Type classes enable a powerful form of ad-hoc polymorphism which provide solutions to many programming design problems. Inspired by this, Scala programmers have striven to emulate them in the design of libraries like Scalaz and Cats.The natural encoding of type classes combines subtyping and implicits, both central features of Scala. However, this encoding has limitations. If the type class hierarchy branches, seemingly valid programs can hit implicit resolution failures. These failures must then be solved by explicitly passing the implicit arguments which is cumbersome and negates the advantages of type classes.In this paper we describe instances of this problem and show that they are not merely theoretical but often arise in practice. We also discuss and compare the space of solutions to this problem in Scala today and in the future.

Analysis of large graphs is critical to the ongoing growth of search engines and social networks. One class of queries centers around node affinity, often quantified by random-walk distances between node pairs, including hitting time, commute time, and personalized PageRank (PPR). Despite the potential of these "metrics," they are rarely, if ever, used in practice, largely due to extremely high computational costs.In this paper, we investigate methods to scalably and efficiently compute random-walk distances, by "embedding" graphs and distances into points and distances in geometric coordinate spaces. We show that while existing graph coordinate systems (GCS) can accurately estimate shortest path distances, they produce significant errors when embedding random-walk distances. Based on our observations, we propose a new graph embedding system that explicitly accounts for per-node graph properties that affect random walk. Extensive experiments on a range of graphs show that our new approach can accurately estimate both symmetric and asymmetric random-walk distances. Once a graph is embedded, our system can answer queries between any two nodes in 8 microseconds, orders of magnitude faster than existing methods. Finally, we show that our system produces estimates that can replace ground truth in applications with minimal impact on application output.