Scalable Querying and Mining of Graphs

许多科学工作正在产生可以建模为图表的数据：蛋白质相互作用的高通量生物实验，化合物的高通量筛选，社交网络，生态网络和食物网，数据库模式和本体。 挖掘和分析这些带注释的概率图对于推进科学研究、现有系统的准确建模和分析以及新系统的工程设计至关重要。 本研究的目标是通过整合数据库、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数据库查询和挖掘工具。正在开发新的算法，并在真实的数据集上检查其质量和运行时间。第一组算法解决图数据库中的子图和相似性查询。第二组考虑挖掘重要的子图或图案。一种新的显着性模型，将图形转换成直方图的原始组件，并检查变换域中的图案的意义正在开发中。第三组算法的目标是在大概率图中发现良好连接的集群。该项目通过将研究成果引入本科和研究生课程，将研究和教育结合起来。基于开发的算法的强大开源工具将发布给其他研究人员。 这些将有助于研究越来越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.