jure leskovec cs

140 0 obj<>stream 0000002132 00000 n Leskovec, D. Chakrabarti, J. Kleinberg, C. Faloutsos. Label Propagation for Node Classification, 14. Jingjing Tian. 0000038233 00000 n 0000029074 00000 n Srijan Kumar, Chongyang Bai, VS Subrahmanian, Jure Leskovec ICWSM 2021 – 15th International AAAI Conference on Web and Social Media, 2021; Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks [PDF] Srijan Kumar, Xikun Zhang, Jure Leskovec KDD, 2019 – 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2019 CS224W: Fall 2017 / 0000039070 00000 n 0000004027 00000 n Jure Leskovecy Lars Backstrom Jon Kleinberg Cornell University yStanford University jure@cs.cornell.edu lars@cs.cornell.edu kleinber@cs.cornell.edu ABSTRACT Tracking new topics, ideas, and “memes” across the Web has been an issue of considerable interest. Such networks are a fundamental tool for modeling social, technological, and biological systems. 0000048628 00000 n Download PDF Abstract: Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. CS224W: Fall 2019 / Jure Leskovec Includes joint work: Lars Backstrom, Manuel Gomez-Rodriguez, Jon Kleinberg, Andreas Krause, Seth Myers, Rok Sosic, Caroline Sue, Chenguan Zhu Zhitao (Rex) Ying. 0000034826 00000 n Jure LESKOVEC | Cited by 47,060 | of Stanford University, CA (SU) | Read 314 publications | Contact Jure LESKOVEC Inscrivez-vous sur Facebook pour communiquer avec Jure Leskovec et d’autres personnes que vous pouvez connaître. Familiarity with writing rigorous proofs (at a minimum, at the level of CS 103). The following books are recommended as optional reading: Lecture slides will be posted here shortly before each lecture. What are “normal” growth patterns in social, technological, and information networks? Networks, Crowds, and Markets: Reasoning About a Highly Connected World, 1. This schedule is subject to change. endstream endobj 91 0 obj<> endobj 92 0 obj<> endobj 93 0 obj<>/Font<>/ProcSet[/PDF/Text/ImageB]/ExtGState<>>> endobj 94 0 obj<> endobj 95 0 obj<> endobj 96 0 obj<> endobj 97 0 obj<> endobj 98 0 obj<> endobj 99 0 obj<> endobj 100 0 obj<> endobj 101 0 obj<> endobj 102 0 obj<> endobj 103 0 obj<> endobj 104 0 obj<>stream 0000007288 00000 n Stanford CS Professor. Topics include: representation learning and Graph Neural Networks; algorithms for the World Wide Web; reasoning over Knowledge Graphs; influence maximization; disease outbreak detection, social network analysis. Div Garg. They are especially important for online encyclopedias such as Wikipe-dia: an article can often only be understood in the context of re-lated articles, and hyperlinks make it easy to explore this context. 0000048352 00000 n 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. Social networking sites allow users to manually categorize their friends into social circles (e.g., “circles” on Google+, and “lists” on Facebook and Twitter). Natasha Sharp Course Coordinator . degree ! 0000047555 00000 n 0000018836 00000 n 0000009129 00000 n 0000054019 00000 n 0000002052 00000 n 1. 0000007581 00000 n %PDF-1.3 %�� ¡Using effective features over graphs is the key to achieving good test performance. Stanford Electrical Engineering Department Chair. CS224W: Fall 2013 / xref Email ygallegos@cs.stanford.edu Tel (650) 723-0872 Bio BIO 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. 0000051314 00000 n Optimize embeddings according to: Given ¡Pick r so the retained singular values have at least 90% of the total energy. ¡In this lecture, we overview the traditional features for: §Node-level prediction §Link-level prediction Zecheng Zhang. Join Facebook to connect with Jure Leskovec and others you may know. ¡Back to our example: §With singular values 12.4, 9.5, and 1.3, total energy = 245.7 Communities in networks often overlap as nodes can belong to multiple communities at once. CS224W: Fall 2018 / 0000004566 00000 n 0000035144 00000 n CS 224W. Date/Time: Monday March 10, 2008 1:00 pm - 2:00 pm; Location: KACB 1447 (Academic Building) Phone: URL: Email : shanita@cc.gatech.edu; Fee(s): N/A; Extras: Contact Shanita Williams Summaries. Bio → FRED ROSENZWEIG. Full Summary: No summary paragraph submitted. [ Slides ] Summary Sentence: No summary sentence submitted. 