Topic-Partitioned Multinetwork Embeddings

Abstract We introduce a new Bayesian admixture model intended for exploratory analysis of communication networks-specifically, the discovery and visualization of topic-specific subnetworks in email data sets. Our model produces principled visualizations of email networks, i.e., visualizations that have precise mathematical interpretations in terms of our model and its relationship to the observed data. We validate our modeling assumptions by demonstrating that our model achieves better link prediction performance than three state-of-the-art network models and exhibits topic coherence comparable to that of latent Dirichlet allocation. We showcase our model's ability to discover and visualize topic-specific communication patterns using a new email data set: the New Hanover County email network. We provide an extensive analysis of these communication patterns, leading us to recommend our model for any exploratory analysis of email networks or other similarly-structured communication data. Finally, we advocate for principled visualization as a primary objective in the development of new network models

iBCM: Interactive Bayesian Case Model Empowering Humans via Intuitive Interaction

Clustering methods optimize the partitioning of data points with respect to an internal metric, such as likelihood, in order to approximate the goodness of clustering. However, this internal metric does not necessarily translate into effective clustering from the user's perspective. This work presents the interactive Bayesian Case Model (iBCM), a model that opens a communication channel between the clustering model and the user. Users can provide direct input to iBCM in order to achieve effective clustering results, and iBCM optimizes the clustering by creating a balance between what the data indicate and what makes the most sense to the user. This model provides feedback for users and does not assume any prior knowledge of machine learning on their part. We provide quantitative evidence that users are able to obtain more satisfactory clustering results through iBCM than without an interactive model. We also demonstrate the use of this method in a real-world setting where computer language class teachers utilize iBCM to cluster students' coding assignments for grading

Topic-Partitioned Multinetwork Embeddings

We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns. Our model is based on a novel extension of the latent space network model to the mixed-membership framework, and it uses latent Dirichlet allocation to model the text attributes of our data. To perform inference in this model, we use an approximate stochastic expectation-maximization algorithm. We validate the appropriateness of our model using a simulation study and a prediction task, and demonstrate its capabilities by investigating the communication patterns within a new government email dataset, the New Hanover County email corpus

Replication data for: Topic-partitioned multinetwork embeddings

We introduce a joint model of network content and context designed for exploratory analysis of email networks via visualization of topic-specific communication patterns. Our model is an admixture model for text and network attributes which uses multinomial distributions over words as mixture components for explaining text and latent Euclidean positions of actors as mixture components for explaining network attributes. We validate the appropriateness of our model by achieving state-of-the-art performance on a link prediction task and by achieving semantic coherence equivalent to that of latent Dirichlet allocation. We demonstrate the capability of our model for descriptive, explanatory, and exploratory analysis by investigating the inferred topic-specific communication patterns of a new government email dataset, the New Hanover County email corpus. This work was supported in part by the Center for Intelligent Information Retrieval and in part by the NSF GRFP under grant #1122374. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect those of the sponsors