PERSEUS-HUB: Interactive and Collective Exploration of Large-Scale Graphs
Journal Title: Informatics - Year 2017, Vol 4, Issue 3
Abstract
Graphs emerge naturally in many domains, such as social science, neuroscience, transportation engineering, and more. In many cases, such graphs have millions or billions of nodes and edges, and their sizes increase daily at a fast pace. How can researchers from various domains explore large graphs interactively and efficiently to find out what is ‘important’? How can multiple researchers explore a new graph dataset collectively and “help” each other with their findings? In this article, we present PERSEUS-HUB, a large-scale graph mining tool that computes a set of graph properties in a distributed manner, performs ensemble, multi-view anomaly detection to highlight regions that are worth investigating, and provides users with uncluttered visualization and easy interaction with complex graph statistics. PERSEUS-HUB uses a Spark cluster to calculate various statistics of large-scale graphs efficiently, and aggregates the results in a summary on the master node to support interactive user exploration. In PERSEUS-HUB, the visualized distributions of graph statistics provide preliminary analysis to understand a graph. To perform a deeper analysis, users with little prior knowledge can leverage patterns (e.g., spikes in the power-law degree distribution) marked by other users or experts. Moreover, PERSEUS-HUB guides users to regions of interest by highlighting anomalous nodes and helps users establish a more comprehensive understanding about the graph at hand. We demonstrate our system through the case study on real, large-scale networks.
Authors and Affiliations
Di Jin, Aristotelis Leventidis, Haoming Shen, Ruowang Zhang, Junyue Wu and Danai Koutra
Keywords
Related Articles
- EP ID EP44095
- DOI https://doi.org/10.3390/informatics4030022
- Views 264
- Downloads 0
Evaluating Awareness and Perception of Botnet Activity within Consumer Internet-of-Things (IoT) Networks
The growth of the Internet of Things (IoT), and demand for low-cost, easy-to-deploy devices, has led to the production of swathes of insecure Internet-connected devices. Many can be exploited and leveraged to perform l...
Ubiquitous Learning Architecture to Enable Learning Path Design across the Cumulative Learning Continuum
The past twelve years have seen ubiquitous learning (u-learning) emerging as a new learning paradigm based on ubiquitous technology. By integrating a high level of mobility into the learning environment, u-learning ena...
Design, Use and Evaluation of E-Learning Platforms: Experiences and Perspectives of a Practitioner from the Developing World Studying in the Developed World
Electronic learning platforms are evolving and their evaluation is becoming more complex and challenging with time. Yet, the evaluation of electronic learning services is intrinsically linked to improving the performan...
Domain-Specific Aspect-Sentiment Pair Extraction Using Rules and Compound Noun Lexicon for Customer Reviews
Online reviews are an important source of opinion to measure products’ quality. Hence, automated opinion mining is used to extract important features (aspect) and related comments (sentiment). Extraction of correct asp...
Embracing First-Person Perspectives in Soma-Based Design
A set of prominent designers embarked on a research journey to explore aesthetics in movement-based design. Here we unpack one of the design sensitivities unique to our practice: a strong first person perspective—where...