Data Mining in Bioinformatics Day 5 : Graph Mining February 25 to March 10 Bioinformatics Group MPIs Tübingen Data Mining in Bioinformatics Day 5 : Graph Mining Karsten Borgwardt February 25 to March 10 Bioinformatics Group MPIs Tübingen
The graph edit distance is an intuitive measure to quantify the dissimilarity of graphs, but its computation isNP-hard and challenging in practice. We introduce methods for answering nearest neighbor and range queries regarding this distance efficiently for large databases with up to millions of graph...
参考 ^Zügner D, Akbarnejad A, Günnemann S. Adversarial attacks on neural networks for graph data[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 2847-2856.https://arxiv.org/pdf/1805.07984 ^Zügner D, Günnemann S. Adversa...
Graph Neural Networks (GNNs) show strong expressive power on graph data mining, by aggregating information from neighbors and using the integrated representation in the downstream tasks. The same aggregation methods and parameters for each node in a graph are used to enable the GNNs to utilize the...
concepts and techniques for the discovery of patterns hidden in large data sets, focusing on issues relating to their feasibility, usefulness, effectiveness, and scalability. However, since the publication of the first edition, great progress has been made in the development of new data mining ...
Recent years have witnessed a surge of interest in learning representations of graph-structured data, with applications from social networks to drug discovery. However, graph neural networks, the machine learning models for handling graph-structured data, face significant challenges when running on conven...
His research interests include machine learning systems, data mining, and information retrieval. Liang Wang received both the B.S. and M.S. degrees from Anhui University in 1997 and 2000, respectively, and the Ph.D. degree from the Institute of Automation, Chinese Academy of Sciences (CASIA)...
In recent years, we have witnessed the proliferation of knowledge graphs (KG) in various domains, aiming to support applications like question answering, r
In Proc. 2nd International Conference on Knowledge Discovery and Data Mining, KDD’96, 226–231 (AAAI, 1996); https://www.aaai.org/Papers/KDD/1996/KDD96-037.pdf. DeZoort, G. et al. Charged particle tracking via edge-classifying interaction networks. Comput. Softw. Big Sci. 5, 26 (...
GraphAr (short for "Graph Archive") is a project that aims to make it easier for diverse applications and systems (in-memory and out-of-core storages, databases, graph computing systems, and interactive graph query frameworks) to build and access graph data conveniently and efficiently. ...