation learning

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graph representation learning, Deep Generative Models on graphs, Group Equivariant Molecular Neural Networks and Multiresolution Machine learning

Graph Representation Learning, Deep Generative Models on Gra...

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作者： Hy, Truong Son The University of Chicago

学位级别：Ph.D., Doctor of Philosophy

graph neural networks (GNNs) utilizing various ways of generalizing the concept of convolution to graphs have been widely applied to many learning tasks, including modeling physical systems, finding molecular representations to estimate quantum chemical computation, etc. Most existing GNNs address permutation invariance by conceiving of the network as a message passing scheme, where each node sums the feature vectors coming from its neighbors. We argue that this scheme imposes a limitation on the representation power of GNNs such that each node loses their identity after being aggregated by summing. Thus, we propose a new general architecture called Covariant Compositional Networks (CCNs) in which the node features are represented by higher order tensors and transform covariantly/equivariantly according to a specific representation of the symmetry group of its receptive field. Experiments show that CCNs can outperform competing methods on standard graph learning benchmarks and on estimating the molecular properties calculated by computationally expensive Density Functional Theory (DFT). This novel machine learning approach allows scientists to efficiently extract chemical knowledge and explore the increasingly growing chemical data. Understanding graphs in a multiscale perspective is essential for capturing the large-scale structure of molecules, proteins, genomes, etc. For this reason, we introduce Multiresolution Equivariant graph Variational Autoencoder (MGVAE), the first hierarchical generative model to learn and generate graphs in a multiresolution and equivariant manner. MGVAE is built upon Multiresolution graph Network (MGN), an architecture which explicitly learns a multilevel hard clustering of the vertices, leading to a true multiresolution hierarchy. MGVAE then employs the hierarchical variational autoencoder model to stochastically generate a graph in multiple resolution levels given the hierarchy of latent distributions. Our proposed framework achieves

关键词： graph representation learning Deep generative models graph Molecular neural networks Machine learning

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graph representation learning In A Contrastive Framework For Community Detection 26

Graph Representation Learning In A Contrastive Framework For...

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26th International Computer Conference of the Computer-Society-of-Iran

作者： Balouchi, Mehdi Ahmadi, Ali KN Toosi Univ Technol Fac Comp Engn Tehran Iran

ISBN: (纸本)9781665412414

graph structured data has become very popular and useful recently. Many areas in science and technology are using graphs for modeling the phenomena they are dealing with (e.g., computer science, computational economics, biology,.). Since the volume of data and its velocity of generation is increasing every day, using machine learning methods for analyzing this data has become necessary. For this purpose, we need to find a representation for our graph structured data that preserves topological information of the graph alongside the feature information of its nodes. Another challenge in incorporating machine learning methods as a graph data analyzer is to provide enough amount of labeled data for the model which may be hard to do in real-world applications. In this paper we present a graph neural network-based model for learning node representations that can be used efficiently in machine learning methods. The model learns representations in an unsupervised contrastive framework so that there is no need for labels to be present. Also, we test our model by measuring its performance in the task of community detection of graphs. Performance comparing on two citation graphs shows that our model has a better ability to learn representations that have a higher accuracy for community detection than other models in the field.

关键词： representation learning graph representation learning Contrastive learning Community detection graph neural networks

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graph representation learning: Foundations, Methods, Applications and Systems 21

Graph Representation Learning: Foundations, Methods, Applica...

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27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD)

作者： Jin, Wei Ma, Yao Wang, Yiqi Liu, Xiaorui Tang, Jiliang Cen, Yukuo Qiu, Jiezhong Tang, Jie Shi, Chuan Ye, Yanfang Zhang, Jiawei Yu, Philip S. Michigan State Univ Comp Sci & Engn E Lansing MI 48824 USA Michigan State Univ Comp Sci & Engn Dept E Lansing MI 48824 USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China Case Western Reserve Univ Cleveland OH 44106 USA Case Western Reserve Univ Dept Comp & Data Sci Cleveland OH 44106 USA Florida State Univ Tallahassee FL 32306 USA Florida State Univ Comp Sci Tallahassee FL 32306 USA Univ Illinois Chicago IL USA Univ Illinois Comp Sci Chicago IL USA

ISBN: (纸本)9781450383325

graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be facilitated. Instead of designing hand-engineered features, graph representation learning has emerged to learn representations that can encode the abundant information about the graph. It has achieved tremendous success in various tasks such as node classification, link prediction, and graph classification and has attracted increasing attention in recent years. In this tutorial, we systematically review the foundations, techniques, applications and advances in graph representation learning. We first introduce the foundations on graph theory and graph Fourier analysis. We then cover the key achievements of graph representation learning in recent years. Concretely, we discuss the six topics: 1) network embedding theories and systems;2) foundations of graph neural networks (GNNs);3) CogDL toolkit for GNNs;4) scalable GNNs;5) self-supervised learning in GNNs and 6) heterogeneous graphs and heterogeneous GNNs. Finally, we will introduce the applications of graph representation learning with a focus on recommender systems.

关键词： heterogeneous graphs graph neural networks graph representation learning

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graph representation learning with graph Transformers in Neural Combinatorial Optimization 22

Graph Representation Learning with Graph Transformers in Neu...

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22nd IEEE International Conference on Machine learning and Applications, ICMLA 2023

作者： Wang, Tianze Payberah, Amir H. Vlassov, Vladimir Kth Royal Institute of Technology Department of Computer Science Stockholm Sweden

ISBN: (纸本)9798350345346

Neural combinatorial optimization aims to use neural networks to speed up the solving process of combinatorial optimization problems, i.e., finding the optimal solution of a problem instance from a finite set of feasible solutions that minimize a given objective function. Recently, researchers have applied convolutional neural networks to predict the optimal solution's cost (defined by the objective function) to give as extra input to an exact solver to speed up the solving process. In this paper, we investigate whether graph representations that explicitly model the inherent constraints in combinatorial optimization problems would improve the performance of predicting the optimal solution's cost. Specifically, we use graph neural networks with neighborhood aggregation and graph Transformer models to capture and embed the knowledge in the graph representations of combinatorial optimization problems. We also propose a benchmark dataset containing the Traveling Salesman Problem (TSP) and Job-Shop Scheduling Problem (JSSP), and through the empirical evaluation, we show that graph Transformer models achieve an average loss decrease of 61.05% on TSP and 66.53% on JSSP compared to the baseline convolutional neural networks. © 2023 IEEE.

关键词： Combinatorial Optimization graph representation learning graph Transformer Job-Shop Scheduling Problem Traveling Salesman Problem

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