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

Discovering Archetypes to Interpret Evolution of Individual Behavior