Papers Graph Embedding
“Graph Embedding” 태그가 달린 논문 1,236편 · 필터 해제
Adaptive Homophily Clustering: Structure Homophily Graph Learning with Adaptive Filter for Hyperspectral Image
Hyperspectral image (HSI) clustering has been a fundamental but challenging task with zero training labels. Currently, some deep graph clustering methods have been successfully explored for HSI due to their outstanding p…
ClusteringGraph ClusteringGraph EmbeddingGraph Learning+1Efficient Relational Context Perception for Knowledge Graph Completion
Knowledge Graphs (KGs) provide a structured representation of knowledge but often suffer from challenges of incompleteness. To address this, link prediction or knowledge graph completion (KGC) aims to infer missing new f…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingKnowledge Graphs+3GAN-TAT: A Novel Framework Using Protein Interaction Networks in Druggable Gene Identification
Identifying druggable genes is essential for developing effective pharmaceuticals. With the availability of extensive, high-quality data, computational methods have become a significant asset. Protein Interaction Network…
Graph EmbeddingExploring Graph Mamba: A Comprehensive Survey on State-Space Models for Graph Learning
Graph Mamba, a powerful graph embedding technique, has emerged as a cornerstone in various domains, including bioinformatics, social networks, and recommendation systems. This survey represents the first comprehensive st…
Graph EmbeddingGraph LearningMambaRecommendation Systems+2Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
Inferencing Gene Regulatory Networks (GRNs) from gene expression data is a pivotal challenge in systems biology, and several innovative computational methods have been introduced. However, most of these studies have not …
Graph EmbeddingTwo Layer Walk: A Community-Aware Graph Embedding
Community structures are critical for understanding the mesoscopic organization of networks, bridging local and global patterns. While methods such as DeepWalk and node2vec capture local positional information through ra…
Evolutionary AlgorithmsGraph EmbeddingLink PredictionA Comparative Study on Dynamic Graph Embedding based on Mamba and Transformers
Dynamic graph embedding has emerged as an important technique for modeling complex time-evolving networks across diverse domains. While transformer-based models have shown promise in capturing long-range dependencies in …
Computational EfficiencyDynamic graph embeddingGraph EmbeddingGraph Representation Learning+4A Survey on Knowledge Graph Structure and Knowledge Graph Embeddings
Knowledge Graphs (KGs) and their machine learning counterpart, Knowledge Graph Embedding Models (KGEMs), have seen ever-increasing use in a wide variety of academic and applied settings. In particular, KGEMs are typicall…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+2Predictive Query-based Pipeline for Graph Data
Graphs face challenges when dealing with massive datasets. They are essential tools for modeling interconnected data and often become computationally expensive. Graph embedding techniques, on the other hand, provide an e…
Graph EmbeddingFrom communities to interpretable network and word embedding: an unified approach
Modelling information from complex systems such as humans social interaction or words co-occurrences in our languages can help to understand how these systems are organized and function. Such systems can be modelled by n…
Graph EmbeddingGraph MatchingLink PredictionWord EmbeddingsNon-Progressive Influence Maximization in Dynamic Social Networks
The influence maximization (IM) problem involves identifying a set of key individuals in a social network who can maximize the spread of influence through their network connections. With the advent of geometric deep lear…
Deep Reinforcement LearningDynamic graph embeddingGraph Embeddingreinforcement-learning+1Prediction of Occluded Pedestrians in Road Scenes using Human-like Reasoning: Insights from the OccluRoads Dataset
Pedestrian detection is a critical task in autonomous driving, aimed at enhancing safety and reducing risks on the road. Over recent years, significant advancements have been made in improving detection performance. Howe…
Autonomous DrivingBayesian InferenceGraph EmbeddingKnowledge Graph Embedding+1Distance-Adaptive Quaternion Knowledge Graph Embedding with Bidirectional Rotation
Quaternion contains one real part and three imaginary parts, which provided a more expressive hypercomplex space for learning knowledge graph. Existing quaternion embedding models measure the plausibility of a triplet ei…
Graph EmbeddingKnowledge Graph CompletionKnowledge Graph EmbeddingLink Prediction+2Interpreting single-cell and spatial omics data using deep neural network training dynamics
Single-cell and spatial omics datasets can be organized and interpreted by annotating single cells to distinct types, states, locations or phenotypes. However, cell annotations are inherently ambiguous, as discrete label…
DiversityGraph EmbeddingLearn to Unlearn: Meta-Learning-Based Knowledge Graph Embedding Unlearning
Knowledge graph (KG) embedding methods map entities and relations into continuous vector spaces, improving performance in tasks like link prediction and question answering. With rising privacy concerns, machine unlearnin…
Graph EmbeddingKnowledge Graph EmbeddingLink PredictionMachine Unlearning+2Local Intrinsic Dimensionality for Dynamic Graph Embeddings
The notion of local intrinsic dimensionality (LID) has important theoretical implications and practical applications in the fields of data mining and machine learning. Recent research efforts indicate that LID measures d…
Dynamic graph embeddingGraph EmbeddingDomain and Range Aware Synthetic Negatives Generation for Knowledge Graph Embedding Models
Knowledge Graph Embedding models, representing entities and edges in a low-dimensional space, have been extremely successful at solving tasks related to completing and exploring Knowledge Graphs (KGs). One of the key asp…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge GraphsVisualization of Knowledge Graphs with Embeddings: an Essay on Recent Trends and Methods
In this essay we discuss the recent trends in visual analysis and exploration of Knowledge Graphs, particularly in conjunction with Knowledge Graph Embedding techniques. We present an overview of the current state of vis…
Graph EmbeddingKnowledge Graph EmbeddingKnowledge Graph EmbeddingsKnowledge Graphs+1Scalable Deep Metric Learning on Attributed Graphs
We consider the problem of constructing embeddings of large attributed graphs and supporting multiple downstream learning tasks. We develop a graph embedding method, which is based on extending deep metric and unbiased c…
Contrastive LearningGraph EmbeddingLink PredictionMetric Learning+2Multi-Hyperbolic Space-based Heterogeneous Graph Attention Network
To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, w…
Graph AttentionGraph Embedding