A survey of graph neural networks for recommender systems: Challenges, methods, and directions
Recommender system is one of the most important information services on today's Internet.
Recently, graph neural networks have become the new state-of-the-art approach to …
Recently, graph neural networks have become the new state-of-the-art approach to …
Graph neural architecture search: A survey
BM Oloulade, J Gao, J Chen, T Lyu… - Tsinghua Science and …, 2021 - ieeexplore.ieee.org
In academia and industries, graph neural networks (GNNs) have emerged as a powerful
approach to graph data processing ranging from node classification and link prediction tasks …
approach to graph data processing ranging from node classification and link prediction tasks …
Data-centric artificial intelligence: A survey
Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler
of its great success is the availability of abundant and high-quality data for building machine …
of its great success is the availability of abundant and high-quality data for building machine …
Bond: Benchmarking unsupervised outlier node detection on static attributed graphs
Detecting which nodes in graphs are outliers is a relatively new machine learning task with
numerous applications. Despite the proliferation of algorithms developed in recent years for …
numerous applications. Despite the proliferation of algorithms developed in recent years for …
Not too little, not too much: a theoretical analysis of graph (over) smoothing
N Keriven - Advances in Neural Information Processing …, 2022 - proceedings.neurips.cc
We analyze graph smoothing with mean aggregation, where each node successively
receives the average of the features of its neighbors. Indeed, it has quickly been observed …
receives the average of the features of its neighbors. Indeed, it has quickly been observed …
Interpreting and unifying graph neural networks with an optimization framework
Graph Neural Networks (GNNs) have received considerable attention on graph-structured
data learning for a wide variety of tasks. The well-designed propagation mechanism which …
data learning for a wide variety of tasks. The well-designed propagation mechanism which …
Reinforced, incremental and cross-lingual event detection from social messages
Detecting hot social events (eg, political scandal, momentous meetings, natural hazards,
etc.) from social messages is crucial as it highlights significant happenings to help people …
etc.) from social messages is crucial as it highlights significant happenings to help people …
Reinforced neighborhood selection guided multi-relational graph neural networks
Graph Neural Networks (GNNs) have been widely used for the representation learning of
various structured graph data, typically through message passing among nodes by …
various structured graph data, typically through message passing among nodes by …
Search to aggregate neighborhood for graph neural network
Z Huan, YAO Quanming… - 2021 IEEE 37th …, 2021 - ieeexplore.ieee.org
Recent years have witnessed the popularity and success of graph neural networks (GNN) in
various scenarios. To obtain data-specific GNN architectures, researchers turn to neural …
various scenarios. To obtain data-specific GNN architectures, researchers turn to neural …
A survey on graph representation learning methods
S Khoshraftar, A An - ACM Transactions on Intelligent Systems and …, 2024 - dl.acm.org
Graph representation learning has been a very active research area in recent years. The
goal of graph representation learning is to generate graph representation vectors that …
goal of graph representation learning is to generate graph representation vectors that …