Discovering Archetypes to Interpret Evolution of Individual Behavior. (arXiv:1902.05567v1 [cs.SI])

In this paper, we aim to discover archetypical patterns of individual evolution in large social networks. In our work, an archetype comprises of \emph{progressive stages} of distinct behavior. We introduce a novel Gaussian Hidden Markov Model (G-HMM) Cluster to identify archetypes of evolutionary patterns. G-HMMs allow for: near limitless behavioral variation; imposing constraints on how individuals can evolve; different evolutionary rates; and are parsimonious. Our experiments with Academic and StackExchange dataset discover insightful archetypes. We identify four archetypes for researchers : \emph{Steady}, \emph{Diverse}, \emph{Evolving} and \emph{Diffuse}. We observe clear differences in the evolution of male and female researchers within the same archetype. Specifically, women and men differ within an archetype (e.g. \emph{Diverse}) in how they start, how they transition and the time spent in mid-career. We also found that the differences in grant income are better explained by the differences in archetype than by differences in gender. For StackOverflow, discovered archetypes could be labeled as: \emph{Experts}, \emph{Seekers}, \emph{Enthusiasts} and \emph{Facilitators}. We have strong quantitative results with competing baselines for activity prediction and perplexity. For future session prediction, the proposed G-HMM cluster model improves by an average of 32\% for different Stack Exchanges and 24\% for Academic dataset. Our model also exhibits lower perplexity than the baselines.

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