Results 251 to 260 of about 6,652,811 (287)

Graph transform learning

Neural Networks, 2020
Transform learning is a new representation learning framework where we learn an operator/transform that analyses the data to generate the coefficient/representation. We propose a variant of it called the graph transform learning; in this we explicitly account for the correlation in the dataset in terms of graph Laplacian.
openaire   +3 more sources

Unsupervised Graph Embedding via Adaptive Graph Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Graph autoencoders (GAEs) are powerful tools in representation learning for graph embedding. However, the performance of GAEs is very dependent on the quality of the graph structure, i.e., of the adjacency matrix. In other words, GAEs would perform poorly when the adjacency matrix is incomplete or be disturbed.
Rui Zhang, Yunxing Zhang, Chengjun Lu, Xuelong Li   +3 more
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Internet Financial Fraud Detection Based on Graph Learning

IEEE Transactions on Computational Social Systems, 2023
The rapid development of information technology such as the Internet of Things, Big Data, artificial intelligence, and blockchain has changed the transaction mode of the financial industry and greatly improved the convenience of financial transactions ...
Ranran Li, Zhaowei Liu, Yuanqing Ma, D. Yang, Shuaijie Sun   +4 more
semanticscholar   +1 more source

Learning Unknown Graphs

2009
Motivated by a problem of targeted advertising in social networks, we introduce and study a new model of online learning on labeled graphs where the graph is initially unknown, and the algorithm is free to choose the next vertex to predict. After observing that natural nonadaptive exploration/prediction strategies (like depth-first with majority vote ...
Cesa Bianchi N, GENTILE, CLAUDIO, Vitale F.   +2 more
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Does Invariant Graph Learning via Environment Augmentation Learn Invariance?

Neural Information Processing Systems, 2023
Invariant graph representation learning aims to learn the invariance among data from different environments for out-of-distribution generalization on graphs.
Yongqiang Chen, Yatao Bian, Kaiwen Zhou, Binghui Xie, Bo Han, James Cheng   +5 more
semanticscholar   +1 more source

Multiple Graph-Kernel Learning

2015 IEEE Symposium Series on Computational Intelligence, 2015
Kernels for structures, including graphs, generally suffer of the diagonally dominant gram matrix issue, the effect by which the number of sub-structures, or features, shared between instances are very few with respect to those shared by an instance with itself.
AIOLLI, FABIO, DONINI, MICHELE, NAVARIN, NICOLO', SPERDUTI, ALESSANDRO   +3 more
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Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

International Conference on Machine Learning
While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance.
Maya Bechler-Speicher, Ben Finkelshtein, Fabrizio Frasca, Luis Müller, Jan Tönshoff, Antoine Siraudin, V. Zaverkin, Michael M. Bronstein, Mathias Niepert, Bryan Perozzi, Mikhail Galkin, Christopher Morris   +11 more
semanticscholar   +1 more source

KAGNNs: Kolmogorov-Arnold Networks meet Graph Learning

Trans. Mach. Learn. Res.
In recent years, Graph Neural Networks (GNNs) have become the de facto tool for learning node and graph representations. Most GNNs typically consist of a sequence of neighborhood aggregation (a.k.a., message-passing) layers, within which the ...
Roman Bresson, Giannis Nikolentzos, G. Panagopoulos, Michail Chatzianastasis, Jun Pang, M. Vazirgiannis   +5 more
semanticscholar   +1 more source

Meta Graph Learning for Long-tail Recommendation

Knowledge Discovery and Data Mining, 2023
Highly skewed long-tail item distribution commonly hurts model performance on tail items in recommendation systems, especially for graph-based recommendation models.
Chunyu Wei, Jian Liang, Di Liu, Zehui Dai, Man-Chun Li, Fei Wang   +5 more
semanticscholar   +1 more source

Graph-Based Semisupervised Learning

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008
Graph-based learning provides a useful approach for modeling data in classification problems. In this modeling scenario, the relationship between labeled and unlabeled data impacts the construction and performance of classifiers, and therefore a semi-supervised learning framework is adopted. We propose a graph classifier based on kernel smoothing.
Mark, Culp, George, Michailidis
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