Semi-supervised node classificationGraph convolutional networkLadder neural networksNetwork embeddingGraph convolutional networks (GCNs) and network embedding are the two main categories of popular methods for Semi-Supervised Node Classification (SSNC) in social network. However, the former is commonly ...
When dealing with large graphs, such as those that arise in the context of online social networks, a subset of nodes may be labeled. These labels can indicate demographic values, interest, beliefs or other characteristics of the nodes (users). A core problem is to use this information to ex...
The information propagation mechanism in graph-structured networks such as social networks is the foundation of network security. The graph convolutional network (GCN) is a powerful approach for semisupervised node classification on graph-structure data. The vertex features which pass through the graph ...
Guest Editorial: Deep Neural Networks for Graphs: Theory, Models, Algorithms, and Applications IEEE Trans. Neural Netw. Learn. Syst. (2024) K. Wang et al. Minority-Weighted Graph Neural Network for Imbalanced Node Classification in Social Networks of Internet of People IEEE Internet Things J. ...
GAT algorithm takes node labels as supervised information and can achieves high node classification accuracy. However in real world, node labels are rare. There are only some citation networks and protein interaction networks that have supervised node label information. The scarcity of node labels ...
Xu, Balanced neighbor exploration for semi-supervised node classification on imbalanced graph data, Inf. Sci., vol. 631, pp. 31–44, 2023. Crossref Google Scholar [2] P. Chunaev, Community detection in node-attributed social networks: A survey, Comput. Sci. Rev., vol. 37, p. 100286...
Graph embedding is an advantageous technique for reducing computational costs and effectively using graph information in machine learning tasks like classification, clustering, and link prediction. As a result, it has become a key method in various research areas. However, different embedding methods may...
Er1kkkaaa / Multiclass-Node-Classification-in-Twitch-Social-Networks-A-supervised-task-utilizing-a-custom-DNN- Star 0 Code Issues Pull requests Undergraduate Thesis twitch neo4j dnn social-network-analysis multiclass-classification cypher-query-language node2vec custom-dnn Updated Jul 17, 2021...
Welling M, Kipf TN (2016) Semi-supervised classification with graph convolutional networks. In: J. International Conference on Learning Representations (ICLR 2017) Wu L, Lin H, Gao Z et al (2021) Graphmixup: Improving class-imbalanced node classification on graphs by self-supervised context predi...
node classification or link prediction. To do this, each MTCN conducts multi-task learning (i.e., node classification and link prediction) by sharing the parameters in the deep neural networks to separately generate the representations for node classification and link prediction. In addition, each ...