Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
This map shows the geographic impact of Jure Leskovec's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Jure Leskovec with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jure Leskovec more than expected).
This network shows the impact of papers produced by Jure Leskovec. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Jure Leskovec. The network helps show where Jure Leskovec may publish in the future.
Co-authorship network of co-authors of Jure Leskovec
This figure shows the co-authorship network connecting the top 25 collaborators of Jure Leskovec.
A scholar is included among the top collaborators of Jure Leskovec based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Jure Leskovec. Jure Leskovec is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ren, Hongyu, Hanjun Dai, Bo Dai, et al.. (2021). LEGO: Latent Execution-Guided Reasoning for Multi-Hop Question Answering on Knowledge Graphs. International Conference on Machine Learning. 8959–8970.20 indexed citations
10.
Wang, Yanbang, et al.. (2021). Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks. International Conference on Learning Representations.7 indexed citations
11.
Hu, Weihua, Matthias Fey, Hongyu Ren, et al.. (2021). OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs.. arXiv (Cornell University).1 indexed citations
12.
Li, Pan, Yanbang Wang, Hongwei Wang, & Jure Leskovec. (2020). Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning. Neural Information Processing Systems. 33. 4465–4478.5 indexed citations
13.
Ren, Hongyu & Jure Leskovec. (2020). Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. Neural Information Processing Systems. 33. 19716–19726.5 indexed citations
14.
Godwin, Jonathan, et al.. (2020). Learning to Simulate Complex Physics with Graph Networks. International Conference on Machine Learning. 1. 8459–8468.17 indexed citations
15.
Hu, Weihua, Bowen Liu, Joseph Gomes, et al.. (2019). Pre-training Graph Neural Networks.. arXiv (Cornell University).5 indexed citations
Cho, Eunjoon, Seth A. Myers, & Jure Leskovec. (2011). Friendship and mobility. Knowledge Discovery and Data Mining.13 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.