Scalable Querying and Mining of Graphs

许多科学努力正在产生可以建模为图表的数据：关于蛋白质相互作用的高通量生物实验，对化合物、社会网络、生态网络和食物网的高通量筛选，数据库模式和本体。挖掘和分析这些标注和概率图对于提高科学研究水平、对现有系统进行准确的建模和分析以及对新系统进行工程设计都是至关重要的。本研究项目的目标是集成数据库、生物信息学、机器学习和算法等领域的技术，开发一套可扩展的图形数据库查询和挖掘工具。正在开发新的算法，并正在检查这些算法的质量和在真实数据集上的运行时间。第一组算法解决了图形数据库中的子图和相似性查询。第二组考虑重要子图或主题的挖掘。一种新的重要性模型正在开发中，该模型将图形转换为原始成分的直方图，并检查转换后的域中基元的重要性。第三组算法的目标是在大型概率图中发现连接良好的簇。该项目通过将研究成果引入本科生和研究生课程，将研究和教育结合起来。基于开发的算法的强大的开源工具将为其他研究人员发布。这些将有助于研究日益普遍的大型网络的结构和组织。

A number of scientific endeavors are generating data that can be modeled as graphs: high-throughput biological experiments on protein interactions, high-throughput screening of chemical compounds, social networks, ecological networks and food webs, database schemas and ontologies. Mining and analysis of these annotated and probabilistic graphs is crucial for advancing the state of scientific research, accurate modeling and analysis of existing systems, and engineering of new systems. The goal of this research project is to develop a set of scalable querying and mining tools for graph databases by integrating techniques from the fields of databases, bioinformatics, machine learning, and algorithms. New algorithms are being developed, and these are being examined for their quality and running time on real datasets. The first set of algorithms addresses subgraph and similarity querying in graph databases. The second set considers the mining of significant subgraphs or motifs. A novel significance model which transforms graphs into histograms of primitive components and examines the significance of motifs in the transformed domain is being developed. The third set of algorithms targets the discovery of well-connected clusters in large probabilistic graphs. The project integrates research and education by introducing the results of the research into undergraduate and graduate courses. Robust open-source tools based on the developed algorithms will be released for other researchers. These will be helpful in the study of the structure and organization of large networks that are becoming increasingly common.