Neo4j data science algorithms6 Flashcards
Graph Signal Denoising
Techniques used to remove noise from graph signals, enhancing the quality of the data for further analysis. For example, denoising is applied in sensor networks to improve the accuracy of measurements.
Graph Topological Sorting
An ordering of the nodes in a directed graph such that for every directed edge (u, v), node u appears before v in the ordering, used in scheduling and task prioritization. For example, topological sorting is used to resolve dependencies in build systems.
Graph Attention Mechanisms
Techniques that focus on the most relevant parts of a graph when processing it, often used in Graph Neural Networks to enhance performance by prioritizing certain nodes or edges. For example, attention mechanisms help improve the accuracy of node classification tasks.
Graph Sparsity
A measure of how many edges are present in a graph relative to the number of possible edges, with sparse graphs having relatively few edges. For example, sparsity is leveraged in large-scale graph computations to improve efficiency.
Graph Submodularity
A property of set functions that exhibits diminishing returns, used in optimization problems on graphs, such as influence maximization. For example, submodular functions are used in sensor placement to maximize coverage in a network.
Graph-Based Active Learning
A machine learning approach that uses the structure of a graph to select the most informative data points for labeling, reducing the amount of labeled data needed. For example, active learning can be used to efficiently classify nodes in a large network with minimal labeled data.
Meta-Path-Based Similarity
A measure of similarity between nodes in heterogeneous graphs based on predefined paths that capture semantic relationships. For example, meta-path-based similarity is used in bibliographic networks to find similar authors based on co-authorship and citation paths.
Graph Diffusion
The process of spreading information, influence, or phenomena through a graph, often modeled with diffusion algorithms. For example, graph diffusion is used to simulate the spread of information or diseases in social networks.
Graph Kernel Methods
Techniques that compute the similarity between graphs for tasks like classification and clustering, using kernels to capture graph structures. For example, graph kernels are applied in bioinformatics to compare molecular structures.
Graph Generative Adversarial Networks (GraphGAN)
A type of generative model that uses adversarial training to generate realistic graph structures, used for tasks like link prediction and graph generation. For example, GraphGAN can generate synthetic social networks for simulation and testing.
Graph Capsule Networks
An extension of capsule networks to graphs, aiming to capture hierarchical relationships and spatial information in graph data. For example, graph capsule networks can be used for node classification tasks in complex networks.
Graph-Based Semi-Supervised Learning
Learning approaches that utilize both labeled and unlabeled data in a graph to improve model performance, often leveraging graph structures to propagate labels. For example, semi-supervised learning is used to enhance classification accuracy in partially labeled graphs.
Graph Neural Network Pretraining
The process of training a Graph Neural Network on a large dataset to learn generalizable features, which can be fine-tuned on specific tasks. For example, pretraining GNNs can improve performance in transfer learning applications.
Graph Matching Kernels
Techniques for measuring similarity between graphs by decomposing them into subgraphs and comparing these components, used in tasks like graph classification. For example, graph matching kernels are applied in cheminformatics to compare chemical compounds.
Graph Subsampling
The process of selecting a subset of nodes and edges from a larger graph for analysis, used to reduce computational complexity or focus on specific regions of interest. For example, subsampling is used in large-scale networks to enable efficient data processing.