![]() ![]() In other words: No paywalls, no publishing costs, no hidden costs, no copyright headaches - just your and others’ research work. It adds direct value to the researcher's work.Content can travel between applications (i.e., interoperable).Content is 100% free-to-access and free-to-play.We based the design and development of Hypergraph on a few core principles (in no particular order): Researchers already suffer from budget cuts, scarce positions, and temporary contracts - and we don't want you to have to make another decision that could hurt your career chances. At the end of a research journey, you can easily and accurately retrace who did what, and collect the steps that go into an article. Our effort to reset research publishing isn't zero-sum - sometimes you wish or need to publish a regular article. Ten minutes, and who knows what you could get done. You can go from a clean install to sharing your first research step in under a minute if you got the files ready. When you share your latest research step, your peers can immediately see it, link next steps to it, and help you improve it. Plus, you can share files such as Jupyter notebooks, scripts, data files, videos, audio files, text, and any other open file format. This means that you can share all kinds of steps in your research-the theory, predictions, transcripts, materials, code, data, results (and more). In Hypergraph, you only have to indicate what step you're taking, link it to the step(s) it follows from, add relevant files, and indicate what file you want people to see first. Traditionally, articles are often written in hindsight, causing selective publication, p-hacking, and many other issues. Hypergraph helps researchers reset research publishing, by publicly documenting research step by step, before the issues of after-the-fact articles even begin. The Cora dataset is a citation network dataset for vertex classification task.The beta release of Hypergraph is here ? If you want to dive in immediately, download Hypergraph (Beta) for Windows, macOS, or Linux. Return the key of the dataset with un-preprocessed format. Item_name ( str) – The name of the item in the dataset. Return whether the item_name of the dataset needs to be loaded. If extra key bk_url is provided, it will be used to download theįile from the backup url. In the list is a dict with at lease two keys: filename and md5. Parametersįiles ( List]) – The files to download, each element fetch_files ( files ) ĭownload and check the files if they are not exist. Feature Visualization in Euclidean Space.Mathematical Principles of Hyperbolic Space.Rotating Visualization of Features in Poincare Space.Rotating Visualization of Features in Euclidean Space.Visualization of Features in Poincare Space.Visualization of Features in Euclidean Space.Message Propagation from Vertex Set to Vertex Set with different Edge Weights in Two Stages.Message Propagation from Vertex Set to Vertex Set.Message Propagation from Hyperedge to Vertex with different Edge Weights.Message Propagation from Hyperedge to Vertex.Message Propagation from Vertex to Hyperedge with different Edge Weights.Message Propagation from Vertex to Hyperedge.Message Propagation from Vertices in Set \(V\) to Vertices in Set \(U\) with different Edge Weights.Message Propagation from Vertices in Set \(V\) to Vertices in Set \(U\).Message Propagation from Vertices in Set \(U\) to Vertices in Set \(V\) with different Edge Weights.Message Propagation from Vertices in Set \(U\) to Vertices in Set \(V\).Message Propagation from Target Vertex to Source Vertex with different Edge Weights.Message Propagation from Target Vertex to Source Vertex.Message Propagation from Source Vertex to Target Vertex with different Edge Weights.Message Propagation from Source Vertex to Target Vertex.Message Propagation from Vertex to Vertex with different Edge Weights.Message Propagation from Vertex to Vertex.Smoothing with Left (random-walk) Normalized Laplacian.Smoothing with Symmetrically Normalized Laplacian.Fuse Features Learned from different Structures.Fuse Features Learned from the Spectral and Spatial Domain.What Can be Done with the Two Operations?. ![]()
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