Papers Link Sign Prediction
“Link Sign Prediction” 태그가 달린 논문 23편 · 필터 해제
A Scalable Inter-edge Correlation Modeling in CopulaGNN for Link Sign Prediction
Link sign prediction on a signed graph is a task to determine whether the relationship represented by an edge is positive or negative. Since the presence of negative edges violates the graph homophily assumption that adj…
Link Sign PredictionGegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs
Given a signed bipartite graph (SBG) G with two disjoint node sets U and V, the goal of link sign prediction is to predict the signs of potential links connecting U and V based on known positive and negative edges in G. …
Link Sign PredictionKAN KAN Buff Signed Graph Neural Networks?
Graph Representation Learning aims to create effective embeddings for nodes and edges that encapsulate their features and relationships. Graph Neural Networks (GNNs) leverage neural networks to model complex graph struct…
Community DetectionGraph Representation LearningLink Sign PredictionRepresentation LearningEffective and Lightweight Representation Learning for Link Sign Prediction in Signed Bipartite Graphs
How can we effectively and efficiently learn node representations in signed bipartite graphs? A signed bipartite graph is a graph consisting of two nodes sets where nodes of different types are positively or negative con…
Link Sign PredictionRepresentation LearningGraph Spring Neural ODEs for Link Sign Prediction
Signed graphs allow for encoding positive and negative relations between nodes and are used to model various online activities. Node representation learning for signed graphs is a well-studied task with important applica…
Graph AttentionLink Sign PredictionNumerical IntegrationPrediction+1CSGDN: Contrastive Signed Graph Diffusion Network for Predicting Crop Gene-phenotype Associations
Positive and negative association prediction between gene and phenotype helps to illustrate the underlying mechanism of complex traits in organisms. The transcription and regulation activity of specific genes will be adj…
Contrastive LearningLink PredictionLink Sign PredictionDropEdge not Foolproof: Effective Augmentation Method for Signed Graph Neural Networks
The paper discusses signed graphs, which model friendly or antagonistic relationships using edges marked with positive or negative signs, focusing on the task of link sign prediction. While Signed Graph Neural Networks (…
Data AugmentationLink Sign PredictionSelf-Explainable Graph Transformer for Link Sign Prediction
Signed Graph Neural Networks (SGNNs) have been shown to be effective in analyzing complex patterns in real-world situations where positive and negative links coexist. However, SGNN models suffer from poor explainability,…
Decision MakingLink Sign PredictionPredictionAdversarial Robustness of Link Sign Prediction in Signed Graphs
Signed graphs serve as fundamental data structures for representing positive and negative relationships in social networks, with signed graph neural networks (SGNNs) emerging as the primary tool for their analysis. Our i…
Adversarial RobustnessContrastive LearningLink Sign PredictionEnhancing Signed Graph Neural Networks through Curriculum-Based Training
Signed graphs are powerful models for representing complex relations with both positive and negative connections. Recently, Signed Graph Neural Networks (SGNNs) have emerged as potent tools for analyzing such graphs. To …
Link Sign PredictionRepresentation LearningSGA: A Graph Augmentation Method for Signed Graph Neural Networks
Signed Graph Neural Networks (SGNNs) are vital for analyzing complex patterns in real-world signed graphs containing positive and negative links. However, three key challenges hinder current SGNN-based signed graph repre…
Data AugmentationGraph Representation LearningLink Sign PredictionRepresentation LearningA Signed Subgraph Encoding Approach via Linear Optimization for Link Sign Prediction
In this paper, we consider the problem of inferring the sign of a link based on limited sign data in signed networks. Regarding this link sign prediction problem, SDGNN (Signed Directed Graph Neural Networks) provides th…
Link Sign PredictionPredictionA Graph Convolution for Signed Directed Graphs
A signed directed graph is a graph with sign and direction information on the edges. Even though signed directed graphs are more informative than unsigned or undirected graphs, they are more complicated to analyze and ha…
Link Sign PredictionSigned Graph Neural Networks: A Frequency Perspective
Graph convolutional networks (GCNs) and its variants are designed for unsigned graphs containing only positive links. Many existing GCNs have been derived from the spectral domain analysis of signals lying over (unsigned…
Link Sign PredictionNode ClassificationRepresentation Learning in Continuous-Time Dynamic Signed Networks
Signed networks allow us to model conflicting relationships and interactions, such as friend/enemy and support/oppose. These signed interactions happen in real-time. Modeling such dynamics of signed networks is crucial t…
Graph Neural NetworkLink Sign PredictionRepresentation LearningSSSNET: Semi-Supervised Signed Network Clustering
Node embeddings are a powerful tool in the analysis of networks; yet, their full potential for the important task of node clustering has not been fully exploited. In particular, most state-of-the-art methods generating n…
Cloud RemovalClusteringGraph Neural NetworkLink Sign Prediction+2wsGAT: Weighted and Signed Graph Attention Networks for Link Prediction
Graph Neural Networks (GNNs) have been widely used to learn representations on graphs and tackle many real-world problems from a wide range of domains. In this paper we propose wsGAT, an extension of the Graph Attention …
Graph AttentionLink PredictionLink Sign PredictionSigned Bipartite Graph Neural Networks
Signed networks are such social networks having both positive and negative links. A lot of theories and algorithms have been developed to model such networks (e.g., balance theory). However, previous work mainly focuses …
Link Sign PredictionNetwork EmbeddingSigned Graph Diffusion Network
Given a signed social graph, how can we learn appropriate node representations to infer the signs of missing edges? Signed social graphs have received considerable attention to model trust relationships. Learning node re…
Graph Neural NetworkLink Sign PredictionNetwork EmbeddingPrediction+1Decoupled Variational Embedding for Signed Directed Networks
Node representation learning for signed directed networks has received considerable attention in many real-world applications such as link sign prediction, node classification and node recommendation. The challenge lies …
Link Sign PredictionNode ClassificationRepresentation Learning