KDD 2020

DD 2020 will be held in San Diego, CA, USA from August 23 to 27, 2020. The Automatic Graph Representation Learning challenge (AutoGraph), the first ever AutoML challenge applied to Graph-structured data, is the AutoML track challenge in KDD Cup 2020 provided by 4Paradigm, ChaLearn, Stanford and Google. The challenge website could be found here: https://www.automl.ai/competitions/3

Machine learning on graph-structured data.

Graph-structured data have been ubiquitous in real-world, such as social networks, scholar networks, knowledge graph etc. Graph representation learning has been a very hot topic, and the goal is to learn low-dimensional representation of each node in the graph, which are used for downstream tasks, such as friend recommendation in a social network, or classifying academic papers into different subjects in a citation network. Traditionally, heuristics are exploited to extract features for each node from the graph, e.g., the degree statistics, or random walk based similarities. However, in recent years, sophisticated models such as graph neural networks (GNN) have been proposed for the graph representation learning tasks, which lead to the state-of-the-art results in many tasks, such as node classification, or link prediction.