Dezhi Fang | Searcharxiv

arXiv2017

Carina: Interactive Million-Node Graph Visualization using Web Browser Technologies

We are working on a scalable, interactive visualization system, called Carina, for people to explore million-node graphs. By using latest web browser technologies, Carina offers fast graph rendering via WebGL, and works across desktop (via Electron) and mobile platforms. Different from most existing graph visualization tools, Carina does not store the full graph in RAM, enabling it to work with graphs with up to 69M edges. We are working to improve and open-source Carina, to offer researchers and practitioners a new, scalable way to explore and visualize large graph datasets.

cs.HC↗

arXiv2016

GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called GraphPrints. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets -- small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84% at the time-interval level, and 0.05% at the IP-level with 100% true positive rates at both.

cs.CR↗