Dataset For Survey On Applications Of Graph Analysis

This archive contains data collected and processed while conducting our survey on graph analysis applications. We provide data from three stages of our process to facilitate validation and reproduction of our results. All results are provided in TSV format (Tab-Separated Values). The completed survey will be submitted for publication in the near future. The file "1-query-results.tsv" contains the top 100 search results for 8 queries performed using Google Scholar. For each result we include the query used, the title of the result, and a link to the publication. The file "2-filtered-relevant-results.tsv" contains the subset of search results we found to be relevant, i.e., the work describes an application of graph analysis. The file "3-characterization-of-selected-results.tsv" contains a selection of 60 applications (articles) we reviewed and characterized based on our taxonomy for graph analysis applications. Included are the domain of each application and a set of characteristics. For each characteristic, a value of 1 represents that we consider the characteristic to be present in the surveyed application. Legend for characteristics: Graph features:<br> - Directed<br> - Weighted: vertex (V), edge (E), edge weights removed after filtering low weight edges from input (*).<br> - Heterogeneous: vertex (V), edge (E).<br> - Properties: vertex (V), edge (E).<br> - Temporal<br> Analysis methods:<br> - Neighborhood Statistics: clustering coefficient (C), degree distribution (D), other (*).<br> - Paths &amp; Traversals: shortest path (S), average path length (A), maximum path length (M), traversal (T), other (*).<br> - Connectivity: weakly connected components (W), strongly connected components (S).<br> - Centrality &amp; Ranking: betweenness centrality (B), closeness centrality (C), degree centrality (D), PageRank (P), other (*).<br> - Clustering: rule-based (R), community-based (C).<br> - Subgraph Isomorphism &amp; Mining: pattern detection (P), subgraph isomorphism (S), frequent subgraph mining (F), discriminative subgraph mining (D).<br> - Graph Mutation: construction (C), reduction (R).<br>