We kindly invite scholars to submit abstracts to the session "Visualization of complex networks: graph embedding and factorization" organized for the 5th European Conference on Social Networks EUSN 2021 (Naples, September 7-10, 2021), which has been moved fully online.
Keywords: Graph embedding, Matrix factorization, Large network visualization
Session organizers: Maria Rosaria D’Esposito (University Suor Orsola Benincasa, Naples), Giuseppe Giordano (University of Salerno) and Giancarlo Ragozini (University of Naples Federico II).
Deadline Extension: Submissions due by June 6, 2021
Session description:
One of the main steps in complex network analysis consists in finding effective network data representation and visualization so to conduct efficiently advanced analytic tasks, such as pattern and community discovery, analysis and prediction. An efficient network visualization could help in isolating the most relevant feature of the network too. However, the traditional network representation in not anymore useful with large-scale network processing and analysis.
In literature, “To tackle the challenge, substantial effort has been committed to develop novel network embedding, i.e., learning low-dimensional vector representations for network nodes. In the network embedding space, the relationships among the nodes, which were originally repre sented by edges or other high-order topological measures in graphs, is captured by the distances between nodes in the vector space, and the topological and structural characteristics of a node are encoded into its embedding vector.” (Cui et al, 2019). Several approaches to network embedding approaches have been proposed, among the others the ones based on factorization methods, random walks, and deep learning. A taxonomy of graph embedding methods is in Cai et al. (2018) and in Goyal and Ferrara (2018).
In this session, contributions focused on theoretical, methodological and empirical aspects re lated to network visualization, graph embedding and network factorization with examples of representative algorithms in each category and analysis of their performance on various tasks are welcome. Applications, which show the power of network visualization are equally welcome, with an emphasis on social research, organization and media, among the others.