Leskovec's research focuses on the analyzing and modeling of large social and information networks as the study of phenomena across the social, technological, and natural worlds. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Problems he investigates are motivated by large scale data, the Web and other on-line media. He also does work on text mining and applications of machine learning.

Academic Appointments

Professional Education

  • BSc, University of Ljubljana, Sloveni, Computer Science (2004)
  • PhD, Carnegie Mellon University, Computer Science (2008)

2016-17 Courses

Stanford Advisees

All Publications

  • SNAP: A General-Purpose Network Analysis and Graph-Mining Library ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY Leskovec, J., Sosic, R. 2016; 8 (1)

    View details for DOI 10.1145/2898361

    View details for Web of Science ID 000385621300001

  • Higher-order organization of complex networks SCIENCE Benson, A. R., Gleich, D. F., Leskovec, J. 2016; 353 (6295): 163-166


    Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks--at the level of small network subgraphs--remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.

    View details for DOI 10.1126/science.aad9029

    View details for Web of Science ID 000379208400037

    View details for PubMedID 27387949

  • Growing Wikipedia Across Languages via Recommendation. Proceedings of the ... International World-Wide Web Conference. International WWW Conference Wulczyn, E., West, R., Zia, L., Leskovec, J. 2016; 2016: 975-985


    The different Wikipedia language editions vary dramatically in how comprehensive they are. As a result, most language editions contain only a small fraction of the sum of information that exists across all Wikipedias. In this paper, we present an approach to filling gaps in article coverage across different Wikipedia editions. Our main contribution is an end-to-end system for recommending articles for creation that exist in one language but are missing in another. The system involves identifying missing articles, ranking the missing articles according to their importance, and recommending important missing articles to editors based on their interests. We empirically validate our models in a controlled experiment involving 12,000 French Wikipedia editors. We find that personalizing recommendations increases editor engagement by a factor of two. Moreover, recommending articles increases their chance of being created by a factor of 3.2. Finally, articles created as a result of our recommendations are of comparable quality to organically created articles. Overall, our system leads to more engaged editors and faster growth of Wikipedia with no effect on its quality.

    View details for PubMedID 27819073

  • Information Cartography COMMUNICATIONS OF THE ACM Shahaf, D., Guestrin, C., Horvitz, E., Leskovec, J. 2015; 58 (11): 62-73

    View details for DOI 10.1145/2735624

    View details for Web of Science ID 000363563800024

  • The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility. Journal of the American Medical Informatics Association Ku, J. P., Hicks, J. L., Hastie, T., Leskovec, J., Ré, C., Delp, S. L. 2015; 22 (6): 1120-1125


    Regular physical activity helps prevent heart disease, stroke, diabetes, and other chronic diseases, yet a broad range of conditions impair mobility at great personal and societal cost. Vast amounts of data characterizing human movement are available from research labs, clinics, and millions of smartphones and wearable sensors, but integration and analysis of this large quantity of mobility data are extremely challenging. The authors have established the Mobilize Center ( to harness these data to improve human mobility and help lay the foundation for using data science methods in biomedicine. The Center is organized around 4 data science research cores: biomechanical modeling, statistical learning, behavioral and social modeling, and integrative modeling. Important biomedical applications, such as osteoarthritis and weight management, will focus the development of new data science methods. By developing these new approaches, sharing data and validated software tools, and training thousands of researchers, the Mobilize Center will transform human movement research.

