Graph manipulations for fast centrality computation

The betweenness and closeness metrics have always been intriguing and used in many analyses. Yet, they are expensive to compute. For that reason, making the betweenness and closeness centrality computations faster is an important and well-studied problem. In this work, we propose the framework, BADIOS, which manipulates the graph by compressing it and splitting into pieces so that the centrality computation can be handled independently for each piece. Although BADIOS is designed and fine-tuned for exact betweenness and closeness centrality, it can easily be adapted for approximate solutions as well. Experimental results show that the proposed techniques can be a great arsenal to reduce the centrality computation time for various types and sizes of networks. In particular, it reduces the betweenness centrality computation time of a 4.6 million edges graph from more than 5 days to less than 16 hours. For the same graph, we achieve to decrease the closeness computation time from more than 3 days to 6 hours (12.7x speedup)

Incremental closeness centrality in distributed memory

Networks are commonly used to model traffic patterns, social interactions, or web pages. The vertices in a network do not possess the same characteristics: some vertices are naturally more connected and some vertices can be more important. Closeness centrality (CC) is a global metric that quantifies how important is a given vertex in the network. When the network is dynamic and keeps changing, the relative importance of the vertices also changes. The best known algorithm to compute the CC scores makes it impractical to recompute them from scratch after each modification. In this paper, we propose Streamer, a distributed memory framework for incrementally maintaining the closeness centrality scores of a network upon changes. It leverages pipelined, replicated parallelism, and SpMM-based BFSs, and it takes NUMA effects into account. It makes maintaining the Closeness Centrality values of real-life networks with millions of interactions significantly faster and obtains almost linear speedups on a 64 nodes 8 threads/node cluster

Editorial Note: Letter from Editors-in-Chief

On behalf of the editorial board, it is our privilege and great pride to announce the launch and the very first issue of our journal 'Focus on ELT' (FELT). Devoted to and focused on solely to the English language teaching and learning, FELT is an open source journal and accepts articles written from a theoretical or applied perspective with the possible applications to the field of English language teaching and learning. To make FELT a high-calibre scientific publication venue, we have the following principles while accepting studies to the journal: FELT publishes fully refereed high-quality original research articles and studies for the benefit of teachers and researchers in the field of English Language Teaching. FELT accepts articles written from a theoretical or applied perspective with the possible applications to the field of English language teaching and learning. FELT follows stringent publication ethics and all submissions are undergone rigorous plagiarism check. If there is an issue with plagiarism in any of the submissions, these papers are withdrawn at any state of the publication process

Object and Relation Centric Representations for Push Effect Prediction

Pushing is an essential non-prehensile manipulation skill used for tasks ranging from pre-grasp manipulation to scene rearrangement, reasoning about object relations in the scene, and thus pushing actions have been widely studied in robotics. The effective use of pushing actions often requires an understanding of the dynamics of the manipulated objects and adaptation to the discrepancies between prediction and reality. For this reason, effect prediction and parameter estimation with pushing actions have been heavily investigated in the literature. However, current approaches are limited because they either model systems with a fixed number of objects or use image-based representations whose outputs are not very interpretable and quickly accumulate errors. In this paper, we propose a graph neural network based framework for effect prediction and parameter estimation of pushing actions by modeling object relations based on contacts or articulations. Our framework is validated both in real and simulated environments containing different shaped multi-part objects connected via different types of joints and objects with different masses. Our approach enables the robot to predict and adapt the effect of a pushing action as it observes the scene. Further, we demonstrate 6D effect prediction in the lever-up action in the context of robot-based hard-disk disassembly.Comment: Project Page: https://fzaero.github.io/push_learning