  The success of graph embeddings or node representation learning in a variety
of downstream tasks, such as node classification, link prediction, and
recommendation systems, has led to their popularity in recent years.
Representation learning algorithms aim to preserve local and global network
structure by identifying node neighborhood notions. However, many existing
algorithms generate embeddings that fail to properly preserve the network
structure, or lead to unstable representations due to random processes (e.g.,
random walks to generate context) and, thus, cannot generate to multi-graph
problems. In this paper, we propose RECS, a novel, stable graph embedding
algorithmic framework. RECS learns graph representations using connection
subgraphs by employing the analogy of graphs with electrical circuits. It
preserves both local and global connectivity patterns, and addresses the issue
of high-degree nodes. Further, it exploits the strength of weak ties and
meta-data that have been neglected by baselines. The experiments show that RECS
outperforms state-of-the-art algorithms by up to 36.85% on multi-label
classification problem. Further, in contrast to baselines, RECS, being
deterministic, is completely stable.