    View details for DOI 10.1093/jamia/ocv071

    View details for PubMedID 26272077

  • Donor Retention in Online Crowdfunding Communities: A Case Study of Proceedings of the ... International World-Wide Web Conference. International WWW Conference Althoff, T., Leskovec, J. 2015; 2015: 34-44


    Online crowdfunding platforms like and Kick-starter allow specific projects to get funded by targeted contributions from a large number of people. Critical for the success of crowdfunding communities is recruitment and continued engagement of donors. With donor attrition rates above 70%, a significant challenge for online crowdfunding platforms as well as traditional offline non-profit organizations is the problem of donor retention. We present a large-scale study of millions of donors and donations on, a crowdfunding platform for education projects. Studying an online crowdfunding platform allows for an unprecedented detailed view of how people direct their donations. We explore various factors impacting donor retention which allows us to identify different groups of donors and quantify their propensity to return for subsequent donations. We find that donors are more likely to return if they had a positive interaction with the receiver of the donation. We also show that this includes appropriate and timely recognition of their support as well as detailed communication of their impact. Finally, we discuss how our findings could inform steps to improve donor retention in crowdfunding communities and non-profit organizations.

    View details for PubMedID 27077139

  • Defining and evaluating network communities based on ground-truth KNOWLEDGE AND INFORMATION SYSTEMS Yang, J., Leskovec, J. 2015; 42 (1): 181-213
  • Analyzing Information Seeking and Drug-Safety Alert Response by Health Care Professionals as New Methods for Surveillance. Journal of medical Internet research Callahan, A., Pernek, I., Stiglic, G., Leskovec, J., Strasberg, H. R., Shah, N. H. 2015; 17 (8)


    Patterns in general consumer online search logs have been used to monitor health conditions and to predict health-related activities, but the multiple contexts within which consumers perform online searches make significant associations difficult to interpret. Physician information-seeking behavior has typically been analyzed through survey-based approaches and literature reviews. Activity logs from health care professionals using online medical information resources are thus a valuable yet relatively untapped resource for large-scale medical surveillance.To analyze health care professionals' information-seeking behavior and assess the feasibility of measuring drug-safety alert response from the usage logs of an online medical information resource.Using two years (2011-2012) of usage logs from UpToDate, we measured the volume of searches related to medical conditions with significant burden in the United States, as well as the seasonal distribution of those searches. We quantified the relationship between searches and resulting page views. Using a large collection of online mainstream media articles and Web log posts we also characterized the uptake of a Food and Drug Administration (FDA) alert via changes in UpToDate search activity compared with general online media activity related to the subject of the alert.Diseases and symptoms dominate UpToDate searches. Some searches result in page views of only short duration, while others consistently result in longer-than-average page views. The response to an FDA alert for Celexa, characterized by a change in UpToDate search activity, differed considerably from general online media activity. Changes in search activity appeared later and persisted longer in UpToDate logs. The volume of searches and page view durations related to Celexa before the alert also differed from those after the alert.Understanding the information-seeking behavior associated with online evidence sources can offer insight into the information needs of health professionals and enable large-scale medical surveillance. Our Web log mining approach has the potential to monitor responses to FDA alerts at a national level. Our findings can also inform the design and content of evidence-based medical information resources such as UpToDate.

    View details for DOI 10.2196/jmir.4427

    View details for PubMedID 26293444

  • Overlapping Communities Explain Core-Periphery Organization of Networks PROCEEDINGS OF THE IEEE Yang, J., Leskovec, J. 2014; 102 (12): 1892-1902
  • Structure and Overlaps of Ground-Truth Communities in Networks ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY Yang, J., Leskovec, J. 2014; 5 (2)

    View details for DOI 10.1145/2594454

    View details for Web of Science ID 000335576200005

  • Discovering Social Circles in Ego Networks ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA McAuley, J., Leskovec, J. 2014; 8 (1): 73-100

