2018/8/1 01:59

要旨

Speaker: Keyulu Xu (Massachusetts Institute of Technology)

Title: Representation Learning on Graphs with Jumping Knowledge Networks

Abstract: Recent deep learning approaches for representation learning on graphs follow a neighborhood aggregation procedure. We analyze some important properties of these models, and propose a strategy to overcome those. In particular, the range of “neighboring” nodes that a node’s representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture — jumping knowledge (JK) networks — that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models’ performance.

詳細情報

日時 2018/08/07(火) 15:00 - 16:00
URL https://c5dc59ed978213830355fc8978.doorkeeper.jp/events/78278

場所

〒103-0027 東京都中央区日本橋1-4-1 日本橋一丁目三井ビルディング 15階