Results 11 to 20 of about 287,840 (285)
Topological feature generation for link prediction in biological networks [PDF]
PeerJ, 2023 Graph or network embedding is a powerful method for extracting missing or potential information from interactions between nodes in biological networks.
Mustafa Temiz +3 more
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Joint Embedding of Graphs [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021 Feature extraction and dimension reduction for networks is critical in a wide variety of domains. Efficiently and accurately learning features for multiple graphs has important applications in statistical inference on graphs. We propose a method to jointly embed multiple undirected graphs.
Shangsi Wang +3 more
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GAE-Based Document Embedding Method for Clustering
IEEE Access, 2022 Document embedding methods for clustering using deep neural networks have been proposed recently. However, the existing deep neural network-based document embedding methods for clustering have a problem of either generating document embeddings dependent ...
Sungwon Jung, Sangmin Ka
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Numerical Linear Algebra with Applications, 2020 AbstractThe goal of the present paper is the design of embeddings of a general sparse graph into a set of points in for appropriated ≥ 2. The embeddings that we are looking at aim to keep vertices that are grouped in communities together and keep the rest apart. To achieve this property, we utilize coarsening that respects possible community structures
Benjamin Quiring, Panayot S. Vassilevski
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JONNEE: Joint Network Nodes and Edges Embedding
IEEE Access, 2021 Recently, graph embedding models significantly improved the quality of graph machine learning tasks, such as node classification and link prediction. In this work, we propose a model called JONNEE (JOint Network Nodes and Edges Embedding), which learns ...
Ilya Makarov +2 more
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Structure-Augmented Text Representation Learning for Efficient Knowledge Graph Completion [PDF]
, 2021 Human-curated knowledge graphs provide critical supportive information to various natural language processing tasks, but these graphs are usually incomplete, urging auto-completion of them.
Bo Wang +5 more
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WGEVIA: A Graph Level Embedding Method for Microcircuit Data
Frontiers in Computational Neuroscience, 2021 Functional microcircuits are useful for studying interactions among neural dynamics of neighboring neurons during cognition and emotion. A functional microcircuit is a group of neurons that are spatially close, and that exhibit synchronized neural ...
Xiaomin Wu +4 more
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Attributed Graph Embedding Based on Attention with Cluster
Mathematics, 2022 Graph embedding is of great significance for the research and analysis of graphs. Graph embedding aims to map nodes in the network to low-dimensional vectors while preserving information in the original graph of nodes.
Bin Wang +3 more
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Knowledge Graph Embeddings [PDF]
, 2012 With the growing popularity of multi-relational data on the Web, knowledge graphs (KGs) have become a key data source in various application domains, such as Web search, question answering, and natural language understanding. In a typical KG such as Freebase (Bollacker et al.
Rosso, Paolo +2 more
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Graph Representation Learning and Its Applications: A Survey
Sensors, 2023 Graphs are data structures that effectively represent relational data in the real world. Graph representation learning is a significant task since it could facilitate various downstream tasks, such as node classification, link prediction, etc.
Van Thuy Hoang +5 more
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