Since the early 2000s, we have been recognized for research accomplishments
in data analytics for structured data. Of particular importance is the
following work.
We proposed the celebrated first algorithm called AGM (a-priori-based graph mining), which is the one that opened a new door to the fertile field of graph mining.
Akihiro Inokuchi, Takashi Washio, Hiroshi Motoda, "An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data," PKDD 2000: 13-23.
We first proposed the notion of graph kernels, which enables us to computed a kernel function for structured data.
Hisashi Kashima, Koji Tsuda, Akihiro Inokuchi, "Marginalized Kernels Between Labeled Graphs," ICML 2003: 321-328
In addition to these, we have been actively studied data analytics for
structured data, with particular emphasis on the application to the real
world. Examples include:
Online anomaly detection from time-evolving graphs (Ide and Kashima, KDD 2004)
Induced ordered tree mining in tree-structured databases (Hido and Kawano, ICDM 2005)
Supervised link prediction for network data (Kashima and Abe, ICDM 2006).
Japanese page includes more detailed information.