Node Embeddings In Social Network Analysis

We introduce a distributed representation of nodes, node embeddings, in social network analysis. We compute embeddings for nodes based on their attributes and links. These embeddings can support many social network applications - including analyses of community homogeneity, distance, and detection of community connectors (inter-community outliers, people who connect communities) - thanks to the convenient yet efficient computation provided by node embeddings for structural comparisons. Our experimental results include many interesting insights about the computer science literature network (DBLP). For example, in DBLP prior to 2013 the best way for research in Natural Language & Speech to gain impact toward "best-paper" recognition was to emphasize aspects related to Machine Learning & Pattern Recognition.