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SDEGNN: Signed graph neural network for link sign prediction enhanced by signed distance encoding
The existing signed graph neural networks mainly focus on the design process of neighbor aggregation function, but ignore the correlation between...
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DynamiSE: dynamic signed network embedding for link prediction
In real-world scenarios, dynamic signed networks are ubiquitous where edges have positive and negative types and evolve over time. Graph neural...
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Signed directed attention network
Network embedding has facilitated lots of network analytical tasks by representing nodes as low-dimensional vectors. As an extension of convolutional...
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Learning Weight Signed Network Embedding with Graph Neural Networks
Network embedding aims to map nodes in a network to low-dimensional vector representations. Graph neural networks (GNNs) have received much attention...
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Polarity-based graph neural network for sign prediction in signed bipartite graphs
As a fundamental data structure, graphs are ubiquitous in various applications. Among all types of graphs, signed bipartite graphs contain complex...
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Enhancing signed social recommendation via extracting auxiliary textual information
Real-world applications are increasingly using personalized suggestions to guide users toward interesting content. Graphic Convolutional Neural...
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Local Spectral for Polarized Communities Search in Attributed Signed Network
Signed networks are graphs with edge annotations to indicate whether each interaction is friendly (positive edge) or antagonistic (negative edge)....
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Leaderless output sign consensus of heterogeneous multi-agent systems over switching signed graphs
This study examined the LOSC problem of heterogeneous MASs over signed switching digraphs. We remove the common requirement that a practical leader...
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Enhancing signed social recommendation via extracting consistent and inconsistent relations
Signed Social recommendations leverage signed social information(e.g., trust and distrust) to alleviate the cold-start and data sparsity problem....
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Spectral analysis for signed social networks
In complex real-world networks, the relation among vertices (people) changes over time. Even with millions of vertices, adding new vertices or...
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SSCAN:Structural Graph Clustering on Signed Networks
Structural graph clustering ( \(\textsf{SCAN}\) ) is a...
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Learning Signed Network Embedding via Muti-attention Mechanism
In consideration of most signed network embeddings only focusing on the low-order neighbors of the target node, they fail to make effective use of...
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Can we please everyone? Group recommendations in signed social networks
The ubiquity of social networks and the unprecedented growth in web data have generated an ample resource of information for researchers as well as...
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SGNN: A New Method for Learning Representations on Signed Networks
Graph Convolutional Neural Networks (GCNNs) have emerged as a powerful tool for processing graph-structured data and achieving outstanding...
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Generating Signed Permutations by Twisting Two-Sided Ribbons
We provide a simple approach to generating all \(2^n \cdot n!\)...
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SBiNE: Signed Bipartite Network Embedding
This work develops a representation learning method for signed bipartite networks. Recent years, embedding nodes of a given network into a low...
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Interactive planning of revisiting-free itinerary for signed-for delivery
The trend of online shop** has given rise to the growth of signed-for delivery services. Signed-for delivery is a reliable way of getting proof of...
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A promotive structural balance model based on reinforcement learning for signed social networks
To solve the structural balance problem in signed social networks, a number of structural balance models have been developed. However, these models...
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A differential machine learning approach for trust prediction in signed social networks
Understanding the dynamic nature of group formation and evolution in social networks is seen as a significant step to better describe how...
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A Regularized Convex Nonnegative Matrix Factorization Model for signed network analysis
Community detection and link prediction are two basic tasks of complex network system analysis, which are widely used in the detection of telecom...