    View details for DOI 10.1145/2556612

    View details for Web of Science ID 000333491900004

  • Modeling Information Propagation with Survival Theory Gomez-Rodriguez, M., Leskovec, J., Schoelkopf, B. 2013
  • Community Detection in Networks with Node Attributes IEEE 13th International Conference on Data Mining (ICDM) Yang, J., McAuley, J., Leskovec, J. IEEE. 2013: 1151–1156
  • Structure and Dynamics of Information Pathways in Online Media Gomez-Rodriguez, M., Leskovec, J., Schoelkopf, B. 2013
  • Nonparametric Multi-group Membership Model for Dynamic Networks Kim, M., Leskovec, J. 2013
  • From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews McAuley, J., Leskovec, J. 2013
  • Hidden Factors and Hidden Topics: Understanding Rating Dimensions with Review Text McAuley, J., Leskovec, J. 2013
  • NIFTY: A System for Large Scale Information Flow Tracking and Clustering Suen, C., Huang, S., Eksombatchai, C., Sosic, R., Leskovec, J. 2013
  • Steering User Behavior With Badges Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J. 2013
  • Overlapping Community Detection at Scale: A Nonnegative Matrix Factorization Approach Yang, J., Leskovec, J. 2013
  • Information Cartography: Creating Zoomable, Large-Scale Maps of Information Shahaf, D., Yang, J., Suen, C., Jacobs, J., Wang, H., Leskovec, J. 2013
  • Community Detection in Networks with Node Attributes Yang, J., McAuley, J., Leskovec, J. 2013
  • A computational approach to politeness with application to social factors Danescu-Niculescu-Mizil, C., Sudhof, M., Jurafsky, D., Leskovec, J., Potts, C. 2013
  • No Country for Old Members: User lifecycle and linguistic change in online communities Danescu-Niculescu-Mizil, C., West, R., Jurafsky, D., Leskovec, J., Potts, C. 2013
  • What’s in a name? Understanding the Interplay between Titles, Content, and Communities in Social Media Lakkaraju, H., McAuley, J., Leskovec, J. 2013
  • Measurement error in network data: A re-classification SOCIAL NETWORKS Wang, D. J., Shi, X., McFarland, D. A., Leskovec, J. 2012; 34 (4): 396-409
  • Inferring Networks of Diffusion and Influence ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA Gomez-Rodriguez, M., Leskovec, J., Krause, A. 2012; 5 (4)
  • Community-Affiliation Graph Model for Overlapping Network Community Detection 12th IEEE International Conference on Data Mining (ICDM) Yang, J., Leskovec, J. IEEE. 2012: 1170–1175
  • Image Labeling on a Network: Using Social-Network Metadata for Image Classification 12th European Conference on Computer Vision (ECCV) McAuley, J., Leskovec, J. SPRINGER-VERLAG BERLIN. 2012: 828–841
  • Learning to Discover Social Circles in Ego Networks McAuley, J., Leskovec, J. 2012
  • Latent Multi-group Membership Graph Model Kim, M., Leskovec, J. 2012
  • Information Diffusion and External Influence in Networks Myers, S., Zhu, C., Leskovec, J. 2012
  • Learning Attitudes and Attributes from Multi-Aspect Reviews McAuley, J., Leskovec, J., Jurafsky, D. 2012
  • Automatic versus Human Navigation in Information Networks West, R., Leskovec, J. 2012
  • Discovering Value from Community Activity on Focused Question Answering Sites: A Case Study of Stack Overflow Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J. 2012
  • The Life and Death of Online Groups: Predicting Group Growth and Longevity Kairam, S., Wang, D., Leskovec, J. 2012
  • Human Wayfinding in Information Networks West, R., Leskovec, J. 2012
  • Effects of User Similarity in Social Media Anderson, A., Huttenlocher, D., Kleinberg, J., Leskovec, J. 2012
  • Image Labeling on a Network: Using Social-Network Metadata for Image Classiffcation McAuley, J., Leskovec, J. 2012
