¡Using effective features over graphs is the key to achieving good test performance. 5:15–5:30PM: Invited presentation: Deep Graph Library (Zheng Zhang and Alex Smola) slides Graph Neural Networks (GNNs) provide an expressive way to fuse information from network structure and node features. Stanford University, Stanford, CA, USA In this video a group of the most recent node embedding algorithms like Word2vec, Deepwalk, NBNE, Random Walk and GraphSAGE are explained by Jure Leskovec. You may refer to the paper or project webpage for more details. Knowledge Graphs Jure Leskovec, Stanford University 2. Research 2821 Mission College Blvd Santa Clara, CA 95054 Jon Kleinberg KLEINBER@CS.CORNELL.EDU Computer Science Department … 4:15–4:45PM: Invited talk: Bistra Dilkina slides. Such models can find patterns which ac- We develop the Latent Multi-group Membership … Note that the output of the algorithm depends on the order in which the nodes are considered. 2:45–3:15PM: Presentation and discussion: Open Graph Benchmark (Jure Leskovec) 3:15–4:15PM: Poster session #2 / Coffee break. What are “normal” growth patterns in social, technological, and information networks? Use case: Identity-aware Graph Neural Networks (AAAI 2021) Reproducing experiments in Identity-aware Graph Neural Networks, Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec, AAAI 2021. Latent Multi-group Membership Graph Model Myunghwan Kim MYKIM @ STANFORD . His research focuses on machine learning and data mining with graphs, a general language for describing social, technological and biological systems. CoRR abs/2009.00142 ( 2020 ) Scenario: ¡ Graph where everyone starts with all B ¡ Small set Sof early adopters of A §Hard-wire S–they keep using Ano matter what payoffs tell them to do ¡ Assume payoffs are set in such a way that nodes say: If more than q=50% of my friends take A I’ll also take A. Download PDF Abstract: Many applications of machine learning require a model to make accurate pre-dictions on test examples that are distributionally different from training ones, while task-specific labels are scarce during training. “Graph Convolutional Neural Networks for Web-Scale Recommender Systems. However, GNNs are prone to adversarial … ¡1)New problem:Outbreak detection ¡ (2)Develop an approximation algorithm §It is a submodularopt. Publications (in chronological order) 2021; ... Semantic Text Features from Small World Graphs. Extracting Summary Sentences Based on the Document Semantic Graph. Follow @jure. Jure has 12 jobs listed on their profile. @InProceedings{pmlr-v119-you20b, title = {Graph Structure of Neural Networks}, author = {You, Jiaxuan and Leskovec, Jure and He, Kaiming and Xie, Saining}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {10881--10891}, year = {2020}, editor = {Hal Daumé III and Aarti Singh}, volume = {119}, series = {Proceedings of Machine Learning Research}, … Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systems. EDU Stanford University, Stanford, CA 94305, USA arXiv:1205.4546v1 [cs.SI] 21 May 2012 Abstract that it represents. If we generate bigger and bigger graphs with fixed avg. Jure Leskovec is Associate Professor of Computer Science at Stanford University, Chief Scientist at Pinterest, and investigator at Chan Zuckerberg Biohub. ¡Traditional ML pipeline uses hand-designed features. How- ever, the expressive power of existing GNNs is upper … You've reached the end of your free preview. Here we study a wide range of real graphs Graph evolution: Densification and shrinking diameters by Jure Leskovec, Jon Kleinberg, Christos Faloutsos - ACM TKDD , 2007 Jiaxuan You • Zhitao Ying • Jure Leskovec 2020-12-01 Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs ‪Professor of Computer Science, Stanford University‬ - ‪‪Cited by 79,326‬‬ - ‪Data mining‬ - ‪Machine Learning‬ - ‪Graph Neural Networks‬ - ‪Knowledge Graphs‬ - ‪Complex Networks‬ View Jure Leskovec’s profile on LinkedIn, the world's largest professional community. Subspace, Latent Structure and Feature Selection techniques: Statistical and Optimization perspectives Workshop, Slovenia, 