90 0 obj <> endobj Students are expected to have the following background: The recitation sessions in the first weeks of the class will give an overview of the expected background. Weihua Hu. Complex data can be represented as a graph of relationships between objects. 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. Complex data can be represented as a graph of relationships between objects. You can access slides and project reports of previous versions of the course on our archived websites: 0000042080 00000 n Bio → MARY CHAN. <]>> For each node #collect 6 7(#), the multiset* of nodes visited on random walks starting from u 3. Teaching Assistants. Runshort fixed-length random walks starting from each node on the graph using some strategy R 2. CS224W: Fall 2011 / Familiarity with basic linear algebra (e.g., any of Math 51, Math 103, Math 113, CS 205, or EE 263 would be much more than necessary). To this end, we … All deadlines are at 11:59pm PT except for project proposal and report (which will be at 12:00pm PT). 0000050005 00000 n ��;x9�>Qb�>�&�A�!��Ǘ�X%�':1�s�� ep@xc2�`C�F�>6*,�ڍ��6BMs�1�H(h2r0xPs�/��b�ʺ ݳ,K��47�ü��#,��A[,��Va��u 7��:P��5G2��z � �]� Pages 63 This preview shows page 12 - 21 out of 63 pages. CS224W: Fall 2016 / x�b```f``-a`c`�:� �� @1v�- L@b�\��}?�.��k��0)2�h�N_��ii��C�Rb�d�ɝ��e$�:�BN^Q)2)��ē`�a �$�� \�el��3��qm��A�Ç�*>�N@� r�^3�Z�MP��q�K['��+�R^��HYT9Ꝁ��4�YLtqػ�M7_��aVhT��2fJ&��-.��'��C��ɼRU:Y��!aR��e��MNߠĩ�q��D.�C"Ze�q�&�f�w,�n*-ڒՑp��!dW, y��u��`HHxlX�5M@�!��d46vqq�q @��ظ�@0 2P� --n�XAJ� HBJ@`ll��R�р�J�Q�3;�9` ��D {"C��i@ă�M��)��8S(�C�SL;��(6� %%EOF ¡Traditional ML pipeline uses hand-designed features. H��mLSW��饅1�cx]�.�^ٖ�2�2-.Y� F�A��.�ȍ��R(}��p��h{B `@��bR� F2��bܷi��6X��s�i`�۲O�p��d� �0�X��uuoV�lhTm5JE��d�+�DS�Lܛ�G�Q�jVO��B]/�ނ��^��2��O�7p�V�gZ�ᒆ��;՚�:N-��kuG��{��v��M��j�0�Z/^2��U�� Former General Motors Vice Chairman and Board Member. I>9��[�̢��w��? CS224W: Fall 2014 / European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) , 2005. Traditional Generative Models for Graphs, Knowledge of basic computer science principles, sufficient to write a reasonably non-trivial computer program (e.g., CS107 or CS145 or equivalent are recommended), Familiarity with the basic probability theory (CS109 or Stat116 are sufficient but not necessary), Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary). CS224W: Machine Learning with Graphs Jure Leskovec, Weihua Hu, Stanford University http://cs224w.stanford.edu Bio → STEPHEN GIRSKY. EDU Stanford University, Stanford, CA 94305, USA Jure Leskovec JURE @ CS . Jure Leskovec is on Facebook. 0000009631 00000 n ¡SVD:A= U SVT: unique §U: user-to-concept factors §V: movie-to-concept factors §S: strength of each concept ¡Q: So what’s a good value for r? 0000039865 00000 n Drive.ai Co-Founder. Magna Board Member and Former President of Global Connected Consumer at General Motors. Event Details. 0000002332 00000 n § Links from important pages count more § Recursive question! Such networks are a fundamental tool for modeling social, … Jure Leskovec Stanford University jure@cs.stanford.edu Abstract—One of the main organizing principles in real-world networks is that of network communities, where sets of nodes organize into densely linked clusters. Bio → Investors. Michele Catasta. CS Faculty Candidate Seminar - Jure Leskovec. He focuses on statistical modeling of network structure, network evolution, and spread of information, influence and viruses over networks. Advisor. 0000041336 00000 n Jure Leskovec Carnegie Mellon … 0000005397 00000 n 697 Followers, 127 Following, 85 Posts - See Instagram photos and videos from Jure Leskovec (@profjure) specific to the analysis of massive graphs. Jure Leskovec∗ Lars Backstrom† Ravi Kumar‡ Andrew Tomkins‡ ∗Carnegie Mellon University †Cornell University ‡Yahoo Research jure@cs.cmu.edu lars@cs.cornell.edu {ravikuma, atomkins}@yahoo-inc.com ABSTRACT We present a detailed study of network evolution by analyzing four large online social networks with full temporal information about node and edge arrivals. 0000027311 00000 n 0000009472 00000 n 0000003384 00000 n 0000035516 00000 n 0 Ρ� �YF��m��PN�˲ b��tg؞��P�oa�?87�=�w]:p�jZv�H��fr��8�m�A��U�o��N]��4�U��+SoT�W��z�"��< �Mݠ��˵�f�?