  • Defining and Evaluating Network Communities based on Ground-truth 12th IEEE International Conference on Data Mining (ICDM) Yang, J., Leskovec, J. IEEE. 2012: 745–754
  • Clash of the Contagions: Cooperation and Competition in Information Diffusion 12th IEEE International Conference on Data Mining (ICDM) Myers, S. A., Leskovec, J. IEEE. 2012: 539–548
  • HADI: Mining Radii of Large Graphs ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA Kang, U., Tsourakakis, C. E., Appel, A. P., Faloutsos, C., Leskovec, J. 2011; 5 (2)
  • Large-Scale Web Data Analysis IEEE INTELLIGENT SYSTEMS Leskovec, J. 2011; 26 (1): 11-11
  • Sentiment Flow Through Hyperlink Networks Miller, M., Sathi, C., Wiesenthal, D., Leskovec, J., Potts, C. 2011
  • Modeling Social Networks with Node Attributes using the Multiplicative Attribute Graph Model Kim, M., Leskovec, J. 2011
  • Dynamics of Bidding in a P2P Lending Service: Effects of Herding and Predicting Loan Success Ceyhan, S., Shi, X., Leskovec, J. 2011
  • The Network Completion Problem: Inferring Missing Nodes and Edges in Networks Kim, M., Leskovec, J. 2011
  • Patterns of Temporal Variation in Online Media Yang, J., Leskovec, J. 2011
  • Supervised Random Walks: Predicting and Recommending Links in Social Networks Backstrom, L., Leskovec, J. 2011
  • Friendship and Mobility: User Movement In Location-Based Social Networks Cho, E., Myers, S., A., Leskovec, J. 2011
  • Correcting for Missing Data in Information Cascades Sadikov, E., Medina, M., Leskovec, J., Garcia-Molina, H. 2011
  • The Role of Social Networks in Online Shopping: Information Passing, Price of Trust, and Consumer Choice Guo, S., Wang, M., Leskovec, J. 2011
  • Kronecker Graphs: An Approach to Modeling Networks JOURNAL OF MACHINE LEARNING RESEARCH Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C., Ghahramani, Z. 2010; 11: 985-1042
  • Multiplicative Attribute Graph Model of Real-World Networks 7th Workshop on Algorithms and Models for the Web Graph Kim, M., Leskovec, J. SPRINGER-VERLAG BERLIN. 2010: 62–73
  • Predicting Positive and Negative Links in Online Social Networks Leskovec, J., Huttenlocher, D., Kleinberg, J. 2010
  • Citing for High Impact Shi, X., Leskovec, J., McFarland, D., A. 2010
  • Modeling Information Diffusion in Implicit Networks Yang, J., Leskovec, J. 2010
  • Empirical Comparison of Algorithms for Network Community Detection Leskovec, J., Lang, K., Mahoney, M. 2010
  • On the Convexity of Latent Social Network Inference Myers, S., A., Leskovec, J. 2010
  • Radius Plots for Mining Tera-byte Scale Graphs: Algorithms, Patterns, and Observations Kang, U., Tsourakakis, C., Appel, A., Faloutsos, C., Leskovec, J. 2010
  • Governance in Social Media: A case study of the Wikipedia promotion process Leskovec, J., Huttenlocher, D., Kleinberg, J. 2010
  • Signed Networks in Social Media 28th Annual CHI Conference on Human Factors in Computing Systems Leskovec, J., Huttenlocher, D., Kleinberg, J. ASSOC COMPUTING MACHINERY. 2010: 1361–1370
  • Meme-tracking and the Dynamics of the News Cycle 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Leskovec, J., Backstrom, L., Kleinberg, J. ASSOC COMPUTING MACHINERY. 2009: 497–505
  • Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters Internet Mathematics Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M. 2009; 1 (6): 29--123
  • Modeling blog dynamics Goetz, M., Leskovec, J., Mcglohon, M., Faloutsos, C. 2009
  • The Battle of the Water Sensor Networks (BWSN): A Design Challenge for Engineers and Algorithms Leskovec, J., Ostfeld et al, A. 2009
