Special Database Seminar

Wednesday, July 19, 2017 - 1:00pm to 2:00pm


7501 Gates Hillman Centers


MICHALIS VAZIRGIANNIS, Professor http://www.lix.polytechnique.fr/~mvazirg/

Graph kernels have emerged as a powerful tool for graph comparison. Most existing graph kernels focus on local properties of graphs and ignore global structure. In this effort, we compare graphs based on their global properties as these are captured by the eigenvectors of their adjacency matrices. We present two algorithms for both labeled and unlabeled graph comparison. These algorithms represent each graph as a set of vectors corresponding to the embeddings of its vertices. The similarity between two graphs is then determined using the Earth Mover's Distance metric. These similarities do not yield a positive semidefinite matrix. To address for this, we employ an algorithm for SVM classification using indefinite kernels. We also present a graph kernel based on the Pyramid Match kernel that finds an approximate correspondence between the sets of vectors of the two graphs. We further improve the proposed kernel using the Weisfeiler-Lehman framework. We evaluate the proposed methods on several benchmark datasets for graph classification and compare their performance to state-of-the-art graph kernels. In most cases, the proposed algorithms outperform the competing methods, while their time complexity remains very attractive.—Dr. Michalis Vazirgiannis is a Professor at LIX, Ecole Polytechnique in France. He holds a degree in Physics and a PhD in Informatics from Athens University(Greece) and a Master degree in AI from HerioWatt Univ Edinburgh. He has conducted research in GMD-IPSI, Max Planck MPI (Germany), in INRIA/FUTURS (Paris). He has been a teaching in AUEB (Greece), Ecole Polytechnique, Telecom-Paristech, ENS (France), Tsinghua (China) and in Deusto University (Spain).His current research interests are on machine learning and combinatorial methods for Graph analysis (including community detection, graph clustering and embeddings, influence maximization), Text mining including Graph of Words, word embeddings with applications to web advertising and marketing, event detection and summarization. Also distributed machine learning algorithms, distributed dimensionality reduction, distributed resource management. He has active cooperation with industrial partners in the area of data analytics and machine learning for large scale data repositories in different application domains such as Web advertising and recommendations, social networks, medical data, aircraft logs and insurance data. He has supervised fourteen completed PhD theses – several of them supported by industrial funding including Google, Airbus, BNP etc.On the publication frontier, he has contributed chapters in books and encyclopedias, published three books and more than a hundred forty papers in international refereed journals and conferences. He has organized conferences in the area of Data Mining and Machine Learning (such as ECML/PKDD) while he participates in the senior PC of AI and ML conferences – such as AAAI and IJCAI. He is also co-author of three patents. He has received the ERCIM and the Marie Curie EU fellowships and lead a DIGITEO chair grant. Since 2015 M. Vazirgiannis leads the AXA Data Science chair.Faculty Host: Christos Faloutsos

For More Information, Contact:


Special Seminar