Multi-relational Graph Neural Architecture Search with Fine-Grained . . . Index Terms—graph neural networks, automated graph learn-ing, graph neural architecture search, multi-relational graphs, fine-grained message passing I INTRODUCTION Graphs are pervasive structured data and have been widely used in many real-world scenarios, such as social networks [1], knowledge bases [2], and recommendation systems [3] Re-
Search for deep graph neural networks - ScienceDirect Meanwhile, appearing as a powerful tool for automatic neural network optimization, neural network architecture search (NAS) [4], [5] has been widely applied for discovering high-performance network architectures in convolutional neural networks (CNNs) and recurrent neural networks (RNNs) Recently, GraphNAS [6] has made the first attempt to apply the NAS method to graph data by utilizing
Lightweight graph neural network architecture search based on heuristic . . . A graph neural network is a deep learning model for processing graph data In recent years, graph neural network architectures have become more and more complex as the research progresses, thus the design of graph neural networks has become an important task Graph Neural Architecture Search aims to automate the design of graph neural network architectures However, current methods require
Graph-Based Vector Search: An Experimental Evaluation of the State-of . . . While SotA graph-based vector search methods adopt diverse strategies for constructing the graph, they virtually all use beam search for query answering (Algorithm 1) because it usually retrieves good answers if the graph is well-connected However, choosing the right nodes to visit first has an impact on how quickly good answers are found
Chapter 14 Graph Neural Networks: Graph Structure Learning - GitHub Pages However, because the graph structure learning process does not consider any particular downstream prediction task on the data, the learned graph structure might be sub-optimal for the downstream task 14 2 1 1 Graph Structure Learning from Smooth Signals Graph structure learning has been extensively studied in the literature of Graph Sig-
Graph Neural Architecture Search: A Survey - IEEE Xplore transforming computation results into graph data Graph neural networks (GNNs) successfully tackle this problem and have thus become a very popular approach in academia and in industries Graph data have recently become ubiquitous in our lives The omnipresence of graph data has boosted the research on graph pattern recognition and graph mining
Graph Differentiable Architecture Search with Structure Learning Graph structure learning aims to generate a clean graph during the training procedure To achieve this, prior constraints such as low rank and smoothness are utilized Learning a new graph structure improves the model’s ability of denoising, as well as its robustness to adversarial attacks of GNNs 2 2 (Graph) Neural Architecture Search
Graph neural networks: A review of methods and applications In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures (like the dependency trees of sentences and the scene graphs of images) is an important research topic which also needs graph reasoning models Graph neural networks (GNNs) are neural models that capture the dependence of graphs
Rethinking Structure Learning For Graph Neural Networks With the above framework, we explore the effectiveness of GSL in GNNs As examples shown in Figure 1, a GSL method creates new connections between nodes with similar GSL bases, which is denoted as the contextual information of the ego node and its neighbors in this case When the GSL bases show high consistency with intra-class nodes, nodes within the same class are connected, which is
Graph-based Neural Architecture Search with Operation Embeddings neural architecture search tasks 3 1 Neural Network Architecture as a Directed Acyclic Graph A neural network architecture represents a computation, that is applied to an input signal using a fixed set of opera-tions We can define the computational graph of an architec-ture Aas G A= (V,E), where V is the set of nodes or the
Adaptive multi-scale Graph Neural Architecture Search framework The search space specifies the set of possible neural network structures, and the larger the search space, the more structures can be searched, which means that we have more chances to find a better architecture However, too large a search space can also lead to an explosion of computing efforts, making the search time-consuming and expensive
Search For Deep Graph Neural Networks - arXiv. org 2 2 Graph Neural Network Architecture Designed for learning in graph domains, Graph Neu-ral Networks (GNNs) are initially proposed in [8], with promising performance presented in recent GNN variants [40, 14, 9] Generally, GNNs could learn node embeddings by performing graph convolutions among adjacent notes, which can be formulated as: x(l+1