SPS DSI (DEGAS) Webinar: 14 December 2022, presented by Dr. Haggai Maron

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News and Resources for Members of the IEEE Signal Processing Society

SPS DSI (DEGAS) Webinar: 14 December 2022, presented by Dr. Haggai Maron

Upcoming IEEE ASI Webinar
IEEE Autonomous Systems Initiative Webinar

Title: Subgraph-Based Networks for Expressive, Efficient, and Domain-Independent Graph Learning
Date: 14 December 2022
Time: 3:00 PM (Paris time) | (Local time zone)
Duration: Approximately 1 hour
Speaker: Dr. Haggai Maron

Register for the Webinar

The DEGAS Webinar Series is an event initiated by the Data Science Initiative (DSI) of the IEEE Signal Processing (SP) Society. The goal is to provide the SP community with updates and advances in learning and inference on graphs. Signal processing and machine learning often deal with data living in regular domains such as space and time. This webinar series will cover the extension of these methods to network data, including topics such as graph filtering, graph sampling, spectral analysis of network data, graph topology identification, geometric deep learning, and so on. Applications can for instance be found in image processing, social networks, epidemics, wireless communications, brain science, recommender systems, and sensor networks. These bi-weekly webinars will be hosted on Zoom, with recordings made available in the IEEE Signal Processing Society’s  YouTube channel following the live events. Further details about live and streaming access will follow.


While message-passing neural networks (MPNNs) are the most popular architectures for graph learning, their expressive power is inherently limited. In order to gain increased expressive power while retaining efficiency, several recent works apply MPNNs to subgraphs of the original graph. As a starting point, this talk will introduce the Equivariant Subgraph Aggregation Networks (ESAN) architecture, which is a representative framework for this class of methods. In ESAN, each graph is represented as a set of subgraphs, selected according to a predefined policy. The sets of subgraphs are then processed using an equivariant architecture designed specifically for this purpose. I will then present a recent follow-up work that revisits the symmetry group suggested in ESAN and suggests that a more precise choice can be made if we restrict our attention to a specific popular family of subgraph selection policies. We will see that using this observation, one can make a direct connection between subgraph GNNs and Invariant Graph Networks (IGNs), thus providing new insights into subgraph GNNs’ expressive power and design space. This talk is based on our ICLR and NeurIPS 2022 papers (spotlight and oral presentations accordingly).

Speaker Biography:

Haggai Maron

Haggai Maron received the Ph.D. degree in 2019 from the Weizmann Institute of Science under the supervision of Prof. Yaron Lipman.
Currently, he is a Senior Research Scientist at NVIDIA Research and a member of NVIDIA's TLV lab. His main field of interest is machine learning in structured domains. In particular, he works on applying deep learning to sets, graphs, point clouds, and surfaces, usually by leveraging their symmetry structure.
Dr. Haggai will be joining the Faculty of Electrical and Computer Engineering at the Technion as an Assistant Professor in 2023.


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