Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
Gephi is an open-source software for visualizing and analyzing large networks graphs. Gephi uses a 3D render engine to display graphs in real-time and speed up the exploration. Use Gephi to explore, analyse, spatialise, filter, cluterize, manipulate and export all types of graphs.
Visualization of graph data is incredibly challenging, particularly when it comes to extremely large, scale-free graphs and social networks. A few simple searches on the Web and you will find some mesmerizing and very cool images. Perhaps the most cited...
The site presents a hierarchical organization for Wikipedia articles with respect to their semantic similarity and provides search and navigation facilities over the hierarchy. The hierarchy is constructed as a recursive division of the English Wikipedia graph into dense subgraphs (graph communities) and can be considered as an extension to the Wikipedia category structure. Unlike Wikipedia categories that are primarily authored by humans, the community hierarchy is fully automatic, purely link-based and reflects the global link structure of Wikipedia.
J. Zhang, Y. Dong, Y. Wang, J. Tang, and M. Ding. Proceedings of the 28th International Joint Conference on Artificial Intelligence, page 4278–4284. AAAI Press, (Aug 10, 2019)
S. Wang, L. Hu, Y. Wang, X. He, Q. Sheng, M. Orgun, L. Cao, F. Ricci, and P. Yu. (2021)cite arxiv:2105.06339Comment: Accepted by IJCAI 2021 Survey Track, copyright is owned to IJCAI. The first systematic survey on graph learning based recommender systems. arXiv admin note: text overlap with arXiv:2004.11718.