{"arxiv_id":"2206.00135","incoming":{"papers":1,"sentences":1,"groups":[],"unlabelled_sentences":1},"outgoing":{"papers":2,"sentences":2,"groups":[{"kind":"background","label":"Cites as background","papers":2,"sentences":2,"quotes":[{"arxiv_id":"2106.07106","title":"Alignment and Comparison of Directed Networks via Transition Couplings of Random Walks","url":"/paper/2106.07106","section":"Introduction","sentence":"When the datasets are represented as graphs with edges annotating pairs of similar data points within each dataset, the integration problem can be addressed using various graph alignment methods [ref] .","intent_p":0.89,"contests_p":null},{"arxiv_id":"1802.03426","title":"UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction","url":"/paper/1802.03426","section":"Introduction","sentence":"Principal Component Analysis (PCA) [ref] is the most traditional linear technique used in dimensionality reduction but there are many popular non-linear techniques, such as Local Linear Embedding [ref] , Isomap [ref] , UMAP [ref] , and t-SNE [ref] .","intent_p":0.98,"contests_p":null}]}],"unlabelled_sentences":0},"claims":[],"thresholds":{"intent_p":0.8,"supports_p":0.95,"contests_p":0.7},"method":"Citing sentences come from each paper's LaTeX source; every \\cite marks the sentence and the works it cites. Pith's classifier typed each sentence. A label is shown at intent probability 0.8 or more (0.95 for support), a contest at 0.7 or more. Measured accuracy against two blind model families is at https://pith.science/accuracy. The quoted sentences are the citing authors' own.","held_up":null,"_links":{"html":"https://pith.science/paper/2206.00135"}}