  Generally, social network analysis has often focused on the topology of the
network without considering the characteristics of individuals involved in
them. Less attention is given to study the behavior of individuals, considering
they are the basic entity of a graph. Given a mobile social network graph, what
are good features to extract key information from the nodes? How many distinct
neighborhood patterns exist for ego nodes? What clues does such information
provide to study nodes over a long period of time?
  In this report, we develop an automated system in order to discover the
occurrences of prototypical ego-centric patterns from data. We aim to provide a
data-driven instrument to be used in behavioral sciences for graph
interpretations. We analyze social networks derived from real-world data
collected with smart-phones. We select 13 well-known network measures,
especially those concerned with ego graphs. We form eight feature subsets and
then assess their performance using unsupervised clustering techniques to
discover distinguishing ego-centric patterns. From clustering analysis, we
discover that eight distinct neighborhood patterns have emerged. This
categorization allows concise analysis of users' data as they change over time.
The results provide a fine-grained analysis for the contribution of different
feature sets to detect unique clustering patterns. Last, as a case study, two
datasets are studied over long periods to demonstrate the utility of this
method. The study shows the effectiveness of the proposed approach in
discovering important trends from data.
