HITS can converge slowly, but not too slowly, in score and rank

none2This paper explores the fundamental question of how many iterations the celebrated HITS algorithm requires on a general graph to converge in score and, perhaps more importantly, in rank (i.e. to ``get right'' the order of the nodes). We prove upper and almost matching lower bounds. We also extend our results to weighted graphs.noneE. PESERICO; L. PRETTOPESERICO STECCHINI NEGRI DE SALVI, Enoch; Pretto, Luc

Hits can converge slowly, but not too slowly, in score and rank

This article explores the fundamental question of how many iterations the celebrated HITS algorithm requires on a general graph to converge in score and, perhaps more importantly, in rank (i.e. to "get right" the order of the nodes). We prove upper and almost matching lower bounds. We also extend our results to weighted graphs

On the limiting behavior of parameter-dependent network centrality measures

We consider a broad class of walk-based, parameterized node centrality measures for network analysis. These measures are expressed in terms of functions of the adjacency matrix and generalize various well-known centrality indices, including Katz and subgraph centrality. We show that the parameter can be "tuned" to interpolate between degree and eigenvector centrality, which appear as limiting cases. Our analysis helps explain certain correlations often observed between the rankings obtained using different centrality measures, and provides some guidance for the tuning of parameters. We also highlight the roles played by the spectral gap of the adjacency matrix and by the number of triangles in the network. Our analysis covers both undirected and directed networks, including weighted ones. A brief discussion of PageRank is also given.Comment: First 22 pages are the paper, pages 22-38 are the supplementary material

Visual Abductive Reasoning Meets Driving Hazard Prediction: Problem Formulation and Dataset

This paper addresses the problem of predicting hazards that drivers may encounter while driving a car. We formulate it as a task of anticipating impending accidents using a single input image captured by car dashcams. Unlike existing approaches to driving hazard prediction that rely on computational simulations or anomaly detection from videos, this study focuses on high-level inference from static images. The problem needs predicting and reasoning about future events based on uncertain observations, which falls under visual abductive reasoning. To enable research in this understudied area, a new dataset named the DHPR (Driving Hazard Prediction and Reasoning) dataset is created. The dataset consists of 15K dashcam images of street scenes, and each image is associated with a tuple containing car speed, a hypothesized hazard description, and visual entities present in the scene. These are annotated by human annotators, who identify risky scenes and provide descriptions of potential accidents that could occur a few seconds later. We present several baseline methods and evaluate their performance on our dataset, identifying remaining issues and discussing future directions. This study contributes to the field by introducing a novel problem formulation and dataset, enabling researchers to explore the potential of multi-modal AI for driving hazard prediction.Comment: Main Paper: 10 pages, Supplementary Materials: 25 page

The Cowl - v.19 - n.18 - Mar 20, 1957

The Cowl - student newspaper of Providence College. Volume 19, Number 18 - March 20, 1957. 6 pages

The Cowl - v.19 - n.17 - Mar 13, 1957

The Cowl - student newspaper of Providence College. Volume 19, Number 17 - March 13, 1957. 6 pages