Avoiding Biases due to Similarity Assumptions in Node Embeddings

Abstract

Node embeddings are vectors, one per node, that capture a graph's structure. The basic structure is the adjacency matrix of the graph. Recent methods also make assumptions about the similarity of unlinked nodes. However, such assumptions can lead to unintentional but systematic biases against groups of nodes. Calculating similarities between far-off nodes is also difficult under privacy constraints and in dynamic graphs. Our proposed embedding, called NEWS, makes no similarity assumptions, avoiding potential risks to privacy and fairness. NEWS is parameter-free, enables fast link prediction, and has linear complexity. These gains from avoiding assumptions do not significantly affect accuracy, as we show via comparisons against several existing methods on 21 real-world networks. Code is available at https://github.com/deepayan12/news.