• Corpus ID: 63087632

Discovering Archetypes to Interpret Evolution of Individual Behavior

  title={Discovering Archetypes to Interpret Evolution of Individual Behavior},
  author={Kanika Narang and Austin Chung and H. Sundaram and Snigdha Chaturvedi},
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… 

Utilizing the simple graph convolutional neural network as a model for simulating influence spread in networks

The methodological approach applies the simple graph convolutional neural network in a novel setting and demonstrates the ability for this model to aggregate feature information from nodes based on a parameter regulating the range of node influence which can simulate a process of exchanges in a manner which bypasses computationally intensive stochastic simulations.

Cognitive Archetypes, Acculturation and Social Coping Trends among Small Scale Muslim Merchants in A Public Market in the Philippines

  • Sherill S. Villaluz
  • Psychology
    International Journal of Academic Research in Business and Social Sciences
  • 2021
Muslim settlers in Laguna, Philippines has increased over the years, thus it is important to understand their dominant cognitive archetypes that may be credited for their vulnerability to acquire

Users roles identification on online crowdsourced Q&A platforms and encyclopedias: a survey

This work presents a survey of users’ social roles that have been identified on online discussion and Q&A platforms including Usenet newsgroups, Reddit, Stack Exchange, and MOOC forums, as well as on crowdsourced encyclopedias, such as Wikipedia, and Baidu Baike.



Career Transitions and Trajectories: A Case Study in Computing

This study analyzes several decades of post-PhD computing careers using a large new dataset rich with professional information, and proposes a versatile career network model, R 3, that captures temporal career dynamics.

The misleading narrative of the canonical faculty productivity trajectory

It is shown that the canonical narrative of “rapid rise, gradual decline” describes only about one-fifth of individual faculty, and the remaining four-fifths exhibit a rich diversity of productivity patterns, suggesting existing models and expectations for faculty productivity require revision.

Universal trajectories of scientific success

A large-scale study of the rise and fall of scientific success by analyzing the success of two major scientific entities—papers and authors—in Computer Science and Physics is presented and it is believed that this study will argue in favor of revising the existing metrics used for quantifying scientific success.

No country for old members: user lifecycle and linguistic change in online communities

This work proposes a framework for tracking linguistic change as it happens and for understanding how specific users react to these evolving norms and yields new theoretical insights into the evolution of linguistic norms and the complex interplay between community-level and individual-level linguistic change.

Clustering Multivariate Time Series Using Hidden Markov Models

An approach based on Hidden Markov Models (HMMs), where the first map each trajectory into an HMM, then define a suitable distance between HMMs and finally proceed to cluster the HMMs with a method based on a distance matrix is proposed.

Identifying user behavior in online social networks

A methodology for characterizing and identifying user behaviors in online social networks is proposed and it is shown that attributes that stem from the user social interactions, in contrast to attributes relative to each individual user, are good discriminators and allow the identification of relevant user behaviors.

Finding progression stages in time-evolving event sequences

A model-based method for discovering common progression stages in general event sequences is developed, in which each sequence belongs to a class, and sequences from a given class pass through a common set of stages, where each sequence evolves at its own rate.

Clustering hidden Markov models with variational HEM

A novel algorithm to cluster HMMs based on the hierarchical EM (HEM) algorithm, which effectively leverages large amounts of data when learning annotation models by using an efficient hierarchical estimation procedure, which reduces learning times and memory requirements, while improving model robustness through better regularization.

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

LIME is proposed, a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning aninterpretable model locally varound the prediction.

Characterizing user behavior in online social networks

A first of a kind analysis of user workloads in online social networks, based on detailed clickstream data collected over a 12-day period, shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities.