Adversarial Attack on Large Scale Graph

On graph algorithms for large-scale graphs

Large-scale evaluation of distributed attack detection

Evaluation of mechanisms for anomaly and attack detection is still a challenging task and hard to achieve. This especially holds for the evaluation of the large-scale behavior and efficiency of distributed detection mechanisms. Since testbeds and real networks are no feasible means for largescale evaluation, we present in this paper a toolchain for the large-scale evaluation of distributed atta...

Novel View Synthesis for Large-scale Scene using Adversarial Loss

Novel view synthesis aims to synthesize new images from different viewpoints of given images. Most of previous works focus on generating novel views of certain objects with a fixed background. However, for some applications, such as virtual reality or robotic manipulations, large changes in background may occur due to the egomotion of the camera. Generated images of a large-scale environment fr...

GraphChi : Large - Scale Graph Computation on Just a

Opening Remarks Summarized by Rik Farrow ([email protected]) Program Co-chair Amin Vahdat opened the conference, telling the audience that this year’s attendance was high, close to but not greater than the record for OSDI. Vahdat explained the review process: 25 out of 215 papers were accepted, producing 1079 written reviews. Authors were sent reviewers’ comments so that they could address any con...

GraphChi: Large-Scale Graph Computation on Just a PC

Current systems for graph computation require a distributed computing cluster to handle very large real-world problems, such as analysis on social networks or the web graph. While distributed computational resources have become more accessible, developing distributed graph algorithms still remains challenging, especially to non-experts. In this work, we present GraphChi, a disk-based system for...