In this paper, we propose Signed Graph Attention Networks (SiGATs), generalizing GAT to signed networks. SiGAT incorporates graph motifs into GAT to capture two well-known theories in signed network research, i.e., balance theory and status theory. In SiGAT, motifs offer us the flexible ...
PPI predictions can be realized by predicting the links of the signed network; therefore, the use of gated graph attention for signed networks (SN-GGAT) is proposed herein. First, the concept of graph attention network (GAT) is applied to signed networks, in which "attention" represents the...
achieves the highest performance across various metrics. Among the three ablated modules, the SignGCN module contributes the most. In fact, even when using the original GCN, which is not specifically designed for signed graph neural networks, the performance in the prediction task of...
The nodes of the graph may show homogeneity, i.e., similarity, or heterogeneity, i.e., dissimilarity, in the network. The similarity or dissimilarity of the nodes is the basis of link inference in evolving social networks [3]. Large scale availability of user activity data [4] reveals ...
(and unsurprisingly), the properties of these networks are often dissimilar. That is, how we choose to weight a network’s edges can change its graph-theoretic profile and impact how we might interpret its function. Although we remain agnostic as to which weighting scheme is superior, we note...
Table 1 List of directed signed networks datasets used in our study. Full size table Notations and basic definitions We denote a directed signed graph as\(G = (V,E,\sigma )\), whereVandEare sets of vertices and directed edges, respectively, and\(\sigma\)is the sign function that maps ...
In Section 2, we give some preliminaries containing some basic knowledge of graph theory, some necessary definitions and lemmas. In Section 3, efficient criteria are established for bipartite consensus of nonlinear coupling networks and linear coupling networks with communication delays. In addition, ...
An unclosed structures-preserving embedding model for signed networks ? 2024 Elsevier B.V.Signed network embedding has sparked substantial attention since it learns a low-dimensional representation of signed networks. However... L Du,H Jiang,DLH Ye - 《Neurocomputing》 被引量: 0发表: 2024年 t-...
Conference paper Epidemic graph convolutional network
Community detection problem in networks has received a great deal of attention during the past decade. Most of community detection algorithms took into account only positive links, but they are not suitable for signed networks. In our work, we propose an algorithm based on random walks for commun...