  • Efficient Sensor Placement Optimization for Securing Large Water Distribution Networks JOURNAL OF WATER RESOURCES PLANNING AND MANAGEMENT-ASCE Krause, A., Leskovec, J., Guestrin, C., VanBriesen, J., Faloutsos, C. 2008; 134 (6): 516-526
  • Mobile Call Graphs: Beyond Power-Law and Lognormal Distributions Seshadri, M., Machiraju, S., Sridharan, A., Bolot, J., Faloutsos, C., Leskovec, J. 2008
  • Planetary-Scale Views on a Large Instant-Messaging Network Leskovec, J., Horvitz, E. 2008
  • Epidemic Thresholds in Real Networks Chakrabarti, D., Wang, Y., Wang, C., Leskovec, J., Faloutsos, C. 2008
  • Statistical Properties of Community Structure in Large Social and Information Networks Leskovec, J., Lang, K., Dasgupta, A., Mahoney, M. 2008
  • Microscopic Evolution of Social Networks Leskovec, J., Backstrom, L., Kumar, R., Tomkins, A. 2008
  • Monitoring Network Evolution using MDL Ferlez, J., Faloutsos, C., Leskovec, J., Mladenic, D., Grobelnik, M. 2008
  • Cost-effective Outbreak Detection in Networks Leskovec, J., Krause, A., Guestrin, C., Faloutsos, C., VanBriesen, J., Glance, N. 2007
  • Web Projections: Learning from Contextual Subgraphs of the Web Leskovec, J., Dumais, S., Horvitz, E. 2007
  • Scalable Modeling of Real Graphs using Kronecker Multiplication Leskovec, J., Faloutsos, C. 2007
  • The Dynamics of Viral Marketing ACM Transactions on the Web (TWEB) Leskovec, J., Adamic, L., Huberman, B. 2007; 1 (1)
  • Graph Evolution: Densification and Shrinking Diameters Leskovec, J., Kleinberg, J., Faloutsos, C. 2007
  • Cascading Behavior in Large Blog Graphs Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N., Hurst, M. 2007
  • Information Survival Threshold in Sensor and P2P Networks Chakrabarti, D., Leskovec, J., Faloutsos, C., Madden, S., Guestrin, C., Faloutsos, M. 2007
  • Sampling from Large Graphs Leskovec, J., Faloutsos, C. 2006
  • Data Association for Topic Intensity Tracking Krause, A., Leskovec, J., Guestrin, C. 2006
  • Patterns of Influence in a Recommendation Network Leskovec, J., Singh, A., Kleinberg, J. 2006
  • The Dynamics of Viral Marketing Leskovec, J., Adamic, L., Huberman, B. 2006
  • Realistic, mathematically tractable graph generation and evolution, using Kronecker multiplication 16th European Conference on Machine Learning (ECML)/9th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) Leskovec, J., Chakrabarti, D., Kleinberg, J., Faloutsos, C. SPRINGER-VERLAG BERLIN. 2005: 133–145
  • Semantic Text Features from Small World Graphs Leskovec, J., Shawe-Taylor, J. 2005
  • Impact of Linguistic Analysis on the Semantic Graph Coverage and Learning of Document Extracts Leskovec, J., Milic-Frayling, N., Grobelnik, M. 2005
  • Graphs over Time: Densification Laws, Shrinking Diameters and Possible Explanations Leskovec, J., Kleinberg, J., Faloutsos, C. 2005
  • Extracting Summary Sentences Based on the Document Semantic Graph Microsoft Research Technical Report MSR-TR-2005-07 Leskovec, J., Milic-Frayling, N., Grobelnik, M. 2005
  • Learning Sub-structures of Document Semantic Graphs for Document Summarization Leskovec, J., Grobelnik, M., Milic-Frayling, N. 2004
  • The Download Estimation task on KDD Cup 2003 SIGKDD Explorations Brank, J., Leskovec, J. 2003
  • Linear Programming boost for Uneven Datasets Leskovec, J., Shawe-Taylor, J. 2003
  • KDD Cup 2003: The Download Estimation task Jozef Stefan Institute Technical Report Brank, J., Leskovec, J. 2003
  • Govorec - sistem za slovensko govorjenje racunalniskih besedil Information Society Leskovec, J. 2001
  • Detection of Human Bodies using Computer Analysis of a Sequence of Stereo Images Leskovec, J. 1999