logAMS.github.io

logAMS 2023

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Welcome to the website for logAMS, the Amsterdam meetup for the global LoG Conference. Hosted by Elsevier & Vrije Universiteit Amsterdam on November 29 + 30, 2023.

About • Dates • Registration • Contact • Organisation

About

The logAMS conference is a satellite event of the global LoG Conference, featuring talks, posters and networking opportunities, aiming to bring together the local community in graph machine learning and geometry.

Details

Meet-up on November 29 + 30, 2023.
The event is free ✨ to attend.
Hosted by Elsevier at Radarweg 29a, 1043 NX Amsterdam.
Coffee and lunch provided.

Dates

October 10, 2023. Registration opens.
November 13, 2023. Registration closes. After this date we maintain a wait list and registrations are considered up to capacity.
November 29 + 30, 2023. Conference.

Schedule

29 November

`Time`	`Event`	`Details`
09:30 - 10:00	Registration and coffee
10:00 - 10:15	Welcome
10:15 - 10:45	Talk: Miltos Kofinas	Neural Networks are Graphs! GNNs for equivariant processing of NNs
10:45 - 11:15	Talk: Taraneh Younesian	GRAPES: Learning to sample graphs for scalable GNNs
11:15 - 11:30	Break
11:30 - 12:00	Talk: Andrea Cavallo	GNNs on heterophilous graphs
12:00 - 13:00	Lunch
13:00 - 15:00	Poster Session	Graph neural networks for metamodelling urban drainage systems (Alexander Garzón) Physics-Based Graph Neural Networks for Rapid Spatio-Temporal Flood Modelling (Roberto Bentivoglio) Anomaly Detection in Continuous-Time Temporal Provenance Graphs (Jakub Reha) Graph Isomorphic Networks for Reliability Assessments in the MV Energy Grid (Charlotte Cambier van Nooten) Heterophily-Based Graph Neural Network for Imbalanced Classification (Yuntao Li) No time to waste: practical statistical contact tracing with few low-bit messages (Rob Romijnders) The Role of Personal Perspectives in Open-Domain Dialogue (Selene Baez Santamaria and Lea Krause) Adapting Neural Link Predictors for Data-Efficient Complex Query Answering (Daniel Daza) Graph representation learning identifies repositionable drug candidates for HIV-1 (Andrew Foster)
15:00 - 15:30	Talk: Dulhan Jayalath and Jonas Jürß	Recursive Algorithmic Reasoning
15:30 - 16:00	Talk: Floris Geerts	Weisfeiler Leman meet Vapnik Chervonenkis
16:00 - 16:15	Break
16:15 - 16:45	Talk: Hinrikus Wolf and Luca Oeljeklaus	Structural Node Embeddings w. Homomorphism Counts
16:45 - 17:15	Talk: Tianqi Zhao	Multi-label Node Classification On Graph-Structured Data
17:15 - ~18:30	Drinks
~18:30 -	dinner

30 November

` Time `	`Event`	`Details`
09:30 - 10:00	Registration and coffee
10:00 - 10:15	Welcome
10:15 - 10:45	Talk: Matthew Stephenson
10:45 - 11:15	Talk: Rob Romijnders	No Time To Waste: practical statistical contact tracing with few low-bit messages
11:15 - 11:30	Break
11:30 - 12:00	Talk: Megha Khosla	Private Graph Reconstruction via Feature Explanations
12:00 - 13:00	Lunch
13:00 - 15:00	Poster Session	Deep Statistical Solver for Distribution System State Estimation (Benjamin Habib) Structural Node Embeddings with Homomorphism Counts (Hinrikus Wolf, Luca Oeljeklaus) Elemental Representations in Graph Neural Networks (Victor Kyllesbech) Neural Networks Are Graphs! Graph Neural Networks for Equivariant Processing of Neural Networks (Miltiadis (Miltos) Kofinas) Graph Neural Networks on heterophilous graphs: performance analysis and new architectures (Andrea Cavallo) Multi-label Node Classification On Graph-Structured Data (Tianqi Zhao) Hodge-aware learning on simplicial complexes (Maosheng Yang) Recursive Algorithmic Reasoning (Dulhan Jayalath and Jonas Jürß)
15:00 - 15:30	Talk: Elvin Isufi	Graph Neural Networks over Random Graphs
15:30 - 16:00	Talk: Andrew Foster	Graph representation learning identifies repositionable drug candidates for HIV-1
16:00 - 17:00	Drinks
17:00 - 18:00	Keynote LoG	Kyle Cranmer