Jure Leskovec.
I am Associate Professor of Computer Science at Stanford University. My research focuses on mining and modeling large social and information networks, their evolution, and diffusion of information and influence over them. Problems I investigate are motivated by large scale data, the web and on-line media.
I am also Investigator at the Chan Zuckerberg Biohub.
What's new
- Tutorial on Deep Learning for Network Biology. Held at ISMB 2018 (July 6, Chicago, IL).
- Tutorial on Representation Learning on Networks. Held at WWW 2018 (April 24, Lyon, France).
- Wiki Workshop 2018. A forum bringing together researchers exploring all aspects of Wikipedia and other Wikimedia sites. Held at WWW 2018 (April 24, Lyon, France).
- MIS2: Misinformation and misbehavior mining on the Web. Workshop held at WSDM 2018.
- Tutorial on Antisocial Behavior On The Web: Characterization And Detection with Srijan Kumar, Justin Cheng. Held at WWW 2017 (April 2, Perth, Australia).
- Wiki Workshop 2017, A forum bringing together researchers exploring all aspects of Wikipedia and other Wikimedia sites. Held at WWW 2017 (April 3, Perth, Australia).
- Teaching the Mining Massive Datasets MOOC on Coursera.
- With Anand Rajaraman and Jeff Ullman we are working in a new edition of Mining of Massive Datasets book. PDFs are at MMDS.org
Latest publications
- To Embed or Not: Network Embedding as a Paradigm in Computational Biology. Frontiers in Genetics, 2019.
- Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks. KDD, 2019.
- Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. KDD, 2019.
- How Powerful Are Graph Neural Networks?. ICLR, 2019.
- Evolution of resilience in protein interactomes across the tree of life. PNAS, 2019.
- Complete the Look: Scene-based Complementary Product Recommendation. CVPR, 2019.
- Faithful and Customizable Explanations of Black Box Models. AIES, 2019.
- Goal-setting And Achievement In Activity Tracking Apps: A Case Study Of MyFitnessPal. WWW, 2019.
- Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems. WWW, 2019.
- Predicting pregnancy using large-scale data from a women’s health tracking mobile application. WWW, 2019.
- Inferring Multidimensional Rates of Aging from Cross-Sectional Data. AISTATS, 2019.
- The Local Closure Coefficient: A New Perspective On Network Clustering. WSDM, 2019.