2005. ¡In this lecture, we overview the traditional features for: §Node-level prediction §Link-level prediction Hongwei Wang. Zitnik, D. Jurafsky. Jure LESKOVEC | Cited by 47,060 | of Stanford University, CA (SU) | Read 314 publications | Contact Jure LESKOVEC Jure Leskovec, Anand Rajaraman and Jeff Ullman welcome you to the self-paced version of the on-line course based on the book Mining of Massive Datasets. CS224W: Machine Learning with Graphs Jure Leskovec, Hongyu Ren, Stanford University http://cs224w.stanford.edu 4:45–5:15PM: Invited talk: Marinka Žitnik slides. Jure Leskovec Reasoning in Knowledge Graphs using Embeddings Joint work with H. Ren, W. Hamilton, R. Ying, J. You,M. Identity-aware Graph Neural Networks Jiaxuan You, Jonathan Gomes-Selman, Rex Ying, Jure Leskovec Department of Computer Science, Stanford University fjiaxuan, jgs8, rexy, jureg@cs.stanford.edu Abstract Message passing Graph Neural Networks (GNNs) provide a powerful modeling framework for relational data. 10/3/19 Jure Leskovec, Stanford CS224W: Machine Learning with Graphs 30 This first phase stops when a local maxima of the modularity is attained, i.e., when no individual node move can improve the modularity. CS224W: Machine Learning with Graphs Jure Leskovec, Weihua Hu, Stanford University http://cs224w.stanford.edu Generating novel graph structures that optimize given objectives while obeying some given underlying rules is fundamental for chemistry, biology and social science research. Position-aware Graph Neural Networks Jiaxuan You 1Rex Ying Jure Leskovec Abstract Learning node embeddings that capture a node’s position within the broader graph structure is cru-cial for many prediction tasks on graphs. Authors: Tailin Wu, Hongyu Ren, Pan Li, Jure Leskovec. Jure Leskovec. degree ! Please login to be able to save your searches and receive alerts for new content matching your search criteria. Download PDF Abstract: Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. J. Leskovec, N. Milic-Frayling, M. Grobelnik. Learning Semantic Sub-graphs for Document Summarization Jure Leskovec*, Marko Grobelnik*, Natasa Milic-Frayling† * Jozef Stefan Institute Ljubljana, Slovenia {Jure.Leskovec, Marko.Grobelnik}@ijs.si †Microsoft Research Cambridge, UK natasamf@microsoft.com Abstract In this paper we present a method for summarizing document by creating a semantic graph of the original … 2/14/21 47 Jure Leskovec, Stanford CS224W: Machine Learning with Graphs, ¡ Stop iteration § After convergence or when maximum iterations are reached 1 1 1 0 Correct Now! 9/29/19 Jure Leskovec, Stanford CS224W: Machine Learning with Graphs, cs224w.stanford.edu 29 n k n k p kk pkk C ii i »-==-× - = (1) 1 (1) Clustering coefficient of a random graph is small. Pan Li, Yanbang Wang, Hongwei Wang, Jure Leskovec: Distance Encoding - Design Provably More Powerful Graph Neural Networks for Structural Representation Learning. EDU Stanford University, Stanford, CA 94305, USA Jure Leskovec JURE @ CS . by Jure Leskovec, Jon Kleinberg, Christos Faloutsos , 2005 How do real graphs evolve over time? Authors: Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, Jure Leskovec. STANFORD . Kronecker Graphs: An Approach to Modeling Networks Jure Leskovec JURE@CS.STANFORD.EDU Computer Science Department Stanford University Stanford, CA 94305 Deepayan Chakrabarti DEEPAY@YAHOO-INC.COM Yahoo! If nothing happens, download GitHub Desktop and try again. 132 Results for: Author: Jure Leskovec Edit Search Save Search Failed to save your search, try again later Search has been saved (My Saved Searches) Save this search. J. Leskovec, J. Shawe-Taylor. Our new #KDD2018 paper with @PinterestEng. https://t.co/rvFhja3PvO” problem! Authors: William L. Hamilton, Rex Ying, Jure Leskovec. Download PDF Abstract: Representation learning of graph-structured data is challenging because both graph structure and node features carry important information. ¡ (3) Speed-up greedy hill-climbing §Valid for optimizing general submodularfunctions (i.e., also works for influence maximization)

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