GRAPH algorithmsENCODINGThe existing signed graph neural networks mainly focus on the design process of neighbor aggregation function, but ignore the correlation between nodes, which leads to the decline of the representation ability of neural networks. In order to solve the above problems, a SDEGNN...
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...
Message-passing Graph Neural Networks (GNNs), which collect information from adjacent nodes achieve dismal performance on heterophilic graphs. Various schemes have been proposed to solve this problem, and propagating signed information on heterophilic edges has gained great attention. Recently, some works...
machine-learningpytorchsigned-networksgraph-neural-networksmagnetic-laplaciandirected-networkssigned-directed-networks UpdatedFeb 1, 2023 Python Python Implementation for Signed Random Walk with Restart (SRWR) pythonrandom-walk-with-restartsigned-networkssigned-random-walk-with-restartpersonalized-ranking ...
signed directed graphsentry valuesentry signscycle valuesStable matrices are related to the values of their entries by the concept of an all negative quasi... GM Lady - 《Annals of Mathematics & Artificial Intelligence》 被引量: 6发表: 1996年 The $\\mathbb{F}_2$-Rank and Size of Graphs ...
Supervised random walks: predicting and recommending links in social networksView more references Cited by (5) Large-scale online multi-view graph neural network and applications 2021, Future Generation Computer Systems Citation Excerpt : Due to its strong expressiveness and superior performance,GNN was...
A Rule-Based Approach to Fault Diagnosis Using the Signed Directed Graph Fault diagnosis is the problem of determining the root causes of process upsets. This paper presents a very efficient method of identifying the possible ca... MA Kramer,BL Palowitch - 《Aiche Journal》 被引量: 616发表:...
Signed network embedding methods allow for a low-dimensional representation of nodes and primarily focus on partitioning the graph into clusters, hence losing information on continuous node attributes. Here, we introduce a spectral embedding algorithm fo
Signed networks refer to a class of network systems including not only cooperative but also antagonistic interactions among nodes. Due to the existence of antagonistic interactions in signed networks, the agreement of nodes may not be established, instead of which disagreement behaviors generally emerge...
Due to the fact much of today's data can be represented as graphs, there has been a demand for generalizing neural network models for graph data. One recent direction that has shown fruitful results, and therefore growing interest, is the usage of graph convolutional neural networks (GCNs). ...