�Z��U{�f�a��P:�Ţy�4-y�F��:��*��Q!ʧ�1�+01��G. 0000026905 00000 n 0000001316 00000 n JURE LESKOVEC. 0000019723 00000 n 0000029712 00000 n 0000008839 00000 n ¡Let the energyof a set of singular values be the sum of their squares. Introduction; Machine Learning for Graphs, 5. By means of studying the underlying graph structure and its features, students are introduced to machine learning techniques and data mining tools apt to reveal insights on a variety of networks. 0000006251 00000 n 0000039678 00000 n CS224W: Fall 2010. 0000002542 00000 n Carnegie Mellon University Research Showcase Computer Science Department School of Computer Science 6-1-2007 Cost-effective Outbreak Detection in Networks Jure Leskovec Carnegie Mellon University Andreas Krause Carnegie Mellon University Carlos Guestrin Carnegie Mellon University, guestrin@cs.cmu.edu Christos Faloutsos Carnegie Mellon University, christos@cs.cmu.edu Jeanne … Bio → Advisory Board. STANFORD . I am Associate Professor of Computer Science at Stanford University, and investigator at Chan Zuckerberg Biohub. Download PDF Abstract: We propose Black Box Explanations through Transparent Approximations (BETA), a novel model agnostic framework for explaining the behavior of any black-box classifier by simultaneously optimizing for fidelity to the original model and interpretability of the explanation. Jure Leskovec est sur Facebook. Familiarity with algorithmic analysis (e.g., CS 161 would be much more than necessary). 0000007768 00000 n JULIAN MCAULEY and JURE LESKOVEC, Computer Science Department, Stanford University People’s personal social networks are big and cluttered, and currently there is no good way to automatically organize them. … EDU Stanford University, Stanford, CA 94305, USA arXiv:1205.4546v1 [cs.SI] 21 May 2012 Abstract that it represents. 21421 Jure Leskovec Stanford CS224W Machine Learning with Graphs from CS 224 at University of Southern California 0000047951 00000 n Links from important pages count more recursive. 0000007401 00000 n ��{T+)]P��'%-N�.��B+g0`�j4l�ք��ȵ�� ` �K�O �@$v�&��?�Ү ��%�;l�wd�d�H��*YԈ ��AC�~Z��ڦtssSӳ�r�Z~ZH��!�J� G�ƊV��r{�}4.|K@G�qG��8h�)��U��m�碬V��'��3ho-fj�fW��"X�p(�ШpՊ+嶨mt|8�]?zg�f�~8%8�m9�ؘ=B �� CL嗃��frv��N�� $�F��Q�D��g�}X�zA�X��oZ��;I� ��V��_�Hp$/N��N� �vЙ�xZn��{��H?-��3rW��D86w 9�Lf��X�O Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. trailer Jure Leskovec. 0000018246 00000 n 0000019249 00000 n 0000052690 00000 n startxref STEPHEN BOYD. 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. 0000027724 00000 n Links from important pages count more Recursive question 21421 Jure Leskovec. 0000006993 00000 n 90 51 Jure Leskovec Stanford University jure@cs.stanford.edu ABSTRACT Hyperlinks are an essential feature of the World Wide Web. Authors: Aditya Grover, Jure Leskovec. My general research area is applied machine learning and data science for large interconnected systems. He focuses on statistical modeling of network structure, network evolution, and spread … However, present feature learning … CS224W: Fall 2012 / He focuses on statistical modeling of network structure, network evolution, and spread … 0000052819 00000 n If we generate bigger and bigger graphs with fixed avg. Notes and reading assignments will be posted periodically on the course Web site. This course focuses on the computational, algorithmic, and modeling challenges Jonathan Gomes-Selman. Jure Leskovec Carnegie Mellon University jure@cs.cmu.edu Jon Kleinberg ∗ Cornell University kleinber@cs.cornell.edu Christos Faloutsos Carnegie Mellon University christos@cs.cmu.edu ABSTRACT How do real graphs evolve over time? Authors: Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec. Content What is this course about? 0000036899 00000 n Jiaxuan You Head TA. 0000040036 00000 n 0000029901 00000 n 0000000016 00000 n 0000053828 00000 n CS224W: Fall 2015 / School University of Southern California; Course Title CS 224W; Uploaded By russellacademic. ‪Professor of Computer Science, Stanford University‬ - ‪‪Cited by 79,326‬‬ - ‪Data mining‬ - ‪Machine Learning‬ - ‪Graph Neural Networks‬ - ‪Knowledge Graphs‬ - ‪Complex Networks‬

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