ACCEPTED TUTORIALS

Authors:

Alexey Drutsa, Dmitry Ustalov, Nikita Popov and Daria Baidakova

Tutorial:

Improving Web Ranking with Humans in the Loop: Methodology, Scalability, Evaluation

Authors:

Rezvaneh Rezapour, Samin Aref, Ly Dinh and Jana Diesner

Tutorial:

PyNetworkshop: Analyzing the Structure of Networks in Python – The Essentials, Signed Networks, and Network Optimization

PyNetworkshop is a hands-on tutorial on using network libraries in Jupyter for analyzing the structure of social networks. Social network analysis is a longstanding methods toolbox used to examine the structures of relations between social entities, which can represent individuals, groups, or organizations, among other entity types. After covering general preliminaries and essentials, this tutorial focuses on different methods for analyzing the structure of signed directed networks. Existing network metrics and models are flexible in that they can detect structural dynamics that exist at three fundamental levels of analysis, namely the micro, meso, and macro levels of networks. While several open-source tools for analyzing networks are available for Python, there is a need for a pipeline that guides scholars through a multilevel analysis of networks. This tutorial is based on recent methodological advancements at the intersection of social network analysis and graph optimization nature.com/articles/s41598-020-71838-6. The intended audience are researchers who use networks or plan to start using networks in their work. We do not assume any prior knowledge other than basic level of mathematics and basic familiarity with Jupyter Python (being able to run “Hello World!” in Jupyter). 

Authors:

Pasquale Lisena and Albert Meroño-Peñuela

Tutorial:

SWApi: SPARQL Endpoints and Web API

The success of Semantic Web technology has boosted the publication of Knowledge Graphs in the Web of Data, and several technologies to access them have become available covering different spots in the spectrum of expressivity: from the highly expressive SPARQL to the controlled access of Linked Data APIs, with GraphQL in between. Many of these technologies have reached industry-grade maturity. Finding the trade-offs between them is often difficult in the daily work of developers, interested in quick API deployment and easy data ingestion. In this tutorial, we will cover this in-between technology space, with the main goal of providing strategies and tools for publishing Web APIs that ensure the easy consumption of data coming from SPARQL endpoints. Together with an overview of state-of-the-art technologies, the tutorial focuses on two novel technologies: SPARQL Transformer, which allows to get a more compact JSON structure for SPARQL results, decreasing the effort required by developers in interfacing JavaScript and Python applications; and grlc, an automatic way of building APIs on top of SPARQL endpoints by sharing queries on collaborative platforms. Moreover, we will present recent developments to combine the two, offering a complete resource for developers and researchers. Hands-on sessions will be proposed to internalize those concepts with practical exercises.

Author:

Francisco Couto

Tutorial:

Exploring Biomedical Web Resources Using Shell Scripting

Authors:

Martin Müller, Florian Laurent, Manuel Schneider and Olesia Altunina

Tutorial:

Conversational Artificial Intelligence: Can Your AI Beat the Turing Test?

 

Authors:

Benjamin Ricaud, Nicolas Aspert and Volodymyr Miz

Tutorial:

Large Scale Graph Mining: Visualization, Exploration, and Analysis

 

Authors:

Flavian Vasile, David Rohde, Olivier Jeunen, Amine Benhalloum and Otmane Sakhi

Tutorial:

Recommender Systems through the Lens of Decision Theory

Authors:

Florian Laurent, Yanick Schraner, Christian Scheller and Sharada Mohanty

Tutorial:

Flatland: Multi-Agent Reinforcement Learning on Trains

Authors:

Shubhanshu Mishra, Rezvaneh Rezapour and Jana Diesner

Tutorial:

Information Extraction from Social Media: Tasks, Data, and Open-Source Tools

Authors:

Yu Rong, Wenbing Huang, Tingyang Xu, Yatao Bian, Hong Cheng, Fuchun Sun and Junzhou Huang

Tutorial:

Advanced Deep Graph Learning: Deeper, Faster, Robuster, Unsupervised

Author:

Evann Courdier

Tutorial:

Deep Learning with PyTorch

Authors:

Tudor Mihai Avram, Dragan Cvetinovic, Levan Tsinadze, Johny Jose, Rose Howell and Mario Koenig

Tutorial:

Machine Learning-Driven Ad Blocking: From Data Collection to Deployment

Author:

Michaël Defferrard

Tutorial:

Learning from Graphs: From Mathematical Principles to Practical Tools

Author:

Onur Çelebi

Tutorial:

Building an Efficient Text Classifier from the Ground Up

Author:

Arjun Gopalan

Tutorial:

Neural Structured Learning

Authors:

Linjun Shou, Ming Gong, Jian Pei, Xiubo Geng, Xingjie Zhou and Daxin Jiang

Tutorial:

Scaling NLP Applications to 100+ Languages

Authors:

Irene Teinemaa, Javier Albert and Dmitri Goldenberg

Tutorial:

Uplift Modeling: from Causal Inference to Personalization

Authors:

Shobeir Fakhraei and Christos Faloutsos

Tutorial:

Graph Mining and Multi-Relational Learning: Tools and Applications

Authors:

Wolfram Wingerath, Benjamin Wollmer, Felix Gessert, Stephan Succo and Norbert Ritter

Tutorial:

Going for Speed: Full-Stack Performance Engineering in Modern Web-Based Applications

Authors:

Xiangyu Zhao, Wenqi Fan, Jiliang Tang and Dawei Yin

Tutorial:

Deep Recommender System: Fundamentals and Advances

Authors:

Krishnaram Kenthapadi, Ben Packer, Mehrnoosh Sameki and Nashlie Sephus

Tutorial:

Responsible AI in Industry: Practical Challenges and Lessons Learned

Authors:

Jiawei Chen, Xiang Wang, Fuli Feng and Xiangnan He

Tutorial:

Bias Issues and Solutions in Recommender System

Authors:

Stratis Ioannidis, Jennifer Dy and Ilkay Yildiz

Tutorial:

Learning from Comparisons

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