Program

Tutorial: Hands-on with scikit-network

09:50 - 10:00	Opening
10:00 - 11:30	Introduction and core components: data structures and their manipulation, simple tasks on graphs: slides and notebook
11:30 - 11:45	Coffee break
11:45 - 12:45	Real-world use case: analysis of the Wikipedia “vital articles” graph: notebook
12:45 - 13:00	Concluding Remarks

14.30 - 14.35	Opening
14.35 - 14.45	Paper Pitch: Neural Maximum Independent Set
14.45 - 14.55	Paper Pitch: Fea2Fea: Exploring Structural Feature Correlations via Graph Neural Networks
14.55 - 15.05	Paper Pitch: Web Image Context Extraction with GraphNeural Networks and Sentence Embeddings on the DOM tree
15.05 - 15.15	Paper Pitch: The Effects of Randomness on the Stability of Node Embeddings
15.15 - 15.25	Paper Pitch: Graph homomorphism features: why not sample?
15.25 - 15.35	Paper Pitch: Towards Mining Generalized Patterns From RDF Data And A Domain Ontology
15.35 - 15.55	Coffee break
15.55 - 16.00	Poster Pitch: GUDIE: a flexible, user-defined method to extract subgraphs of interest from large graphs
16.00 - 16.05	Poster Pitch: Representation Learning using Graph Neural Nets: A case-study in HMMs
16.05 - 16.10	Poster Pitch: Vessel’s Identity Graph and Analysis
16.10 - 16.15	Poster Pitch: A Study of Explainable Community-Level Features
16.15 - 17.15	Keynote: Graph Representation Learning Beyond Homophily & Proximity by Danai Koutra
17.15 - 18.30	Interactive Poster Session
18.30 - 18.45	Conclusion and Best Paper Award