WorldCat Identities

Leskovec, Jurij

Overview
Works: 54 works in 79 publications in 2 languages and 407 library holdings
Roles: Author, Thesis advisor, Interviewee
Classifications: QA76.9.D343, 006.312
Publication Timeline
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Most widely held works by Jurij Leskovec
Mining of massive datasets by Anand Rajaraman( Book )

24 editions published between 2011 and 2015 in English and held by 152 WorldCat member libraries worldwide

This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction. It includes a range of over 150 challenging exercises. --
The role of social networks in online shopping information passing, price of trust, and consumer choice by Stephen Guo( )

2 editions published in 2011 in English and held by 4 WorldCat member libraries worldwide

Prodiranje Ag[zgoraj]+ v dentinske tubule z enosmernim električnim tokom by J Leskovec( )

1 edition published in 1998 in Slovenian and held by 3 WorldCat member libraries worldwide

Modeling link qualities in a sensor network by Jurij Leskovec( )

1 edition published in 2005 in English and held by 3 WorldCat member libraries worldwide

Največje (spletne) klepetulje na svetu smo Slovenci by Jurij Leskovec( )

1 edition published in 2007 in Slovenian and held by 3 WorldCat member libraries worldwide

Meme-tracking and the dynamics of the news cycle by Jurij Leskovec( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Graph evolution : densification and shrinking diameters by Jurij Leskovec( )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

Dr. Jure Leskovec : raziskovalec omrežij na univerzi Stanford v Kaliforniji by Jurij Leskovec( )

1 edition published in 2012 in Slovenian and held by 2 WorldCat member libraries worldwide

Sumarizacija besedil s pretvorbo v semantično mrežo : diplomska naloga by Jurij Leskovec( Book )

1 edition published in 2004 in Slovenian and held by 2 WorldCat member libraries worldwide

Dynamics of large networks by Jurij Leskovec( Book )

1 edition published in 2008 in English and held by 2 WorldCat member libraries worldwide

Abstract: "A basic premise behind the study of large networks is that interaction leads to complex collective behavior. In our work we found very interesting and counterintuitive patterns for time evolving networks, which change some of the basic assumptions that were made in the past. We then develop models that explain processes which govern the network evolution, fit such models to real networks, and use them to generate realistic graphs or give formal explanations about their properties. In addition, our work has a wide range of applications: it can help us spot anomalous graphs and outliers, forecast future graph structure and run simulations of network evolution. Another important aspect of our research is the study of 'local' patterns and structures of propagation in networks. We aim to identify building blocks of the networks and find the patterns of influence that these blocks have on information or virus propagation over the network. Our recent work included the study of the spread of influence in a large person to-person product recommendation network and its effect on purchases. We also model the propagation of information on the blogosphere, and propose algorithms to efficiently find influential nodes in the network. A central topic of our thesis is also the analysis of large datasets as certain network properties only emerge and thus become visible when dealing with lots of data. We analyze the world's largest social and communication network of Microsoft Instant Messenger with 240 million people and 255 billion conversations. We also made interesting and counterintuitive observations about network community structure that suggest that only small network clusters exist, and that they merge and vanish as they grow."
Iskanje orodja, s katerim bomo izluščili modrost množic : dr. Jure Leskovec, profesor na Stanfordu by Jurij Leskovec( )

1 edition published in 2010 in Slovenian and held by 2 WorldCat member libraries worldwide

Predicting positive and negative links in online social networks by Jurij Leskovec( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Empirical comparison of algorithms for network community detection by Jurij Leskovec( )

1 edition published in 2010 in English and held by 2 WorldCat member libraries worldwide

Scalable modeling of real graphs using Kronecker multiplication by Jurij Leskovec( )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

The dynamics of viral marketing by Jurij Leskovec( )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

Graphs over time : densification laws, shrinking diameters and possible explanations by Jurij Leskovec( )

1 edition published in 2005 in English and held by 2 WorldCat member libraries worldwide

Web projections learning from contextual subgraphs of the web by Jurij Leskovec( )

1 edition published in 2007 in English and held by 2 WorldCat member libraries worldwide

Human Decisions and Machine Predictions by Jon Kleinberg( )

2 editions published in 2017 in English and held by 0 WorldCat member libraries worldwide

We examine how machine learning can be used to improve and understand human decision-making. In particular, we focus on a decision that has important policy consequences. Millions of times each year, judges must decide where defendants will await trial-at home or in jail. By law, this decision hinges on the judge's prediction of what the defendant would do if released. This is a promising machine learning application because it is a concrete prediction task for which there is a large volume of data available. Yet comparing the algorithm to the judge proves complicated. First, the data are themselves generated by prior judge decisions. We only observe crime outcomes for released defendants, not for those judges detained. This makes it hard to evaluate counterfactual decision rules based on algorithmic predictions. Second, judges may have a broader set of preferences than the single variable that the algorithm focuses on; for instance, judges may care about racial inequities or about specific crimes (such as violent crimes) rather than just overall crime risk. We deal with these problems using different econometric strategies, such as quasi-random assignment of cases to judges. Even accounting for these concerns, our results suggest potentially large welfare gains: a policy simulation shows crime can be reduced by up to 24.8% with no change in jailing rates, or jail populations can be reduced by 42.0% with no increase in crime rates. Moreover, we see reductions in all categories of crime, including violent ones. Importantly, such gains can be had while also significantly reducing the percentage of African-Americans and Hispanics in jail. We find similar results in a national dataset as well. In addition, by focusing the algorithm on predicting judges' decisions, rather than defendant behavior, we gain some insight into decision-making: a key problem appears to be that judges to respond to 'noise' as if it were signal. These results suggest that
 
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Alternative Names
Jure Leskovec informaticus uit Slovenië

Jure Leskovec Slovene computer scientist

Jure Leskovec slovensk datalog arbejdende på Stanford

Jure Leskovec slovenski računalničar

Leskovec, J.

Leskovec, Jure

Leskovec, Jure, 1980-

Leskovec, Jurij‏ 1980-

레스코벡, 쥬어 1980-

Languages