Scalable Querying and Mining of Graphs

许多科学努力正在生成可以建模为图形的数据：蛋白质相互作用的高通量生物实验，化合物的高通量筛选，社会网络，生态网络和食物网，数据库模式和本体。挖掘和分析这些注释和概率图对于推进科学研究的状态，对现有系统的准确建模和分析以及新系统的工程至关重要。本研究项目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，为图形数据库开发一套可扩展的查询和挖掘工具。人们正在开发新的算法，并对这些算法的质量和在真实数据集上的运行时间进行了检验。第一组算法处理图数据库中的子图和相似度查询。第二组考虑重要子图或母题的挖掘。一种新的显著性模型将图形转换为原始成分的直方图，并在转换后的域内检测母题的显著性。第三组算法的目标是在大概率图中发现良好连接的聚类。该项目通过将研究成果引入本科和研究生课程，将研究与教育相结合。基于开发算法的健壮的开源工具将发布给其他研究人员。这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.