  Identifying similar mutual funds with respect to the underlying portfolios
has found many applications in financial services ranging from fund recommender
systems, competitors analysis, portfolio analytics, marketing and sales, etc.
The traditional methods are either qualitative, and hence prone to biases and
often not reproducible, or, are known not to capture all the nuances
(non-linearities) among the portfolios from the raw data. We propose a
radically new approach to identify similar funds based on the weighted
bipartite network representation of funds and their underlying assets data
using a sophisticated machine learning method called Node2Vec which learns an
embedded low-dimensional representation of the network. We call the embedding
\emph{Fund2Vec}. Ours is the first ever study of the weighted bipartite network
representation of the funds-assets network in its original form that identifies
structural similarity among portfolios as opposed to merely portfolio overlaps.
