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The area of graph embeddings is currently dominated by contrastive learningmethods, which demand formulation of an explicit objective function andsampling of positive and negative examples. This creates a conceptual andcomputational overhead. Simple, classic unsupervised approaches likeMultidimensional Scaling (MSD) or the Laplacian eigenmap skip the necessity oftedious objective optimization, directly exploiting data geometry.Unfortunately, their reliance on very costly operations such as matrixeigendecomposition make them unable to scale to large graphs that are common intoday's digital world. In this paper we present Cleora: an algorithm which getsthe best of two worlds, being both unsupervised and highly scalable. We showthat high quality embeddings can be produced without the popular step-wiselearning framework with example sampling. An intuitive learning objective ofour algorithm is that a node should be similar to its neighbors, withoutexplicitly pushing disconnected nodes apart. The objective is achieved byiterative weighted averaging of node neigbors' embeddings, followed bynormalization across dimensions. Thanks to the averaging operation thealgorithm makes rapid strides across the embedding space and usually reachesoptimal embeddings in just a few iterations. Cleora runs faster than otherstate-of-the-art CPU algorithms and produces embeddings of competitive qualityas measured on downstream tasks: link prediction and node classification. Weshow that Cleora learns a data abstraction that is similar to contrastivemethods, yet at much lower computational cost. We open-source Cleora under theMIT license allowing commercial use under https://github.com/Synerise/cleora.

Cleora: A Simple, Strong and Scalable Graph Embedding Scheme