About this Research Topic
Over several decades graphs have been used to formulate numerous problems in computer science, operations research, communication networks, transportation, and many other domains. Their widespread application is attributed to their flexible, simple, and yet powerful modeling capabilities. In recent years, there is growing interest in the area of graph data mining that deals with processing, analysis, and extraction of meaningful information from large volumes of real-world graph data. The growing interest in graph data mining is not only owing to the availability of large volumes of graph data but also due to its wide applicability. Such applications include web analytics, bioinformatics, chemoinformatics, social network analysis, traffic analysis, and program flow analysis, to name a few.
One of the most challenging graph mining problems that have attracted considerable research attention is the problem of frequent sub-graph mining that aims to discover subgraph patterns or network motifs that frequently recur in a graph dataset. This is important for identifying interpretable structural properties of complex networks such as protein interaction networks, molecular networks in chemical compounds, social media networks, etc. Few other interesting graph mining problems are querying of subgraphs or supergraphs, community detection in social networks, discovering influential nodes in a graph, mining traffic congestion behavior, finding optimal facility locations, mining nearest neighbors and recommending routes for navigation, and so forth.
The goal of this research topic is to foster research on theory, systems, and applications of graph data mining and management. In this context, we invite high-quality research contributions that focus on either of the following topics of interest but are not limited to:
• Frequent graph patterns
• Rare graph patterns
• Approximate graph patterns
• Graph patterns with constraints
• Graph clustering
• Graph classification
• Graph searching and indexing
• Graph pattern summarization
• Graph compression
• Uncertain graph patterns
• Dynamic graphs
• Spatio-temporal graphs
• Streaming graph models
• Hypergraphs and higher dimensional graphs
• Geometric graphs
• Anomaly detection in graph data
• Applications of graph mining in web analytics, communication networks, bioinformatics, chemoinformatics, social network analysis, event stream analysis, cyber-security, e-commerce analytics, transactional data analysis, and all other areas of interest.
Keywords: graph mining, big data, graph patterns, Graph clustering, Graph classification, Graph compression, Geometric graphs, Dynamic graphs
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.