２０１９年５月１０日（月）１６：００ ～ １７：００

九州工業大学　飯塚キャンパス　
総合研究棟　大学院セミナー室　２Ｆ（Ｎ２１４）

A wide variety of machine learning techniques have been developed and used for a lot of applications. Traditionally data are vectors, meaning that each instance is a vector of features, by which the entire data set is a matrix. A typical example is a user-item matrix in e-commerce, where each instance is a user or item, for which features are purchase records. A recent, more emerging data type is a graph, particularly that with unique nodes, resulting in an adjacency matrix showing the similarity between nodes (instances). Examples are social networks, web links and biological networks, especially gene networks, in which edges represent biological relationships between nodes (genes), such as gene regulation, protein-protein interactions, and so on. In this talk, I will explain the key idea of machine learning　for graphs (MLG) and also describe major problem settings and efficient algorithms for the problems of MLG.