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

A Visual Framework for Graph and Text Analytics in Email Investigation

The aim of this work is to build a framework which can benefit from data analysis techniques to explore and mine important information stored in an email collection archive. The analysis of email data could be accomplished from different perspectives, we mainly focused our approach on two different aspects: social behaviors and the textual content of the emails body. We will present a review on the past techniques and features adopted to handle this type of analysis, and evaluate them in real tools. This background will motivate our choices and proposed approach, and help us build a final visual framework which can analyze and show social graph networks along with other data visualization elements that assist users in understanding and dynamically elaborating the email data uploaded. We will present the architecture and logical structure of the framework, and show the flexibility nature of the system for future integrations and improvements. The functional aspects of our approach will be tested using the ‘enron dataset’, and by applying real key actors involved in the ‘enron case’ scandal

VAST 2014, Challenge One: Event Analysis Within Big Data

News articles and email conversation data could be very useful in the analysis of developing and ongoing events, such as preventing a potential threat or possibly even locating a missing person. There is currently no “one-size-fits-all” solution to visualizing diverse forms of datasets and their sheer sizes are far too great to efficiently analyze by brute force methods. However, using principles of Visual Analytics, it is possible to take this information overload and transform it into a useful tool to help increase the efficiency of event analysis. A visualization system was developed for email conversation networks using web technologies. An interactive force diagram was constructed, allowing for an easy analysis of communication links between people. This force diagram was able to be filtered down to specific people or emails and with color coded nodes based on positions held in a company. A dynamic list of email headers was created that allowed for filtering based on specifically chosen people or by user defined importance. Lastly, a slide-out menu was implemented to allow for a side by side comparison between two selected people by displaying their employee records. The system created was used on a data set from the VAST 2014 mini challenge 1 and it allowed for the successful analysis of a fictional companies email network. Although this specific system was designed around the VAST 2014 data set, it could easily be modified to work with diverse email conversation network data to aid in various forms of analysis

Visualization of large temporal social network datasets

Social network datasets consist of what sociologists call ‘social structures’, accumulation of all communication channels that social actors share ideas and information between each other. Social network analysis reveals characteristics and properties of social networks by applying specific metrics. Although, size of a real-life social network dataset can reach millions of relations belong to millions of social actors with large temporal dimension, existing information visualization tools can represent at most several thousands of these actors. This thesis presents a conceptual design study focused on visualization of large temporal social network datasets with a novel visualization method. Proposed technique combines Ideal Gas Law (IGL) with Jacob Moreno’s theory of The Cannon of Creativity to layout social network datasets in 3D hyperbolic space and can render 50,000 social actors at interactive speed. A proof-of-the-concept program is developed around this technique allowing users to perform several analysis tasks on temporal social network datasets. Users can explore the network, control the amount of visual clutter, and identify communication anomalies in run time. Moreover, they can search a specific actor and visually follow her communication pattern. The effectiveness of proposed technique is presented with case and usability studies performed using generated and real-life datasets. In particular the Enron email dataset (323,073 emails, 19,898 email addresses over four years) and 20 Newsgroups (44,797 postings, 20 news groups and 5417 email addresses over one month) datasets are analyzed

Segue: Overviewing Evolution Patterns of Egocentric Networks by Interactive Construction of Spatial Layouts

Getting the overall picture of how a large number of ego-networks evolve is a common yet challenging task. Existing techniques often require analysts to inspect the evolution patterns of ego-networks one after another. In this study, we explore an approach that allows analysts to interactively create spatial layouts in which each dot is a dynamic ego-network. These spatial layouts provide overviews of the evolution patterns of ego-networks, thereby revealing different global patterns such as trends, clusters and outliers in evolution patterns. To let analysts interactively construct interpretable spatial layouts, we propose a data transformation pipeline, with which analysts can adjust the spatial layouts and convert dynamic egonetworks into event sequences to aid interpretations of the spatial positions. Based on this transformation pipeline, we developed Segue, a visual analysis system that supports thorough exploration of the evolution patterns of ego-networks. Through two usage scenarios, we demonstrate how analysts can gain insights into the overall evolution patterns of a large collection of ego-networks by interactively creating different spatial layouts.Comment: Published at IEEE Conference on Visual Analytics Science and Technology (IEEE VAST 2018

Design and Implementation of a Pressure Monitoring System Based on IoT for Water Supply Networks

Increasing the efficiency of water supply networks is essential in arid and semi-arid regions to ensure the supply of drinking water to the inhabitants. The cost of renovating these systems is high. However, customized management models can facilitate the maintenance and rehabilitation of hydraulic infrastructures by optimizing the use of resources. The implementation of current Internet of Things (IoT) monitoring systems allows decisions to be based on objective data. In water supply systems, IoT helps to monitor the key elements to improve system efficiency. To implement IoT in a water distribution system requires sensors that are suitable for measuring the main hydraulic variables, a communication system that is adaptable to the water service companies and a friendly system for data analysis and visualization. A smart pressure monitoring and alert system was developed using low-cost hardware and open-source software. An Arduino family microcontroller transfers pressure gauge signals using Sigfox communication, a low-power wide-area network (LPWAN). The IoT ThingSpeak platform is used for data analysis and visualization. Additionally, the system can send alarms via SMS/email in real time using the If This, Then That (IFTTT) web service when anomalous pressure data are detected. The pressure monitoring system was successfully implemented in a real water distribution network in Spain. It was able to detect both breakdowns and leaks in real time